Daniela Genius

Rodrigo Cortés Porto

Ludovic Apvrille

François Pêcheux

A Tool for High-Level Modeling of Analog/Mixed Signal Embedded Systems

Keywords: Embedded Systems, Analog/Mixed Signal Design, Virtual Prototyping, Code Generation

Embedded systems are commonly built upon heterogeneous digital and analog integrated circuits, including sensors and actuators. Model-driven approaches for designing software and hardware have been proposed, yet they are generally limited to the digital parts of systems. This paper presents the extension of an integrated modeling and simulation tool for the verification and virtual prototyping of embedded systems described at different abstraction levels to analog/mixed-signal systems.

Introduction

Model-driven techniques make use of high level models to create the specification of the software, and then rely on model transformations to generate the corresponding source code. They have been widely proposed for designing software and hardware. Nonetheless, in most cases, these approaches are limited to the digital part of the system, whereas embedded systems -e.g. robotics and automotive systems -are frequently built upon heterogeneous hardware e.g. processors, FPGAs, DSPs, hardware accelerators, digital and analog analog/mixed signal(AMS) and radio frequency(RF) circuits.

In very early design phases, rapid but less precise exploration of the design space is required. For this purpose, heterogeneous embedded systems require a high-level representation that includes models of their AMS and RF components. The problems of synchronization between the time domains of the different Models of Computation (MoC) however must be precisely considered.

This paper presents the integration of models for analog components into an existing multi-level modeling and virtual prototyping tool. In the modeling phase, we define a way to capture both digital and analog domains. In the prototyping phase, we propose a way to combine a Timed Data Flow (TDF) simulation with an event-based (SystemC) simulation.

Related work in the next section demonstrates the lack of an integrated approach offering at the same time high-level heterogeneous system modeling, for-mal verification, correct-by-construction code generation, and cycle/bit precise simulation of both the digital and the analog parts. Section 3 presents the bases of this work, Section 4 our contribution and Section 5 provides results for a larger case study.

Related Work

Well established tools in analog/mixed signal design, like Ptolemy II [Ptolemy.org, 2014], based upon a data-flow model, address heterogeneous systems by defining several sub domains using hierarchical models. Instantiation of elements controlling the time synchronization between domains is left to the responsibility of designers.

Metropolis [START_REF] Balarin | Metropolis: An integrated electronic system design environment[END_REF] is based on a high level model and facilitates the separation of computation from communication concerns. Heterogeneous systems are taken into consideration, but heterogeneity can only be represented using processes, mediums, quantities and constraints. Hierarchical models are not allowed: all processes should be implemented in the same hierarchical level. Metro II [START_REF] Davare | A next-generation design framework for platform-based design[END_REF] introduces hierarchy and allows Adaptors for data synchronization as a bridge between the semantics of components belonging to different MoCs. The model designer still has to implement time synchronization. As a common simulation kernel handles all process execution, MoCs are not well separated.

SystemC [IEEE, 2011] is a C++ class library which makes it possible to model (digital) hardware on multiple levels of abstraction. Among the frameworks based on SystemC are HetSC [START_REF] Herrera | A framework for heterogeneous specification and design of electronic embedded systems in systemc[END_REF], HetMoC [START_REF] Zhu | Hetmoc: Heterogeneous modelling in systemc[END_REF] and ForSyDe [START_REF] Niaki | Formal heterogeneous system modeling with systemc[END_REF], all having the disadvantage that instantiation of elements and controlling the synchronization have to be managed by the designer.

SystemC-AMS extensions [Accellera Systems Initiative, 2010] is a standard describing an extension of SystemC with AMS and RF features [START_REF] Vachoux | Analog and mixed signal modelling with SystemC-AMS[END_REF]. The usual approach for modeling the digital part of a heterogeneous system with SystemC is to rely on the Discrete Event (DE) part of SystemC AMS extensions. The Timed data Flow (TDF) part adds support for signals where data values are sampled with a constant time step.

