Nicolas Jullien

Karine Roudaut

Can Open Source projects succeed when the producers are not users? Lessons from the data processing field

Keywords: Knowledge economics, sociology, open source, science, standardization. JEL: O31, O32 Introduction

Can open source projects succeed when the producers are not users? Lessons from the data processing field.

production.

FLOSS appears to be a specific organization of production [START_REF] O'mahony | Guarding the commons: how community managed software projects protect their work[END_REF][START_REF] O'mahony | The governance of open source initiatives: what does it mean to be community managed[END_REF]. It organizes the collaboration between actors of divergent interests [START_REF] O'mahony | Boundary organizations: Enabling collaboration among unexpected allies[END_REF], creating both "coat-tailing systems" to integrate heterogeneity in terms of contributions and goals [START_REF] Hemetsberger | Collective development in open-source communities: An activity theoretical perspective on successful online collaboration[END_REF], and a model for virtual teams close to Cohen et al.'s (1972) garbage can model [START_REF] Lacolley | La prise de décision dans les équipes de la communauté des logiciels libres: Faut-il mettre le bazar dans la poubelle?[END_REF][START_REF] Li | Decision making paths in self-organizing technology-mediated distributed teams[END_REF].

But this does not mean that producing FLOSS is costless, and the fact remains that producers must have incentives to participate. In classic knowledge or software production systems, there are either financial flows from the user to the producers, or collective, public support of the producers (as in open science) to do so2 . In this regard as well, FLOSS is innovative. While the motivations for participating vary3 , the core of the incentive framework is the "private collective" innovation model (von [START_REF] Hippel | Open source software and the "private-collective" innovation model: Issues for organization science[END_REF], or the "user-as-innovator principle" [START_REF] Lakhani | How open source software works: Free user to user assistance[END_REF][START_REF] Hippel | Open source software and the "private-collective" innovation model: Issues for organization science[END_REF]: as users directly benefit from the piece of innovation they produce, they have incentive to produce it, and as they can expect add-on, feedback or cumulative innovation on their own proposition, they have incentive to freely share it. Does that mean that the only industries where FLOSS-like organization may flourish are those where producers and users are the same? This is the hypothesis we explore in this paper. We discuss this point by studying domains where FLOSS organization should be, a priori, of maximum efficiency, because the production of knowledge is modular and incremental, but are instead systems where the users and the producers are disjointed. This requires us to first analyze the problems raised by this division, before identifying an industry where these problems may be overcome, for the conditions most favorable to finding successful projects. In this case, we are dealing with algorithm-based industries. The lack of research on the subject suggests a qualitative approach to develop initial, but detailed, exploratory results (Von [START_REF] Krogh | Community, joining, and specialization in open source software innovation: A case study[END_REF]. We develop this approach via interviews with researchers in those industries.

Our study question, to quote [START_REF] Yin | Case study research: design and methods[END_REF], will be to understand if software producers may find interest in participating in open source projects when they are not users, and why.

The article is organized as follows. In section 2 we review the literature to define the characteristics of a FLOSS organization and the consequences of a split of the user-producer actors on it. In section 3 we present our fields of study, "algorithm-based industries", and specifically open source projects in these industries, as we want to explore the reason producers have (or do not have) to participate in such projects. This discussion is followed by a description of our methodology. In section 4, we present our findings on the knowledge creation process, and on the use of FLOSS, before discussing them with regard to the incentives for proposing FLOSS solutions.

Research questions: incentive and investment for software producers in a FLOSS project.

FLOSS is a kind of knowledge good [START_REF] Foray | The Economics of Knowledge[END_REF], produced by a group of people making it freely available to users. [START_REF] Crowston | Success in free and open source software development: Theory and measures[END_REF], followed by [START_REF] Lee | Measuring open source software success[END_REF], looked at FLOSS organization as information system organization. They show that, as a software production organization, FLOSS can be studied using classic information system models [START_REF] Delone | Information systems success: The quest for the dependent variable[END_REF][START_REF] Clément-Fontaine | Les licences libres. analyse juridique[END_REF][START_REF] O'mahony | Guarding the commons: how community managed software projects protect their work[END_REF], stressing the importance of the production process to explain the quality of the production and thus of the success of the product. It is, more generally, an example of a "knowledge commons" (Hess and Ostrom, 2006a), i.e. an organization dedicated to the production of, to quote [START_REF] Lundvall | The learning economy[END_REF], new algorithms Figure 1: Institutional Analysis and Development framework (Hess and Ostrom, 2006a, p. 44) to meet "practical" needs that deal with "know-why" production, "scientific knowledge of the principles", and "laws of nature".

Considering Hess and Ostrom's (2006a) framework, shown in figure 1, splitting the user-as-innovator means to restudy the action arena, or how people interact to define and produce what is needed (what we call the "FLOSS factory"), and the cost-benefit for the providers to participate in this project, to go to the action arena.

The fact that some use what others produce is not uncommon in knowledge commons production. [START_REF] Crowston | The motivational arc of massive virtual collaboration[END_REF], quoting Mockus et al.'s (2000) study on the open source project Apache, and [START_REF] Zachte | Wikipedia statistics-tables-english[END_REF] work on Wikipedia, noted that the distribution of contributions is biased, with a few people doing most of the work and most doing little or nothing, as is the case in most volunteer organizations [START_REF] Marwell | The Critical Mass in Collective Action: A Micro-Social Theory[END_REF][START_REF] Crowston | Lessons from volunteering and free/libre open source software development for the future of work[END_REF]. What is specific here is those who do are not users, and thus may not perceive the platform of the FLOSS project as directly useful for them. And this "perceived usefulness" [START_REF] Hackman | The design of work teams[END_REF][START_REF] Seddon | A respecification and extension of the delone and mclean model of is success[END_REF] has proved to be an important factor in explaining participation [START_REF] Crowston | The motivational arc of massive virtual collaboration[END_REF]. We will thus study, for the producers, the interest of participation, looking at their perceived costs and benefits, considering the fact that we tried to focus on projects where these perceived costs should be minimal and these perceived benefits maximal. We do not mean only financial consequences, but more generally what the perceived involvement and interests are. But as explanatory variables, we will also have to look at users' adoption and feedback, and the projects' environments (who initiated the project, or the policy makers, institutional support, the level of openness, or the rules-in-use) as both matter for producers' valuations, as explained by [START_REF] Crowston | The motivational arc of massive virtual collaboration[END_REF], we will have to look at them also, as an explanatory background. The "biophysical characteristics"4 will be described in the methodology, in the presentation of the field we decided to look at and of the Floss factories existing in this field.

The producers.

Not being users, producers may not benefit from innovation, which is the core of von Hippel's classic FLOSS incentive regime, but not the only incentive.

Theoretical analyses of incentives, in software projects [START_REF] Foray | L'économie du logiciel libre: organisation coopérative et incitation à l'innovation[END_REF][START_REF] Lerner | Some simple economics of open source[END_REF] or in wikis (Forte and Bruckman (2005), using [START_REF] Latour | Laboratory Life: The Social Construction of Scientific Facts[END_REF] analysis of science "cycles of credit"), estimate that the other main vector for participation is the quest for reputation. Applied works on Wikipedia [START_REF] Nov | What motivates wikipedians?[END_REF][START_REF] Yang | Motivations of wikipedia content contributors[END_REF][START_REF] Zhang | Group size and incentives to contribute: A natural experiment at chinese wikipedia[END_REF], professional electronic networks [START_REF] Wasko | Why should i share? examining social capital and knowledge contribution in electronic networks of practice[END_REF][START_REF] Jullien | L'engagement dans des collectifs de production de connaissance en ligne[END_REF], and open source software [START_REF] Shah | Motivation, governance, and the viability of hybrid forms in open source software development[END_REF][START_REF] Scacchi | Free/open source software development: recent research results and emerging opportunities[END_REF], confirm that peer recognition, whether it be professional or community recognition, is a main motive for participation, in addition to intrinsic factors (personal enjoyment and satisfaction from helping by sharing their knowledge). This argument that on-line volunteer participation can be explained by the same incentives found in science has also been used in reverse. [START_REF] Schweik | Free/Open-Source Software as a Framework for Establishing Commons in Science[END_REF] uses the same framework by Hess and Ostrom (2006a) to describe open source organization in order to extract its main mechanisms to construct an open science project.

Considering this, the chance to find a successful FLOSS production project where providers are not users should be enhanced if the producers belong to the scientific community, where reputation is one of the main rewards (Foray and Cassier, 2001b,a). This should be the case, even if the sociology of sciences [START_REF] Lamy | L'autonomie scientifique face à la mercantilisation. formes d'engagement entrepreneurial des chercheurs en france[END_REF] shows that the commitment to non-academic projects (in that case entrepreneurship) may be weighted by potential concerns about the "scientific value" of such non-traditional academic production, the "attachment" to the cycles of academic valorization.

The first question to answer are thus the following: What is the interest for producers to publish open source material? Does this provides an improvement in their reputation? How do they consider this reputation in comparison to classic academic reward?

The investment to participate in a FLOSS project may seem obvious, as it deals with the production of a piece of software. Once this software is produced, making it open source is just a matter of license and of uploading the product on a platform. Looking at case studies where producers have to produce software anyway may thus lead to a close-to-zero extra investment for FLOSS. However, there are specific costs attached to the participation in a FLOSS community (Von Krogh et al., 2003) [START_REF] Kogut | Open source software development and distributed innovation[END_REF], but it does not make them disappear.

Thus our second major question is what specific investments are involved in contributing to an opensource project, and how they are evaluated by the producers themselves. Actually, these investments may vary as the producers are not the users, because they have to understand what the users need, and may have to explain to these users how their production meets these needs. And the benefits may also vary according to the incentives the project initiators have settled on. This leads to two preliminary questions we will discuss in the following two sub-sections.

Factors influencing participation.

The construction of the absorptive capacities.

As explained by [START_REF] Oecd | The knowledge-based Economy[END_REF], the way and conditions in which an algorithm can be used are complex and require specific skills ("know-how"). In other words, even if the conditions of use of the algorithm are codified (described) and a program exists for making it run on a computer, this is not always enough for the end user to be able to use it correctly. On the other hand, users express needs tha providers have to understand, and translate into software specifications. This is what [START_REF] Zimmermann | Le concept de grappes technologiques. Un cadre formel[END_REF] called the "technologies of use" (our translation from French). In other words, both knowledge users and providers have to develop their "absorptive capacities", or their capacity for understanding what others produce, in order to decrease the "tacitness" of the knowledge and to pass it across borders. In traditional "user-asinnovator" FLOSS organization, and, as already mentioned, these absorptive capacities are less an issue because, per definition, the users directly translate and produce, even if, as [START_REF] Jullien | Peut-on envisager une écologie du logiciel libre favorable aux nuls? Terminal[END_REF] have shown, this is not completely solved for those users who are not providers. Social psychology and computer use studies have brought some answers on how users and producers construct the absorptive capacities5 : bridges have to be established by the design of specific "boundary objects" [START_REF] Star | Institutional ecology, 'translations' and boundary objects: Amateurs and professionals in Berkeley's museum of vertebrate zoology, 1907-39[END_REF]. The goal is to implement the knowledge in an "appropriable" way for the user, or/and by specific "frontier persons" coming from one side of the boundary but having developed competencies on the other side. These boundary spanners or "intermediaries" have been described by Actor Network Theory (ANT), or the Sociology of Translation [START_REF] Callon | Mapping the Dynamics of Science and Technology[END_REF][START_REF] Akrich | Sociologie de la traduction : textes fondateurs[END_REF], as "mediators", a role which involves a "translation", here between two distinct "communities" of research, involved in a collective action. From the organizational point of view, because of his position, this actor is close to figure of the "marginal sécant" (influential outsider), as described by [START_REF] Crozier | L'acteur et le système[END_REF]. [START_REF] Barcellini | User and developer mediation in an open source software community: Boundary spanning through cross participation in online discussions[END_REF], for the open source project Python, and [START_REF] Laniado | Co-authorship 2.0: patterns of collaboration in wikipedia[END_REF], for Wikipedia, shown the importance of these actors for the functioning of open knowledge communities.

In addition, an algorithm (as defining and defined as a "piece of knowledge") does not work alone, but has to interact with others. This is the "modularity" issue, i.e. "how parts are grouped together and about how groups of parts interact and communicate with one another" (Koppl and Langlois, 2001, p. 18). This articulation of basic pieces of knowledge to create more complex knowledge exists in software [START_REF] Langlois | Networks and innovation in a modular system: Lessons from the microcomputer and stereo component industries[END_REF], molecules [START_REF] Bureth | Brevet, innovation modulaire et collaboration[END_REF] and chains of treatment (coding-decoding of a voice signal for telecommunications, gene sequencing and analysis in biology, medical image processing, etc.) One has to make sure there is no incompatibility between the different elements of the chain, that what the "out" data produces is what is expected as "in" by the following element in the process.

So to define the investment producers have to make, we will have to understand how the interactions are constructed. This will be our first preliminary study question.

We formulate the hypothesis that a FLOSS factory may help for the construction of such interactions because, as [START_REF] Bessen | Open Source Software: Free Provision of Complex Public Goods[END_REF] explained, it clarifies the interfaces between the different components (thus decreasing the part of tacit knowledge), facilitating producers' work, as they then only have to focus on the production of a single piece of knowledge) as well as that of intermediaries (i.e., the creation of chains of treatment is facilitated).

The projects' environment.

Finally, looking at FLOSS producers participation to projects may disguise the importance of the institutional approach in these projects. Producing open source algorithms may be, for the sponsors of the platform (institutions of services within an institution dedicated to computer support for researchers), a way to facilitate the coordination and the construction of standards [START_REF] Simcoe | Open Standards and Intellectual Property Rights[END_REF], because they depend on these standards. They use them for their own production, either as user or as producer of technology using software bundled with technological outputs (such as data acquisition and processing offers like remote sensing tools). [START_REF] Olson | The logic of Collective Action[END_REF] argued that there may be transfers to ensure participation in collective good, and that institutions are needed to organize these transfers. But just because there is interest in FLOSS projects at the institutional level does not mean that, at the individual level, the transfers or institutional support are important enough to support producers' participation. So, in our case, the structure supporting the platform project (service, contributors' reward) may appear as a factor explaining (non-)participation, as already noted when we referred to [START_REF] Lamy | L'autonomie scientifique face à la mercantilisation. formes d'engagement entrepreneurial des chercheurs en france[END_REF], but it can not be our point of entry in this study.

Methodology.

Our case study is based on three scientific fields : bio-informatics, remote sensing and digital communication. In this section, after presenting a short definition of these scientific fields, we explain why they offer fertile grounds for our discussion. We explain the qualitative research method (Miles and Huber-man, 1994), based on an interpretative approach. The empirical data produced by this method are the foundation of our argument.

Field of investigation: data processing.

Information and communication technologies have changed the way some knowledge-based sciences work: "ICT allow the exploration and analysis of the contents of gigantic databases" (Foray, 2004, p. 29). Genetics, but also earth observation, or ICT industries like digital communication, have seen major breakthroughs in the past decade. All these disciplines take a huge amount of data, provided by physical "pipes" (sensors, genes, antennas and coders), apply algorithms6 to process the data and provide a result (a piece of information), and then compare this result with reality (e.g. original signal, comparable genes, in-the-field measures). This processing activity involves, to a large extent, filtering and cleaning the data of errors of measure and comparing various sources of data, usually with statistical methods.

One consequence of the appeal of statistical skills for the traditional sciences is the emergence of new scientific fields for these specific classical domains: biocomputing or bioinformatics to support the work of the biologist, remote sensing to help with earth observation, and digital transmission for communications.

"Bioinformatics derives knowledge from computer analysis of biological data. These can consist of the information stored in the genetic code, but also experimental results from various sources, patient statistics, and scientific literature. Research in bioinformatics includes method development for storage, retrieval, and analysis of the data. Bioinformatics is a rapidly developing branch of biology and is highly interdisciplinary, using techniques and concepts from informatics, statistics, mathematics, chemistry, biochemistry, physics, and linguistics. It has many practical applications in different areas of biology and medicine." [START_REF] Nilges | A definition of bioinformatics[END_REF] Remote Sensing "in the most generally accepted meaning refers to instrument-based techniques employed in the acquisition and measurement of spatially organized (most commonly, geographically distributed) data/information on some property(ies) (spectral; spatial; physical) of an array of target points (pixels) within the sensed scene that correspond to features, objects, and materials" [START_REF] Short | Remote sensing tutorial[END_REF]. Once the data have been collected, they may be used to recognize patterns. This is done by pattern recognition, i.e. "techniques for classifying a set of objects into a number of distinct classes by considering similarities of objects belonging to the same class and the dissimilarities of objects belonging to different classes [....]" (Short, 2009, part 2.6 ).

Digital transmission or data communication is "the electronic transmission of information that has been encoded digitally (as for storage and processing by computers)"7 . The problem of digital transmission is that errors may occur during the transmission and thus information may be lost.

To cope with this problem, some techniques add extra information (error-correcting codes) which, once decoded helps to correct the errors (see [START_REF] Anderson | Digital Transmission Engineering[END_REF], for a complete presentation). Of course, the aim of the algorithms is to correct as many errors as possible with a minimum of extra information added and/or in a minimum of data processing time. To do so, statistical techniques for digital data have had to be developed (referred to as digital communication theory, covered by a dedicated IEEE group8 ).

What is common to these fields is that designing a "better" algorithm requires competencies in statistics and classification, a form of applied mathematics. These are not integrated into biology, earth observa-tion or communication, but rather are autonomous disciplines referenced by IEEE. This separation was the insight which made us think about a distinction between algorithm producers and users. And this distinction has been confirmed by the people we interviewed, as the interaction between them (discussed further in Section ).

These definitions highlight at least three successive steps to providing a statistical solution to a problem, leading to the production of software:

first, the "translation" of a practical problem into "data", which means the identification of the physical patterns which must be captured, and their definition as digital data which can be computerized, second, the "expression of the algorithms", i.e. the different mathematical steps in the treatment of the data, and the results expected from the calculation, third, the "implementation" of the algorithms, or the production of compatible formats understandable by the chip or computer which will do the job9 . This is the classic process of production of an application in software programming: a functional description of a problem, software development to implement it and the integration of this development into working processes and products. The result of such a process in the fields we study is a piece of software, using more or less dedicated "languages", such as VHDL for chip design. In addition to the production of software, these three domains contain FLOSS factories, as presented in the next subsection.