In the scope of the project BeyondDreams [Beyond Dreams Consortium, 2011], a mixed analogdigital systems proof-of-concept simulator has been developed, based on the SystemC AMS extension standard. Another simulator is proposed in the H-Inception project [H-Inception Consortium, 2015]. All of these approaches rely on SystemC AMS code i.e. they don't provide a high-level interface for specifying the application.

Outside the analog/mixed signal domain, UML/SysML based modeling techniques such as MARTE and Gaspard2 [START_REF] Vidal | A co-design approach for embedded system modeling and code generation with UML and MARTE[END_REF][START_REF] Selic | Modeling and Analysis of Real-Time and Embedded Systems with UML and MARTE: Developing Cyber-Physical Systems[END_REF][START_REF] Gamatié | A model-driven design framework for massively parallel embedded systems[END_REF] are popular for capturing embedded systems, but are scarcely used for heterogeneous system design with virtual prototyping in mind. Furthermore, with very few exceptions such as [START_REF] Taha | An entirely model-based framework for hardware design and simulation[END_REF], Li et al., 2018], they do not support refinement until a low level of abstraction i.e. cycle bit accurate level nor provide full-system simulation.

The B method and more recently Event-B [START_REF] Abrial | Modeling in Event-B: system and software engineering[END_REF] model systems at different abstraction levels and makes it possible to mathematically prove consistency between refinement levels. Based on set theory and the B language, the B method is well established in large-scale public/private projects (urban transports etc.) but less widespread in industry than UML/SySML based approaches.

Very few works exist which bridge the gap between digital and analog aspects. One of these contributions stems from the Microelectromechanical systems (MEMS) community [START_REF] Bouquet | Transformation of sysml structure diagrams to vhdl-ams[END_REF]. It transforms structural SysML diagrams to VHDL-AMS code. Another is Discrete Event System Specification (DEVS [START_REF] Concepcion | DEVS formalism: A framework for hierarchical model development[END_REF]), a modular and hierarchical formalism for modeling and analyzing general systems that can be discrete event or continuous state systems; the latter can be described by differential equations, or hybrid systems.

Preliminaries

Timed Data Flow SystemC AMS uses among others the Timed Data Flow (TDF) Model of Computation which is based on the timeless Synchronous Data Flow (SDF) semantics [Lee and Messerschmitt, 1987b]. A TDF module is described with an attribute representing the time step and a processing function. The processing function corresponds to a mathematical function which depends on the module inputs and/or internal states. At each time step, a TDF module first reads a fixed number of samples from each of its input ports, then executes the processing function, and finally writes a fixed number of samples to each of its output ports. TDF modules can interact with the DE world (such as digital MPSoC platforms) using converter ports.

Figure 1 shows a TDF cluster. DE modules are represented as white blocks, TDF modules as gray blocks, TDF ports as black squares, TDF converter ports as black and white squares, DE ports as white squares and TDF signals as arrows. So-called converter ports, shown as black-and white squares, serve as interface between the TDF and DE MoC. The TDF modules have the following attributes:

• Module Timestep (Tm) denotes the period during which the module will be activated. One module will only be activated if there are enough samples available at its input ports.

• Rate (R). Each module will read or write a fixed number of data samples each time it is activated. This number is annotated to the ports and it is known as the port rate.

• Port Timestep (Tp) is the period during which each port of a module will be activated. It also denotes the time interval between two samples that are being read or written.

• Delay (D). A delay D can be assigned to a port to make it store a given number of samples each time it is activated, and read or write them in the next activation. To each of these levels, as shown in Figure 2, is associated a separate panel. The method associated to these levels explains how to take hardware/software partitioning decisions at a high level of abstraction and to regularly validate these decisions during software development [START_REF] Li | Formal and virtual multi-level design space exploration[END_REF]. Software tasks for the partitioning model are captured within the functional abstraction level, and software tasks used in deployments are captured in the software design abstraction level. In both partitioning and deployment, the computation part of tasks is then deployed to processors and hardware accelerators, and the communication and storage parts are deployed to buses and memories.