FLOSS factory in data processing.

The second reason for studying data processing for knowledge-based sciences is that there are FLOSS initiatives, more or less visible at an institutional level. An exhaustive presentation of all of them is outside the scope of this paper, but some are quite visible on the Web, at algorithm level and at platform or chain-of-treatment level, and others are more local, as Table 2 shows. Here we selected, as our "unit of analysis" (Miles and Huberman (1994, p. 25), [START_REF] Yin | Case study research: design and methods[END_REF] third component of research design), platform projects aiming to aggregate knowledge based on the accessibility of the participants; we wanted to have access to the initiator(s) of the project and we wanted them to try to be open to users and to contributions, as our goal was to study the participation of people to these projects. According to Yin (2003) a case study approach could be considered here, because the focus of the study is first to answer to the analysis of the process, and second to understand how and why the participants in these platforms have been involved in these processes of knowledge production and feed these tools. And third, we want to cover contextual conditions, because they are relevant to the phenomenon under study. These platform projects offer us the opportunity to explore this phenomenon in context, using a variety of data, both an individual level, organizational, community, or research programs supported.

In remote sensing, we chose a library of open source programs, "Orfeo tool box", developed by the French space agency (CNES10 ). The aim of this library is to provide basic tools and algorithms to program the chains of treatment needed to use satellite data provided by the Pleiade satellite program11 .

In bio-computing, we identified two platform initiatives: the "Biogenouest" platform (the life science core facility network in western France) called "Bioside" (developed by Télécom Bretagne and the As far as digital communication is concerned, we were not able to identify any initiatives other than Open core 14 .

Data collection and analysis.

Data collection was performed between the end of 2008 and the end of 2009 and consisted of interviews (21 semi-guided interviews of more than 90 minutes each). All the interviewees were scientific professionals (researchers and research engineers), 18 belonging to public institutions (institutes of technology, CNRS, Institut Pasteur, CNES) and three to firms or private institutions. As the FLOSS movement has its roots "in the university and research environment" (Bonaccorsi and Rossi Lamastra, 2002, p. 6), one can argue that these producers should be more open to such arrangements15 . We had to collect two types of points of view regarding the production of algorithms and their diffusion in the chosen disciplines: the algorithm producers and the producers of the platforms.

Regarding the platform side, we interviewed the initiators of the projects, at CNES (Orfeo Toolbox), at Télécom Bretagne and CNRS (Bioside) and at Institut Pasteur (Mobyle). Regarding the algorithm producers' side, we interviewed researchers from a CNRS laboratory called LabSTICC (Information and Communication Science and Technology Laboratory), which is run by Télécom Bretagne, a French institute of technology, in partnership with two universities in Brittany, Université de Bretagne Occidentale and Université de Bretagne Sud. It brings together three poles working within one central theme: "From sensors to knowledge". The three poles are the following: MOM (microwaves and materials); CAC (communications, architecture and circuits); and CID (knowledge, information, decision) 16 . The fields covered by this laboratory include digital communication, remote sensing and tools for biologists. These initial points of view led us to interview other actors in the process of production-diffusion-use of an algorithm, in firms (chip design and remote sensing) or in public institutions (bio-computing, users of the platforms), because they were cited in the interviews and thus could provide us with a more global vision of the field than the people interviewed for a specific project, who were more project-centered, but also could confirm and specify how joint work is organized.

What we want to empirically appreciate in this paper is the participation of the researchers in FLOSS production and how this production is organized in a FLOSS factory. For this purpose, we collected information on the activities and production of the researchers and research engineers in the selected disciplines (bio-computing, remote sensing and digital transmission). We interviewed people about their representations of their production in their scientific environment and about the definition of what a "good algorithm" is. We also interviewed them on the existence of joint work, namely collaboration practices between the different actors contributing to and using the chains of treatment. Amongst the interviewees, some participate in the cooperative development of platforms of knowledge production (some open source, others not), while others do not. This allowed us to identify why they do or do not participate. We stopped the collection of new interviews when the exploration of the content of each new interview did not bring additional significant meaning (a summary of the methodology is available in the Appendix, table 1).

While the total number of interviews may appear low, their length allowed us to collect a fair amount of rich material (more detail and variance), and to identify some "coherence in attitude and social behavior" embedded in "a historical path, both personal and collective", to quote and translate [START_REF] Beaud | L'usage de l'entretien en sciences sociales. plaidoyer pour l'"entretien ethnographique[END_REF], the result expected from this kind of qualitative analysis. "This path is personal, as each interview describes the trajectory of a scientific actor, but also collective because it describes the specific scientific field it is embedded in" (ibid). According to Flyvbjerg (2011, pp. 301-316), referring to the definition of Merriam-Webster's Online Dictionary [START_REF] Merriam-Webster | Online Dictionary[END_REF], "case study focus on an "individual unit", what Stake (2008) calls a "functioning specific" or "bounded system".[...] Finally, case studies focus on "relation to environment"[...]". In our case,the emphasis is on analyzing the actors' relationships with their scientific knowledge production, and their environment.

After a brief introduction of the goal of the interview, the guide looked at the following dimensions: the career, the origin of the participation in the project, their definition of their contribution to the project is (based on interviees' own activity in the project), the significance of the project for the person's career, and the definition they give to their work17 .

Results.

All the actors we interviewed agreed on the aim of the collaboration: proposing the best methods to extract "pertinent" information from physical data, using algorithms. However, this "best" does not mean the same thing to everyone: the algorithm producer would look at mathematics lock-ins to be solved, while the user would look at the quickest way to solve a problem (not always the most efficient), either looking at an already implemented algorithm to do the job or, when this is not possible, taking it to an algorithm producer who can understand them. This means that they have to invest time and money to understand each other, to develop "patterns of interaction". Actually this investment seems to be made more by specific actors (the "boundary spanners") than by the use of tools such as the open source platforms developed, in the project we studied. Non-use is not due to an a priori discrimination against FLOSS, but rather because these projects seem to provide less help to boundary spanners and few incentives to algorithm producers (in terms of reputation or of institutional incentive), whereas they have important extra investments to make the programs they developed open source.

The contextual factors: absorptive capacity and environment.

A costly construction of the absorptive capacity and of the research agenda.

On the application or process side, the algorithm is not integrated into the final product the same way.

-In biotechnologies the chain of algorithms (bio-computing) is only a tool, and the user (biologist) has little interest in it and does not look at it. Statistics are only here to "clean the data" (expression we heard) 18 , to extract pertinent information from the data, to be able to perform biological tests on a small number of items (e.g. to select a small sample of genes able to produce a specific protein).

Data analysis is part of the biological research process and of the article published, but is not really the core of the production.

-In signal processing there is a strong link between algorithms, and chips, and the users of the algorithms are chip designers and chip integrators (mobile phone manufacturers, for instance), so they have good knowledge of algorithms, and ask for algorithms that are not too complex to be implemented on chips. Consequently, there are two criteria for evaluation, namely the efficiency of the algorithm and its practical usability, that is to say its practical implementation in a circuit.

-Remote sensing falls in between: if the design of the algorithm is driven by the application, by the problem the users (called "thematicians") want to solve, then the algorithm producers are more involved in the definition of the knowledge which fits with the needs. In publication, there is an algorithm and description of the way it has been used. The difference between remote sensing and biology is that, in the latter, data processing is part of the final result, and not a pre-requisite before other biological tests. "We take others' data, we aggregate them and analyse them with statistical and bio-computing methods and we extract value added in terms of knowledge" (Mathieu, 30, engineer in Biocomputing).

While perception varies from one domain to another, all the interviews shed light on three common key structuring notions: those of efficiency, productivity and usability. The algorithm is developed with the scientific goal of making real data processing more efficient, i.e. to accelerate the work of the users, to clear their way, extracting pertinent information, allowing a decrease in the error rate, faster. In other words, it is taken as an external input by the users to make it possible to push back the boundaries.

Because of this search for efficiency, algorithms are very specialized and are, as we expected, a scientific specialization in themselves, far from the specialization of the disciplines of application (biology, geog-raphy, etc.). The consequence is a distinction between algorithm producers (called algorithmists in the rest of the article) and knowledge-based scientists, such as biologists or geographers (called end-users in the rest of the article). At the same time, the interaction described here -whether structured around an infrastructure such as a platform or centered on individuals -stresses the importance, and the personal "costs" for the researcher, of building a collective at work. One respondent used the eloquent expression of "bundle of skills" ("faisceau de compétences", Cyril -Ph. D. (Biology), Computer Service), when he described what a bioinformatics platform is. The collective building dimension, more than the individual, institutional or organizational ones, seems to be the key for producing results.

All the actors we interviewed agreed on the aim of the collaboration: proposing the best methods to extract "pertinent" information from physical data, using algorithms. This means, from the algorithmist's side, identifying applied mathematics lock-ins and solving them, and from the end-user's side, identifying either an algorithmist able to understand their problem and solve it, or some available algorithm to do the job that is already implemented. Otherwise, there is a human go-between, with knowledge from both worlds:

"I am at the interface" [...] "A kind of interpreter role between the two communities, trying to reformulate the programmer's question to the biologist and then, to reformulate the biologist's answer to the programmer." (Cyril,40,Ph. D. (Biology), Computer service) Typically, although not always, these frontier actors, or "marginal men"19 have a PhD in the algorithmic part but work in end-user labs, as research engineers, or graduated in biology but have turn to computer (because of the job market) and work for biologists in a computer service. For instance, in the field of biology, these new actors are named bio-statisticians, or bio-informaticians. They are able to choose amongst the great deal of knowledge proposed (being algorithms or applied maths researchers to work with), and to construct of the chains of production (aggregation of the pieces), that end-users use.

"There is monitoring work to do, [which is] very important, and [which we] do not always do very well, there are lots of novelties, it is a fast-evolving domain, there are lots of, most of the time, we use programs which, eventually, are obsolete, there are others [that are] really better to do the same thing and we do not know them" [...] (Mathieu, 30, Computer engineer in biocomputing)

In any case, constructing these interactions means investing. Either people develop the standard pieces of software, institutions hire dedicated people as bridges, or researchers have to cross the border themselves.

And it seems that the more specific the problem raised, the closer the interaction between algorithmists and end-users must be. This has consequences for the way researchers on the algorithm side choose the scientific question they want to tackle. We found three strategies, based on three research agendas, leading to three different conceptions of what an algorithm is.

The first strategy we identified is driven by the research agenda in mathematics, for which the topics of investigation are more theoretical. The aim is to produce the "best algorithm possible" in terms of mathematical performance. This means "working alone, without taking the implementation into account" (Anne, 30, associate professor in applied mathematics for Digital Communications). Regarding usability, it is quite like working on "a huge labyrinthine system" which "will never be implemented" ("une usine à gaz, qui ne verra jamais le jour", Anne). However, it is useful as a "benchmark, a theoretical frontier in terms of performance" for the second and third strategies. The second strategy is more driven by practical implementation and involves looking for "a less efficient algorithm, but an implemented one" (ibid). In the third strategy, the algorithm is developed with the scientific goal of making real data processing more efficient, i.e. to accelerate and facilitate the job of end-users: to clear the way, extracting relevant information that allows a decrease in the error rate, and makes it faster. In other words, it makes it possible to push back the boundaries. This third strategy is driven by practical implementation and requires a "dialogue" (Gurvan, 50, professor in remote sensing) between the designer of the algorithm and the person or people who implement(s)/use(s) it. This dialogue may be directly between algorithmists and end-users to define the problem to be solved, in other words the information to be extracted from the physical data, or it might be between algorithmists and data providers, to understand what these data contain. According to our interviewees, the second or third strategies of research are not necessarily easier to solve than the first. But in that case, efficiency matters more than novelty or theoretical strength, relying on a formal mathematical demonstration, for instance. But for these two last strategies, the implementation, i.e. the software development of the algorithm matters, so they should be more willing to participate in FLOSS projects, whose goal is facilitating the transfer to users.

The environment.

This selection of programs and the verification that they work together appears costly, and in order to increase end-users' productivity, efficiency, and usability (terms present in interviewees' discourse), soft-ware may play this go-between role, when the process of treatment is "standard" and "common knowledge" amongst end-users. The actors have developed platforms with the goal of collecting this common knowledge to propose standard chains of treatment. In the case of remote sensing, for example, the Matlab platform (proprietary software), and its open source competitor, Scilab, were cited by two researchers we interviewed, and these standard developments are sold as components of the platform, or as extra libraries.

The reason for these dedicated platform development initiatives, is that their initiators bet they will help them to better serve these demands, because they have new material (for example CNES with the Pleiade satellite series) or recurrent demands for data analysis (as with the computer support for scientific analysis service at Pasteur), or because of the increasing coordination costs due to dealing with compatibility issues between modules whose source and behavior are closed (chips, see http://opencores.org/ opencores,mission).

The reasons for open-sourcing these platforms are linked to standards competition (as explained by [START_REF] Muselli | Les licences informatiques. un instrument stratégique des éditeurs de logiciels[END_REF]: free provision facilitates the evaluation of this experience-good20 and thus its adoption, but also the development of complementary technologies (the programs working on it). The reason for this is that it is a platform whose value lies as much in its facility of use as in the library of programs working on it, and it eventually acts as a tool to federate and coordinate the community.

For the time being, as far as bio-computing platforms are concerned (Mobyle and Bioside), the designers have implemented open-source programs in their platforms by themselves. In remote sensing (Orfeo), the institution (CNES) has paid researchers to design and format programs, according to our interviews, in order to increase the coordination and the reuse of algorithms, and to produce, or be able to use new algorithmic knowledge.

"What I put in this... this particular library, are things asked for by CNES and, actually, it is CNES which is encouraging the development of this particular library." (Maurice, 30, associate professor in Remote Sensing) Question: "And these programs, they were developed here, at Pasteur?" "Very few of them. I think twenty out of the 230, 10% are developed either by our division, or, sometimes, by researchers who have taken a keen interest in programming, and we have installed their programs because they work well." (Cédric, 50, computer engineer in a Biocomputing support service)

This could be explained by the novelty of the platforms and the fact they have not yet reached a critical mass allowing the automatic functioning of an increasing returns process21 , as in a classical network effect [START_REF] Katz | Network externalities, competition, and compatibility[END_REF]. In that respect, platform sponsors face the classic difficulties for initiating what would be a virtuous cycle for them; it ends up being a waiting-game from both users and producers:

first of all, if these platforms are open (and free) in access for users, they are not (yet?) for the developers, as traditional FLOSS projects are. The designers do not really believe outside people can collaborate on the development of the platform by itself, and there is neither injunction from the employer, nor strong support; Question: "And, you said it was not yet on the agenda to open... well, to have a collaboration... How would you imagine this organization?" "Well, this precisely, is difficult, because it depends on what we would like to get from it, because, either we kept on developing, but we would delegate some parts to people willing to involve themselves, or if we were not able to develop any more, we would not have the point of view on the project, people would take the lead [....] But, actually, I do not know exactly, it is just ... actually, it is the people who asked "do you want to do it?" (Nestor, 40, computer engineer in Biocomputing, PhD in Biology).

"The scientific direction is happy we do that, because they understand we are one of the departments open to the outside" (Cédric, 50, computer engineer in a Biocomputing support service) .

"In a way, the network [RENABI] aims to respond to the lack of will from the institutes and from the State to federate the bio-computing facilities. So all of the interested actors are networking and collaborating. But it is not really supported." (Mathieu,30, in addition to this, the algorithms did not mention injunction or specific reward from their employer to contribute to these platforms, a point we will come back to later, on the other hand, the interest in new algorithms is far from being obvious for end-users, as they do not understand them. But these users have to cope with the increasing amount of data and the competition between laboratories, making it necessary to develop these new algorithms, and make them faster / more efficient in terms of error rate.

"Everything is open, but it is not enough. Most of the time, the biologist uses the same things each time, he already knows, he comes back to the same web sites, etc. and... most of the time, he is... He doesn't know the best tools, the researcher in bio-computing, in his sub-field of competency, he brings a real added value." (Mathieu, 30, computer engineer in Biocomputing)

" [Programs] are free (open source), but one needs to install them in an appropriate environment, and thus, according to the type of data and to the constraints of the algorithm, regarding the memory, the speed of execution, or I do not know what, means that it is not obvious a biologist can use it. You need an expert both to install it and..." (Cédric,50,computer engineer in a Biocomputing support service) So the fact remains that end-users still need an expert, a frontier user, or institutional support to detect, finance and process these new algorithms. As far as institutional FLOSS platforms are concerned, frontier users say that the workflow may be useful for standard chains of production, but also that they may have already programmed these chains, and their program is as easy to use for them as the workflow. Thus, the added value of a FLOSS platform for the end-users of algorithms is weak.

"I start from a set of tools that I know [...] it is when a new issue arises that I will look elsewhere, another tool [eg a different algorithm] [...] There is no method that consensus from everyone." [...] I find information on new technologies and algorithmic tools in publications, the 'R' package provides development, and I am able to fend for myself to implement it." (Diane,43,research engineer, Given the background of these FLOSS platforms, it seems that there is a recognized need for such a tool, to improve producers and users' efficiency, providing standard chains of treatment. On the other hand, these projects came more from computer services than from a global demand from the whole community or as a strategic goal for institutions. This point leads to the question of why the producers have not invested more in the platform to diffuse their production.

FLOSS, a worthwhile investment for producers?

All the researchers we met are open-minded regarding FLOSS. They may use FLOSS-based computers (with GNU/Linux operating system, or/and Firefox browser) and some disciplinary FLOSS programs, which can "facilitate their work" because they do not have to "redevelop standard applications". Most of them (both algorithmists and end-users) think that, at a global level, having access to FLOSS programs implementing the algorithm would facilitate access to "knowledge" (of the existence of a new algorithm, of its performance, of how to use it, etc.) It is not just a question of publishing the program they have "cobbled together" for their own need, under a free, open source license. It is a question of making a "clean", "stable", "robust" program, with documentation, thus of doing extra work 22 . A part of the work is "do-it-yourself", especially regarding the development of software to test hypotheses. But the investments needed to make the experience replicable, for the industrialization of software, is too demanding. This weak appetite for publishing FLOSS programs should also be partially explained by the fact that the gains expected do not cover this extra investment.