TTool allows verification and fast simulation for the digital part but also cycle bit accurate virtual prototyping on a Multi-Processor System-on-Chip (MPSoC) based on the SoCLib [SocLib consortium, 2003] public domain library written in SystemC. As SystemC-AMS is an extension to SystemC, the choice of this tool for integrating analog/mixed signal components was natural.

Integration of Analog Components

In the following, we show how TDF concepts can be integrated into a high-level modeling and virtual prototyping tool, while maintaining as far as possi-ble the idea of correct-by-construction code generation. Figure 2 uses orange circles to explain how the methodology described above has to be adapted to AMS components in TTool. Hardware parts, shown on the lower right, can be simulated with a cycleaccurate precision. Analog/Mixed Signal components are not represented on the partitioning level, as the decision to have them implemented in hardware or software is not in the hands of the designer of the embedded platforms. They must thus be visible in the deployment diagram, from which the hardware topcell and the description of the mapping of software objects to processors, memories and communication elements is generated.

Our contribution is twofold: represent SystemC-AMS components in the Deployment Diagram and integrate the the communication between digital and analog part of the platform in the prototyping code.

Graphical Representation of Analog Components in TTool

TTool has been extended with an abstract way to capture SystemC-AMS blocks with their DE, TDF and converter ports. Each TDF cluster is designed in its own panel because SystemC-AMS must calculate a separate schedule [Accellera Systems Initiative, 2010] for each of them. When a TDF module is created, it is possible to modify its attributes and parameters. The name and Timestep or Period (Tm) of a module can be defined and time resolution selected.

The parameters of a TDF module such as its internal variables or template parameters can be also set up, as it is shown in Figure 3.

Analog components are difficult to parametrize, since most components are more or less unique. Our current (and temporary) solution is to offer a dialog window where the SystemC-AMS processing() function can be provided, as shown in Figure 4.

MPSoC Virtual Prototype

If the deployment model contains only SystemC-AMS clusters, then TTool generates SystemC AMS TDF code of the components as well as the SystemC-AMS top cells from the mixed graphical/textual descriptions, and supplies a Makefile. In case software code is also deployed, processors / buses / memories must also be generated. SoCLib offers a way to describe Multi-Processor System-on-Chip platforms with semantics based on the shared memory paradigm, where digital components are interconnected with VCI [VSI Alliance, 2000] interfaces.

Components can be initiators issueing requests (typically CPUs and hardware accelerators), or targets answering to requests (e.g. RAM).

In order to combine SoCLib specification with SystemC-AMS components, we have defined generic adaptor modules that can connect SystemC-AMS components to VCI interfaces. An adaptor is modeled as a general-purpose input/output(GPIO) target component, following the modeling rules for writing cycle-bit precise SystemC simulation models for SoCLib.described below. GPIO components are visible in the deployment diagram, and, like the other VCI components, their interconnection to the central VCI interconnect is represented by an arc. Clicking on one of the GPIO opens the corresponding TDF cluster. Finally, the generated topcell is thus composed of SoCLib modules and interfaces to the SystemC-AMS clusters.

Simulation

Due to their different Model of Computation, AMS components require to execute their simulated behavior apart from the rest of the system, but regularly synchronize with the digital platform. The SystemC kernel is controlling the AMS kernel: the AMS kernel runs continuously until it is interrupted by and access to a converter port by a TDF cluster. When the TDF module accesses its input converter port, the DE simulation time advances until it is equal to the TDF simulation time of the input converter port. If later an access to an output converter port occurs with a TDF simulation time that is less than the new DE simulation time, a time synchronization issue occurs. To avoid this situation, the TDF simulation time of the output converter ports always needs to be greater or equal than the DE simulation time. This problem was exposed in [START_REF] Andrade | Pre-Simulation Formal Analysis of Synchronization Issues between Discrete Event and Timed Data Flow Models of Computation[END_REF]. Since model-driven approaches expect to ideally provide model validation before code generation (and thus simulation), we propose a way to statically identify synchronization problems [START_REF] Cortés Porto | Integration of SystemC-AMS simulation platforms into TTool[END_REF]. The schedulability of the analog part is validated using the schedulability check of SystemC-AMS [Lee and Messerschmitt, 1987a], before code is generated.