The reason for researchers to participate in platforms or standards development is that they have been asked to do so and supported, which is interesting for funding new research, but also to make their work better known. In the remote sensing and bio-computing cases we studied, researchers were funded to contribute, developing "clean" software, i.e. well-developed, documented programs, and in these cases, developed under rules that make them work with other programs. In electronics, if a patent is published on a technology (an algorithm and a circuit, most of the time) and integrated into a norm, this generates financial flow for the lab (and for the researcher).

They may also have institutional incentives to publish "clean" software, if it is requested within a bilateral cooperation (research contract), but this remains marginal in the activity of the researcher (i.e. the algorithm producer). If the notion of scientific engineering is relevant when looking at scientific production, the reward for researchers is the same for all, and based on classical science behavior: the publication of articles in recognized scientific journals within the discipline. The researchers interviewed confirmed the attachment to the academic scientific values that [START_REF] Lamy | L'autonomie scientifique face à la mercantilisation. formes d'engagement entrepreneurial des chercheurs en france[END_REF] found in other fields. But the process is quite long (time for an article to be accepted, time for establishing one's reputation), and uncertain (will the article be accepted?)

Interviewees know and practice other channels of diffusion: bilateral agreement (secret), or IPR system (patents and their exploitation in bilateral or standard setting organizations, software diffusion and the choice of the license), but before being selected by users (especially for contractual relationships), one has to have solid scientific recognition, and thus to have published.

"Actually, publication is also a showcase of what we can do. And the contract we won with [....], was won precisely because we show our competencies, acknowledged by publications, in that domain." (Maurice, 30, associate professor in Remote Sensing).

Thus, the user system, and the specific demands set by the researchers' employer (the organization) to be part of this system, may be, in the short run, more interesting. This is why researchers agree to participate, in a contractual basis, in applied projects aiming to develop FLOSS platforms, providing FLOSS programs. But in their everyday work, there is no such incentive, as the publication of the source code of the program used to process the simulations joined to the presentation of the method is neither compulsory nor common practice in the disciplines studied. Some researchers also pointed to the fact that publishing software may even be counter-productive in the scientific perspective, as providing the software program to competing labs helps them to close the gap more quickly, thus diminishing the original inventor's room for publication, access to collaboration with the industry and therefore to financing, etc.

In conclusion, the costs for open sourcing work are perceived as high and the gain for the better only slightly positive.

On the other hand, access to new algorithms is facilitated by their open-sourcing, as it accelerates the appropriation of new algorithms. This point was stressed by all the people we met. It is not a necessity as these users understand the methods published in scientific journals and are able to re-develop the implementation of the method, whether it is software (bio-computing, remote sensing) or hardware (digital communications), but it is a real added value.

To sum up, we are facing a classic collective action paradox: even if agents agree on the fact that a FLOSS organization will improve the efficiency of the domain, on both the algorithmist side and the end-user side, no one seems to be ready to invest the extra cost for producing the IP.

"Why have we lost this leadership? Because nobody could dedicate himself 100% to the project, for many people it was something in addition [to the day-to-day work], there is something like activism in that, and eventually, if you cannot direct enough human resources to the project there are risks of loss of impetus." (Cyril,Computer Service,40) Does this disqualify the idea of having a FLOSS factory without a FLOSS IP regime? We address this question in the conclusion.

Conclusion: FLOSS without user as innovator?

This study of "applied" science knowledge production has shown that today, FLOSS is not a solution on its own to knowledge diffusion, because there are not enough incentives for researchers to publish their software, and there is a need for extra investments to integrate the software produced into a chain of production. The design of standard platforms may help for that, but for the time being, knowledge producers still don't have incentives to contribute to these platforms. When users are not producers, in contrast to the traditional FLOSS incentive regime supporting a FLOSS factory, the other two traditional regimes do not seem able to take over in the long run. This situation may change with the evolution of the system of evaluation in science, which has been initiated in bio-computing.

Actually, though the impact of publication of a piece of software seems low (but not null) in remote sensing or in digital communication, according to our interviewees, it is quite important in biology (new algorithms for biology). The availability of a program (usable, thus under an open source license) is said to increase the citation of the original article. This is because, when an algorithm is used in a biological experiment, the article is cited in the biological article by the frontier actor, namely the bio-statistician who co-authors it and "has to put a paragraph in the article about the statistical techniques used". For some scientific publications of new algorithms, the program must even be open sourced 23 . And because some frontier users can understand it and must cite the original publication when using an algorithm, there is a direct classical science reward. This could be combined by an increase in the interest for some platforms from the algorithmist side 24 . In those sciences, there are problems in evaluating the efficiency of the algorithm, and comparing it to an already existing one, replicating the simulations from the original paper. Thus, at the research community level, a collective platform implementing standard chains of treatment and providing standard data could be of interest for the community. This would facilitate the replication of the tests performed on the programs and the benchmarking between two algorithms. According to some researchers we met, this evolution could be facilitated by the increase of the level in programming skill in the younger generation of researchers (they develop "better", "cleaner" programs, even for their own needs). In that scheme, at least for the algorithmist side, it would be a way to create a FLOSS IP regime, as the producers would become the users of the platform.

But to transfer the finding to users, the existence of frontier users is still a necessity, and these specialists do not really need the second step of codification which is the platform, the workflow: how using the new algorithm, linked with which program, for which kind of thematic problem, etc. So capitalization remains at the individual level and is not really transferred to end-users. In these communities, we have not identified actors who are both producers and users. The best proxy is the frontier user (or boundaryactor), but who remains a user (and not a producer of original algorithms).

What this work shows is that even if there is a common goal to focus algorithm production on more efficiency, productivity and re-usability, and a common agreement to say that FLOSS may be a tool to do so, the perceived rewards are too low for both users and producers for them to invest in the extra cost of involving themselves in the project.

We may not have looked at the right platforms, because they are still in their infancy. But we think this works in our favour: when the developers are not the users, the initial phase to build a platform, which is the harder phase in a collective action [START_REF] Marwell | The Critical Mass in Collective Action: A Micro-Social Theory[END_REF], is too difficult to overcome to create a sustainable project, because people do not anticipate the success of the platform and the additional reward it may provide. The financial incentive may be used to bootstrap the platform with content, but, even then, it did not appear sufficient to attain the diffusion phase. Before building a new "open source" garbage can, the sponsors or the managers of such projects have to create sustainable incentives, which may be connected with people's work: here it means scientific reward or obligation (you have to open source the code used to be published, for example). In other fields, these rewards remain to be defined and may be field-specific.

This opens the way for future research: first of all, as our work is exploratory, can this result be generalized? Other studies, in other countries, are needed for that. Secondly, as far as management of science is concerned, and from an open science perspective, it would be interesting to explore other open initiatives to see whether the researchers have found more rewards or have created incentive frameworks to open source their work. As noted, scientific journals increasingly require the sources of the program for publication; this must be explored. But incentives can also arise internally, with direct reward from the institutions that hire the scientists. Research on institutional interest in participating in open source projects may be an important next step.

  : one has to understand how the community works, what the global structure of the project is, what new features are expected, what programing language(s) is/are in use, etc. FLOSS organization may decrease the cost in comparison to a classic closed platform

  But, as far as algorithm producers are concerned, even if they have a positive opinion of open source, open-sourcing the program they have developed when conceiving a new algorithm (to test it) is not obvious. "That is, I do plenty of things otherwise, that I do not systematically put. [...] Because... well, for the time being, I judge it is not mature enough, to propose it as a FLOSS, because it needs to be quite under control. The development is, all the same, quite... quite important, to do open source, you need the open source program working, you need comments everywhere, you need guarantees of... of quality which are rather more high than if you simply make the program for your own machine, with data you control quite well. So, this is why, you have to do the ratio between the time spent to make a good program to provide the FLOSS and the time spent only to develop the method, validate it to, finally publish it directly." (Maurice, 30, associate professor in Remote Sensing) [Question. "Do you open source your programs?"] "No, clearly, because, most of the time, we make our stuff as it is, and them, when speaking about handing over [the piece of software] it requires re-writing..." Question. "This is it. So, you give up" "Well, it takes time, so, of course". (Delphine, 40, Professor in Digital Communications)

"

  So, this interaction algo-circuit, it is... It is not that simple to make it live, because, you always need to maintain this double competency, because, if not, you are losing. . . well, at some point, if you are going too much on the circuit side, you are losing all the algo part, thus you cannot really be innovative; on the other hand, if you are going too much on the algo side, well, you do what the other algos do, thus. . . " (Joseph, 50, Professor in Digital

	Communications)

Foray and Cassier (2001b,a) propose a synthesis of the discussion of the ins and outs of the economics of knowledge creation and insist on the need for the creation of incentives for the producers of knowledge to produce and to diffuse.

The study of FLOSS participants' motivations has already generated a vast literature, reviewed by[START_REF] Lakhani | Why Hackers Do What They Do: Understanding Motivation and Effort in Free/Open Source Software Projects[END_REF] and[START_REF] Shah | Motivation, governance, and the viability of hybrid forms in open source software development[END_REF],[START_REF] Scacchi | Free/open source software development: recent research results and emerging opportunities[END_REF] among others.

Here, we mean the scientific and industrial field the platforms address, their technical characteristics if they matters.

For a review of the literature and a presentation of the case of bio-computing, see O'Day et al. (2001).

An algorithm is a "procedure that produces the answer to a question or the solution to a problem in a finite number of steps" (Britannica Concise Encyclopedia). "In writing a computer program to solve a problem, a programmer expresses in a computer language an algorithm that solves the problem, thereby turning the algorithm into a computer program". (Sci-Tech Encyclopedia)

http://wordnetweb.princeton.edu/perl/webwn?s=digitalcommunication

http://committees.comsoc.org/comt/

In the first case, this translation means the design of a chip (hardware translation via a dedicated software language VHDL, VHDL standing for "VHSIC hardware description language"), in the second the development of a program (software translation).

"Founded in 1961, the Centre National d'Etudes Spatiales (CNES) is the [French] government agency responsible for shaping and implementing France's space policy in Europe". http://www.cnes.fr/web/CNES-en/ 3773-about-cnes.php

11 http://www.orfeo-toolbox.org/packages/OTBSoftwareGuide.pdf

http://www.sb-roscoff.fr/SIG/spip.php?rubrique7

http://mobyle.pasteur.fr/cgi-bin/portal.py

http://www.opencores.org/

See, for instance, the open source science initiative, http://www.opensourcescience.net/, or the online initiative to cure tropical deseases, in biotech, http://pubs.acs.org/cen/science/84/8430sci1.html.

Extract fromhttp://international.telecom-bretagne.eu/welcome/research/ laboratories-networks/labsticc/

See table 1 at the end of the article. The guide was adapted from the one proposed by the CCCP-Project, http: //www.cccp-prosodie.org/spip.php?article40

In the rest of the article, the words or expressions between quote are translations of citations from our interviewees.

For a study of that role in the case of engineering, see[START_REF] Evan | The engineering technician: dilemmas of a marginal occupation[END_REF]

Defined as a good a user needs to use to be able to value, such as information. SeeShapiro and Varian (1999, p. 5).

Due to technical interrelation[START_REF] Arthur | Competing technologies: an overview[END_REF]: the more programs there are available, the more users these platforms may have, and the more users they have, the more interest the algorithm producers may have in contributing.

[START_REF] Stallman | GNU Coding Standards. Free Software Foundation[END_REF] al. (First publication 1992, this version, 2009) explain what Free Software is for the creator of the idea.

See the "software" section in Bioinformatics journal intruction to the authors, http://www.oxfordjournals. org/our_journals/bioinformatics/for_authors/general.html

We thank Patrick Meyer and Patrick Hénaff (Télécom Bretagne) for having formulated this idea in an informal discussion we had with them.

Choice of the methodology Qualitative research approach by means of semi-structured interviews with individual actors participating (or not) in collaborative platform projects (FLOSS factories). Methodology based on [START_REF] Beaud | Guide de l'enquête de terrain: produire et analyser des données ethnographiques[END_REF]; [START_REF] Blanchet | L'enquête et ses méthodes[END_REF]; [START_REF] Bertaux | Le récit de vie[END_REF]. We identified the players interviewed given a) their visibility in the selected collaborative platform projects, or b) their visibility in a disciplinary field in which the selected projects are developed. In our investigation, we have always given priority to the individual (personal and professional) dimension of the contribution to these projects against the institutional dimension.

Data collection Development of an interview guide based on the one used in a research project on on-line communities of practice (ANR CCCP-Prosodie).

Semi-structured interviews (21) with researchers and research engineers in the disciplines selected, contributing (or not) to these platform projects.

Recording and full transcript of the interviews.

Interview guide.

Adapted from http://www. cccp-prosodie. org/spip.php? article40 From interviews, we wanted to include the participation, or lack of participation, in this type of project, the terms of participation (eg. if it was part time or not), and a commitment (or not) to the the idea defended by these platforms in the scientific production. The main themes developed in the guide were:

-Description of who the actor is, his job, his activities.

-Description of what he does in the selected project, the way he contributes to the project (production, how he works and produces knowledge, role, relation to the production, relation to the project).

-Description of the project's activities (relations, actors, activities).

-Commitment to the project: description of the process steps of the actor's commitment to the project, from the first contribution to greater involvement. The goal is to identify the various registers of the commitment. Evolution of the actor in the career of the project.

-Evaluation by the interviewee of his project participation (relative to the project itself, the job, and the scientific field).

-Professional career.

Data analysis

Classical sociological manual procedure to analyze thematic content of the interviews: a horizontal analysis, i. e., for each interview the characterization of the main themes emerging from the discourses, then a transverse or vertical analysis for all interviews, i. e. the identification of the common themes amongst the interviews, and the confrontation of the actors' positions on each theme. Content analysis on each selected disciplines, then on all the software platform projects.


Aurélie Villard

Alan Lelah

Daniel Brissaud

Marc Mantelli

An eco-design tool for manufacturers of semi-conductor technologies: looking for environmental opportunities in the design phase

Keywords: design, life cycle assessment, semi-conductor technologies

à la diffusion de documents scientifiques de niveau recherche, publiés ou non, émanant des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

I. INTRODUCTION

s the society has turned into a connected and wired community, the fast multiplication of electric and electronic products contributes to increasing environmental issues like ever-increasing world-wide electrical consumption and considerable production of ewaste. To control these blended exponential developments, design of electric and electronic products have been subject in these recent years to a growing body of environmental legislation e.g. European Union's directives (RoHS and WEEE). Facing with this higher and higher legislative pressure, finished goods manufacturers which launches their products had to better know intermediate products. That pressure justifies that an effort has to be done in the semi-conductor industry to design and manufacture cleaner components or at least to better know the environmental impact generated by all these microelectronic components. Semi-conductor industry usually responds to external pressure on environmental concerns thanks to its natural strategic roadmaps. The semiconductor industry mainstream is driven by a phenomenon, called Moore law, which states that the number of transistors that can be placed inexpensively on an integrated circuit (IC) doubles approximately every two years. This phenomenon induces a positive effect on the environmental because there are more and more components per unit of production, the wafer, for quasi equivalent manufacturing flows between technological generations, called nodes. Moreover, designers are challenged by customers to design low power and energy efficient circuit which drastically reduces the environmental impact of the microchip during its use phase. These trends have lead high technical challenges and have forced designers to be more and more competitive by developing innovative solutions.

II. ECO-DESIGN IN THE SEMI-CONDUCTOR SECTOR

Since designers deal with increasing technological stakes in relation to both the miniaturization of circuits and the search for energy efficiency, up to today, the design effort has been essentially limited to energy saving solutions or to responding to legally enforced restriction of substances [START_REF] Ekvael | Attributional and Consequential Environmental Assessment of the Shift to Lead-Free Solders[END_REF]. In this context, an assessment in the semi-conductor sector shows that the direct integration of environmental constraints in ICs design is an emerging concern [START_REF] Griese | Sustainable development of microelectronic technology processes integration of ecodesign[END_REF], [START_REF] Higgs | Developing an overall CO2 footprint for semiconductor products[END_REF]. Drivers in new technologies design comes from standard industrial values: cost, functionalities, size and efficiency. At designer-level, there are other constraints: electrical performances, testability, reliability, choice of materials (avoid banned substances like PFOS in photoresists). Environmental performances are not measured and so cannot weigh in decision-making processes. Eco-design represents a natural process for industries wanting to assume their role in safeguarding the environment and resources. The constraints linked to environmental impacts become a decisive factor which can be systematically integrated in the earlier stages of technology development. There have been several previous approaches [START_REF] Griese | Sustainable development of microelectronic technology processes integration of ecodesign[END_REF], [START_REF] Krishnan | Quantifying the Environmental Footprint of Semiconductor Equipment Using the Environmental Value Systems Analysis (EnV-S)[END_REF], [START_REF] Harland | Design for Environment in New Semiconductor Manufacturing Technologies[END_REF] to An eco-design tool for manufacturers of semiconductor technologies: looking for environmental opportunities in the design phase A develop environmental design and evaluation tools for the semi-conductor industry. Murphy [START_REF] Murphy | Development of Parametric Material, Energy, and Emission Inventories for Wafer Fabrication in the Semiconductor Industry[END_REF] developed a parametric model to predict changes in inventory in wafer fabrication process. Somani [START_REF] Somani | Environmental Impact Evaluation Methodology for Emerging Silicon-Based Technologies[END_REF] provided a "rapid methodology for evaluating new process options". The direct duplication of such methods for a given company poses several problems due to their theoretical complexity, the nature of the necessary information or the in-house organization of work in the company.

The paper describes an eco-design tool, based on a processlevel approach and supported by advanced environmental analysis. The tool is dedicated to manufacturers of semiconductor technologies working in a specific industrial context. First, the method for environmental evaluation of IC technologies is described. Then the functionalities of the tool are described and finally a demonstration is done on an underdevelopment technology.