Case Study: Rover

A rover system, intended to assist rescuers to find victims buried in rubble, consists of four components: central control and motor control and the two sensors, a distance sensor and a temperature sensor.

In the rover case study already published in [START_REF] Genius | Multi-level Latency Evaluation with an MDE Approach[END_REF], we replace the two sensors by more realistic analog models (TDF still being an abstraction of the analog behavior). Central and motor control are still modeled as digital components, whereas the two sensors are modeled as independent TDF clusters. As

Software Design

Figure 5 shows the deployment diagram where the two software tasks (MainControl, motorControl) are mapped onto the CPU, the channel between the tasks on the memory. TDF clusters appear as gray boxes along with digital components, interconnected to the central (digital) interconnect through GPIO components as detailed below;

Diagrams for TDF Clusters

SystemC-AMS Component Diagram panels are shown in Figures 6 and7. The temperature sensor cluster is composed of one single TDF module. The behavior of this module, which is a simplification of the actual behavior of a temperature sensor, is described as SystemC-AMS code. It depends on the value received on its input port in, which is connected to the digital components of the system via a GPIO2VCI component. A value of 0 means that the temperature sensor should be turned off. If a value different from 0 is received, then the temperature sensor will generate random integer val-ues from 0 to 30, representing the temperature currently measured. Temperature values are written to the output port out of the module which is also connected to the GPIO2VCI component. Hence, the values will be available to be read by the digital components of the system. The temperature sensor will operate in timesteps of 10 µs.

The distance sensor cluster shown in Figure 7, is composed of three distinct TDF modules, each modeling an ultrasonic sensor. These are connected to a DE module modeling the controller which reads values from each of the ultrasonic sensors and writes a value to the GPIO2VCI component, depending on the value obtained from its input port in.

A value of 0 makes it read from the ultrasonic_sensor_left, a value of 1 from the ultrasonic_sensor_front, a value of 2 from the ultrasonic_sensor_right. Each ultrasonic sensor produces random values (from 0 to 12) in timesteps of 100 ns. The behavior of this cluster is a simplification of the behavior of a real distance sensor, since the focus of the paper is mostly on the communication between the digital and analog aspects. The state machine of the MotorControl block has only one state startMotor. It receives the two velocity parameters from the motorCommand signal and waits for some random time between 10 and 20 clock cycles. The state machine of the MainControl block is shown in Figure 8. In the following, we provide a more detailed description of the states, specially the ones that interact with the TDF clusters. By executing software functions, the CPU of the digital platform is able to write or read values from the analog components. During the first state startController, the variable sensorOn, initialized to 0 in the MainControl block, and written to the GPIO2VCI component connected to the temperature sensor cluster. The C code inserted into the startController state, shown in the upper part of Figure 9, turns the temperature sensor unit module off. Figure 10 shows the local host console output.

Interaction of Analog

In the next state readDistanceSensor, each ultrasonic sensor is selected in turn by writing a different value to the distance sensor cluster, then the sensor output is read and the read value is printed to the TTY component of the model, as shown in the code from Figure 9.