III. ENVIRONMENTAL EVALUATION OF IC TECHNOLOGY

A. IC technology description The manufacturing of an IC is a complex process with the trend of being more and more intricate. The "manufacturing route" is made of more than four hundreds steps. The specificity of IC lies in the fact that a lot of secondary materials, which does not remain on the final microchip, are used during the manufacturing phase [START_REF] Schischke | Is small green? Life Cycle Aspects of Technology Trends in Microelectronics and Microsystems[END_REF]. The basic pattern is the transistor, drawn on a silicon wafer substrate. Metallic interconnections between transistors, in copper or aluminum, determine the specific function of the electrical circuit. To draw features on the initial silicon substrate, the successive poly-silicon, oxide and metal layers, a series of successive photolithographic and etch loops are applied to the wafer: more than 40 loops and as many photolithographic masks are likely to be used for the more complex ICs. Then, a manufacturing process flow can be divided in "modules", which correspond to specific "mask levels" and so process loops. For instance, for the module "definition of the electrode of a transistor", the process sequence is: photolithography, doping and photoresist removal; whereas for the module "formation of via between two metal layers" -to establish electric connections-the sequence is: photolithography, etching, photoresist removal, material deposition and polishing. Depending on technology specificities, this sequence can vary with extra cleanings, decontaminations and hardenings.

B. Applying LCA to IC technology

Life cycle assessment (LCA) was selected as the support for designers for marking design option regarding a large spectrum of environmental impacts [START_REF] Bhander | Implementing life cycle assessment in product development[END_REF]. LCA is a tool used for drawing the profile of a product, which means determining the potential environmental impacts generated by this product over its full life cycle. Since the last decade, environmental analysis of microchips has been progressing towards LCA [START_REF] Williams | Environmental impacts of microchip manufacture[END_REF]- [START_REF] Boyd | Life-cycle assessment of computational logic produced from 1995 through 2010[END_REF]. However, it is important to keep in mind that main challenges still remain in developing LCA tools to improve data modeling, keep pace with technology changes and handle uncertainty [START_REF] Plepys | The environmental impacts of electronics. Going beyond the walls of semiconductor fabs[END_REF]- [START_REF] Schmidt | Life cycle assessment of silicon wafer processing for microelectronic chips and solar cells[END_REF].

In our case, seven indicators are considered: they are called µ-KEPIs for "Key Environmental Performances Indicators for microelectronic industry", related to the main concerns of semi-conductor manufacturers. The seven µ-KEPIs, µ 1 to µ 7 , are (without any order of magnitude): greenhouse effect, abiotic depletion, eutrophication, eco-toxicity in water, photochemical oxidation, electricity consumption during manufacturing and water consumption during manufacturing. As illustrated in Fig. 1, the scope for LCA is cradle to gate: IC life cycle starts when raw materials and energetic resources are extracted from the soil and finishes at the end of manufacturing. I summarizes the data needed and the system implemented to collect them based on an industrial context. Environment service of the industrial plant Any IC system is complex to model for LCA perspectives. A specific scope is required which is different from the one established for conventional products. Williams [START_REF] Williams | The 1.7 Kilogram Microchip: Energy and Material Use in the Production of Semiconductor Devices[END_REF] developed the concept of the secondary materialization. It was the proposition that "increasingly complex products require additional secondary materials and energy to realize their lower entropy form". In other words, the model for an IC is focused on the secondary raw materials used during manufacturing rather than on its structure or composition. A LCA is assessed separately on each manufacturing process step. Then the impact of the technology is the sum of the impacts of all the elementary process steps.

C. Use LCA to assess IC technology alternatives

LCA is used to compare a situation to its initial situation. The relative evolution of a new item N compared to parent item P is monitored with the deviation stated in percentage, for each µ-KEPIs (1). I (N; µ i ) refers to the µ-KEPI impact of the new item N on the µ i . An item can be a technology, a module or a manufacturing step. The deviation can be stated independently in that way on the 7 µ-KEPIs.

[ ]

100 * ) ; ( / ) ; ( ) ; ( ) ; / ( i i i i µ P I µ P I µ N I µ P N Dev - = (1)
In some cases, the trend -diminution or augmentation of the impact-can be different on the 7 µ-KEPIs. Then the environmental status of N compared to P, is stated with a single indicator, e-Dev(N/P) [START_REF] Griese | Sustainable development of microelectronic technology processes integration of ecodesign[END_REF].

∑ = = - 7 1 ) ; / ( ) / ( i i µ P N Dev P N Dev e (2)
The value of e-Dev defines if the change is benefit considering the environment (table II).

IV. TOOL DESCRIPTION

A. Expectations from an eco-design tool

For the deployment of a tool for eco-designing an IC technology, a designer-centered method was applied. The technologists' specificities were considered and mainly the data they can provide in the earlier stages of development. An opinion survey helped to define the outline of the results they judge to be useful and easy-to-use. The following of this section describes the automatic interface for simulating the process flow options during development of a semi-conductor technology by the technology designers, called technologists.

Eco-design tool for technologists is applied on technologies under development that are not yet materialized. It is organized around three axes: -Characterize environnemental performances of design options -Control evolution of new generations compared to previous generations and so limit impacts of future technologies -Find areas for improvement by underlining weak points.

In order to fulfill the three goals, the main stage consists of acquiring a better knowledge of the effect on the environment of the technology manufacturing, thanks to LCA analysis.

B. Quick LCA for IC technologies

Technologists' constraints are carefully evaluated to be in line with day-to-day tasks: the involvement is ensured more efficiently if the time allocated to the task is optimized and its complexity easily manageable. The technologists are in charge to make evolving already existing technologies and improving their performances. They are neither radically creating new processes nor dealing with complex technological leaps. Hence, a strongly simplifying hypothesis is made: there is no radical design; the technologies are designed by analogy. So it is assumed that there is already the exact or a close version of all the elementary process steps used for manufacturing the IC, most materials has been already tested and the equipment properties are known. In terms of environmental pollution, that means that the evaluation can be easily managed if we consider that all the elementary recipes have been at least once used in the plant. To do this, an option for assessing "Quick LCA" has been specifically developed for describing technology structure. Indeed, at the first stages of the design, the exact manufacturing route of the technology is not yet fixed: according to the functional specification, the technologist describes its technology in terms of structure and nature of different modules. According technologists experience, 20 key-parameters have been identified for modeling any nonvolatile memory (NVM) technology. The technologists define a new key-parameter when a new structure is created. The technologist specifies the changes compared to the referent technologies based on these key parameters. For instance, the key parameter "salicide" -which is an alloy used to minimize parasitic resistance between silicon parts and metal levels -depends of the material forming the layer and its thickness: if at least one of these parameters is modified, in-coming and out-coming flows can be deduced from assimilated modules. "Quick LCA" option gives the opportunity to quickly deduce the µ-KEPIs of a new technology by downloading µ-KEPIs of parent technologies. The only condition is that parent technologies have been already evaluated and that their key parameters and associated µ-KEPIs are recorded in a database. In some cases, a module has already been elaborated but needs major updates to match the new technology specificities. Then, it is interesting to go deeper into the details and so evaluate directly the impact of a new module with a specific LCA. It is particularly advised when the sign of the deviation cannot be deduced instinctively or when the manufacturing technique is innovative.

C. Processing of results

Once "Quick LCA" is applied to the new technology, projected environmental impacts of a technology were known several months before its industrialization. LCA results processing permits to identify hotspots: the main contributions to impact categories (emissions, raw materials, chemical substances, water or electricity) are computable so as to the most impacting modules or manufacturing steps. Moreover, the technologist has the opportunity to compare two design options (e.g. two materials forming the salicide) and observe their effect on the environment. Indeed, the calculation of deviation between two options is automatized. A database in a searchable format is available for consultation: it provides a "ranking" of the deviation between modules. It can be easily translated into guidelines e.g. "CoSi 2 Salicide -2000 Angstroms" is preferable to "NiSi 2 Salicide -2000µm" or "Back-end of line copper" is preferable to "Back-end of line Aluminum". Eco-design measures can be proposed at the end of the process. At this stage of the design, the manufacturing route is not yet fixed: the presentation of the different LCA results highlights hotspots and the technologist gets enough time and margins of freedom to modify the most damaging modulesat the condition of having unchanged quality and performance characteristics.

V. APPLICATION OF THE TOOL TO A DESIGN REVIEW

A. Preliminary work

The tool is tested by a R&D team on an under-development technology N. This demonstration occurs during a design review. The development of an IC technology takes about two years. Regularly during this period, the design team joins together during design reviews to evaluate the job achieved by the different actors and to make a point on the future steps. At this design stage, most of technical specificities of the technology N are fixed but no sample has been yet tested. The team is constituted with 5 technologists and a project leader. The experimentation aims at measuring, by means of the eco-design interface, the future environmental impact of this technology N once it will be industrialized and ready to be integrated in its final electronic application. The technology N is designed in order to answer new technical specifications in terms of functionalities and dimensions: IC size, electricity consumption and memory access time will be decreased. Three technologies have been identified by the project leader as parent technologies coming from older technological nodes. They are numbered as follows P 1 , P 2 and P 3 . The technology N has mixed characteristics coming from these pre-existing technologies but they are all NVM technologies, based on same 90nm node. So process route of technology N has similarities with the parent technologies. Once the µ-KEPIs of the three parent technologies and theirs recipes are recorded, the environmental evaluation of the technology N can be assessed with R&D team help.

B. During design review

As a reproduction of the tool interface, Table III and Table IV summarize the data collected during the meeting. Grey boxes were filled by activating drop-down menus. For 14 Type-A parameters, the technologists reply to a theoretical path of questioning: the most likely key parameters, the maturity status of each parameter (already existing or new) and in the first case which is the parent technology. The technologists have the possibility to mention other modules not included in Type-A parameters. For the Type-B key-parameter, the technologists capture the value directly. Finally for Type-C key-parameters, the technologists answer by "yes" or "no" for describing the types of transistor embedded into the technology. Only two of the three parent technologies, P1 and P2, were useful to conduct the analysis on the technology N. No new structure has been planned to be designed for this technology.

For "active" and "floating gate" modules, specific LCAs must be assessed with the current primary knowledge at this stage of the design. The case presented in this section for illustrating "new LCA" studies a change of the manufacturing conditions in the definition of a floating gate (FG) module. Floating gate is a specific structure of non-volatile memories used as a memory cell to stock electrons. The process sequence to form the FG is: poly-silicon deposition, photolithography for FG doping, ionic implantation in FG, photoresist removal, cleaning, photolithography for FG feature definition, cleaning, FG etching and photoresist removal. Because of the dimension shrinks, 3 process steps are modified in the new module N compared to the module from which it is inspired P: the FG doping by ionic implantation, the photolithography for the FG definition and the FG etching (Table V). As the flow is not yet frozen, the evaluation of new process modules requires assumptions on the recipe parameters (duration, chemicals flows, etc.). The benefits or losses in terms of environmental performances are not a priori obvious because there are several changes into recipes. In changing the structure of the module "floating gate" and modifying its associated processes, LCA stresses that the module is improved compared to the previous generation and so the environmental pressure of the future microchip is reduced (Fig. 3). Pareto graphs for each µ-KEPI are computable: the ten most damaging areas, the ten most damaging elementary steps, the sources which together count for more than 90% of the damage. The interpretation of these graphs allowed the R&D team to establish trends and directions for improvement and several ideas arose on how to improve the performances of the technology N. Since copper has been used for forming metal layers in "backend of line" module, the quality of water through of the indicator "aquatic eco-toxicity" has been considerably deteriorated: a survey has been opened for decreasing copper consumption and consequently emissions of copper particles in wastewater. One suggestion is to decrease the thickness initially deposited of each copper line and then to create a more accurate process to remove the excess material by CMP (chemical and mechanical polishing). Same kind of work has been asked to engineers responsible of wet benches for wafer cleanings, for decreasing ultrapure water consumption which is responsible for 16.5% of eutrophication effect. Besides to measure environmental impacts of new techniques, the experiment gives rise to other surprising observations. In FG module, the photoresist thickness for UV insolation is decreased. However, at process level, the volume of photoresist required to be deposited on the wafer is the same as previously. Indeed, the volume of photoresist coated on the wafer is a constant whatever the requested final thickness is. This volume is defined by the properties of the material like its viscosity. The thickness remaining is defined by the rotation speed of the plate on which the wafer lies and the excess is eliminated by centrifuge effect (Fig. 5). Whereas all environmental aspects are improved in the FG module, the benefit is particularly important for the indicator "abiotic depletion". Indeed, in the CML indicator for abiotic depletion, the substance "xenon, in air" is the fifth most sensitive contribution [START_REF] Guinée | Handbook on life cycle assessment. Operational guide to the ISO standards. I: LCA in perspective. IIa: Guide. IIb: Operational annex. III: Scientific background[END_REF]. Xenon is poorly used in microelectronic industry but it used in ionic implantation to create a plasma close to the wafer surface to make electrons available for the deepest levels, and so avoid charging effect. Just the fact to reduce the process duration of 10 seconds per wafer in FG doping will allow a drastic diminution, 74%, of the deviation of the ionic implantation regarding abiotic depletion effect.

VI. CONCLUSIONS AND PERSPECTIVES

The environmental evaluation of an under-development technology has been successfully assessed thanks to the collaboration with a design team. It was part of the elaboration and test of a prototype tool presented in this paper. It was expected from this prototype tool to show designers that environmental criteria could be systematically included into their decision-making process. This experiment was a first step of eco-design in this R&D team and gave several perspectives before envisaging to be used systematically for eco-design perspectives. First of all, it has proved that environmental assessment of new technology is possible. In such an eco-design process, the collaboration and active participation of all the design team is required. This method could be systemized and integrated into day-to-day work only if it is not introducing unreasonable constraints on the workload: the time allocated to this new task should be minor. That is why designers' constraints were carefully evaluated to be in line with their day-to-day tasks. Mainly, the use of a simplifying hypothesis that design is mostly made by analogy has been appreciated and supported: each time a LCA of a technology, a module or a process step is assessed, the seven µ-KEPIs are recorded in a database. The more the knowledge of old technologies accumulates, the more the range of manufacturing processes that are covered grows and so the quicker the evaluation of future technologies will be. For future deployments, the integration in STMicroelectronics of a parametric model at process level as the one proposed by [START_REF] Murphy | Development of Parametric Material, Energy, and Emission Inventories for Wafer Fabrication in the Semiconductor Industry[END_REF] is whished. It will permit to deduct the environmental impacts of future technologies at a process unit level. Moreover, the key-parameters are bound to change when other technologies like MEMS components will be modeled and when technologies will reach other technological nodes. Indeed these changes will cause the creation of new cells and structures. This experiment has validated a method able to make aware designers about environmental aspects without any unreasonable constraints. Even if the approach has not been used and integrated for design, the designers were aware about environmental impact of their activities and that any design decisions are likely to modify the environmental impacts of the future microchip. The interpretation of the results allows the whole design team to discover environmental properties of the ICs life cycle.

Fig. 1 .

 1 Fig. 1. Overview of the IC system: life cycle and scope for LCA Practically, to assess the environmental analysis, material and energetic flows, in-coming or out-coming are inventoried. The TableIsummarizes the data needed and the system implemented to collect them based on an industrial context.

Fig. 2 .

 2 Fig. 2. Cross-section of a transistor floating-gate

Fig. 3 .

 3 Fig.3. Illustration of the environmental benefit from "parent FG module" to "new FG module"

Fig. 4 .

 4 Fig. 4. Environmental impacts for technology N and its parent technologies

Fig. 5 .

 5 Fig.5. Schematic representation of the deposition of photoresist on the wafer

Table I .

 I Data collection

	Data	Source
	Process parameters	"Recipe", document managed
	(raw materials quantity, time)	by Process Engineer
	Power of equipment (Watt)	Electricity facilities
	Raw materials properties	Material Safety Datasheet
	(composition, density, suppliers
	location)
	Raw and energetic materials impacts	Eco-Invent database
	(environnemental impact per kilogram)
	Treatment of chemical waste treatment
	(gas abatement, waste destination and
	waste water plant)

Table II .

 II Environmental status according e-Dev value

	Case	e-Dev value	Environmental status
	1	e-Dev(N/P) > 10%	P is better than N
	2	e-Dev(N/P) < -10%	N is better than P
	3	0%< e-Dev(N/P) < 10% and	P is better than N
		all (Dev(N/P;µi) >0
	4	-10%<e-Dev(N/P)<0% and	N is better than P
		all (Dev(N/P;µi) <0
	5	|e-Dev(N/P)|<10% and at least two	No decision are taken
		Dev(N/P;µi) have different signs	to discriminate N to P

Some precautions have been taken to avoid inappropriate understandings by designers who are not experts in LCA results interpretations: in the fifth case, any conclusion must be done by a LCA specialist and the results are compared to in-house thresholds, fixed in function of company strategic choices.

Table III .

 III Technologists' answers for Type-A key-parameters

	Type	Key parameters	Status	Parent
					technology
		Cell		Existing	P2
		Active		New
		Matrix		Existing	P2
	A : specify if the	Floating gate	New
	module is "new" or	Salicide protection	Existing	P1
	"existing"	Salicide	Existing	P1
		Contact	Existing	P2
		Spacer		Existing	P1
		Back end of line	Existing	P1
		Last aluminum layer	Existing	P1
		Poly-metal dielectric	Existing	P1
		Gate		Existing	P1
		Inter-metal dielectric	Existing	P1
		Passivation	Existing	P1
	A: Specify non-	Extra module 1 :	Existing	P2
	mentioned modules	Floating gate doping
	Table IV. Technologists' answers for Type-B and Type-C key-parameters
	Type		Key parameters	Value
	B : capture value	Metal layers	5
	C : select "yes" for	High voltage	yes
	embedded		Low voltage	yes
	transistors; if not	Medium voltage	no
	select "no"		Ultra high voltage	no
			High speed	no

Table V .

 V Description of the changes in floating gate module

	Process step	Changes in structure	Changes in recipe
	Doping	Less deep implant : energy of	Less energy consumed
		ionic implantation is	Lower quantity of gases
		decreased
	Photo-	Smaller dimensions increase	Small variations of time
	lithography	the risks to have line collapse	(<1%)
		so the thickness of photoresist
		is decreased.
	Etching	Smaller dimensions increase	Different recipe.
		the risks of defectivity	Lower consumptions:
		(default due to wrong	Cl2, HBr, CF4
		patterns). The profile of the	Higher consumptions:
		etching into the line is	CH2F2
		changed.

ACKNOWLEDGMENT

The authors wish to thank all the members of Rousset Technology Center of STMicroelectronics, for providing support and creativity during this project.