After this, the next state calculateDistance simulates how the velocity of the rover is calculated based on the front distance that was read as shown in the state diagram of Figure 8. If the distance was large (greater than 8), the state condition will lead to state0 and a normal speed would be set (a value of 5). If the distance was between 3 and 8, it would go to state1 and a low speed (a value of 2) would be set. In this case, since the front distance was 2 less than 3, the calculateDistance state condition will lead to state2. Here the state variable is set to 2. Then it proceeds to the controlTempSensor state, where, sinc state = 2, sensorOn is set to 1. In the subsequent setTempSensor state, the temperature sensor unit is turned on or off by writing the value of the sensorOn variable to the temperature sensor cluster as shown in the lowest part of Figure 9. In the output from the local host machine, we observe that at time 3196116 ns, a value of 1 is written to the GPIO2VCI component. At this point the temperature sensor unit is turned on.

In the following states, the rover will measure the temperature. Depending on the state variable, the turnDecision state will decide if the rover needs to turn or not. If the distance is greater or equal than 3, then the state variable will be 0 or 1, and the rover will neither measure temperature nor turn. In our case the distance is 2 and the state variable is 2, so it goes to the next state measureTemp.

In the measureTemp state, the temperature sensor cluster is read and the temperature is printed to the TTY, as shown in the lowest part of Figure 9. In the local host console (Figure 10), at time 3935019 ns a value of 21 is read. The output on the TTY is shown in the lower red rectangle from Figure 11.

The dodgeObstacle state calculates whether the rover needs to turn left or right, based on the distance measured from the left and right ultrasonic sensors; it will set the velocity of the left or right motors accordingly. After turning left or right, the motorCommand signal is sent to the motor control state machine, so that it can adjust the velocity of the motors. Finally, the main control state machine loops again to the readDistanceSensor state to start a new cycle. The paper shows the integration of SystemC-AMS (TDF) components into a multi-level modeling tool for complex embedded systems. Virtual prototyping can be performed from the last refinement stage, taking into account both analog and digital parts of the system. To this end, a library was created to provide read and write functions between the digital and analog components, which can be used in State Machine Diagrams modeling software behaviors. Code generation of TTool was extended to SystemC-AMS code. Yet, in order to use these functions, C entry code needs to be inserted in the state blocks of the TTool diagram. In the future, this should be replaced by specific read and write operators.

The feedback of simulation results is currently still limited to the digital part and only semiautomatic. Automating and extending this mechanism to the entire system would enable us to propose a full design space exploration environment for Analog/Mixed Signal systems.

  Figure 1: TDF Cluster

Figure 2 :

 2 Figure 2: Hardware/Software partitioning and Code generation for MPSoC platforms

Figure 3 :

 3 Figure 3: TDF module parameters

Figure 5 :

 5 Figure 5: Deployment Diagram model of the rover

Figure 6 :

 6 Figure 6: Temperature sensor model

  Blocks with the Software Design Level On the software side of the model, two blocks have been created to represent the MotorControl and the MainControl (not shown). They represent the software design level shown on the lower left of Figure 2. In contrast to the purely digital model of the same application, the functional blocks pertaining to the sensors are no longer represented in the block diagram, since they are represented by analog blocks captured in two separate SystemC-AMS panels.Both blocks communicate with each other through a signal motorCommand which is sent by the

Figure 7 :

 7 Figure 7: Distance sensor model

Figure 8 :

 8 Figure 8: Main control state machine with entry code in the startController, readDistanceSensor, setTempSensor and measureTemp states

Figure 9 :

 9 Figure 9: Entry code in the Prototyping window tab


Thi Ty

Mai Dinh

Antoine Barnabé

Thi Mai

Anh Bui

Claudie Josse

Teresa Hungría

Corine Bonningue

Lionel Presmanes

Philippe Tailhades

T M C Dinh

A Barnabe

M A Bui

T Hungria

3O4 thin films

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FIB plan view lift-out sample preparation for TEM characterization of periodic nanostructures obtained by spinodal decomposition in Co1.7Fe1.

Introduction

The iron -cobaltites spinel Co 3x Fe 3-3x O 4 (x ≥ 0.5) have not so far been as widely used in the industry as other spinels such as ferrites in particular. Their properties, however, are of interest for certain technological applications, among which can be mentioned the electrodes for batteries and supercapacitors [1,[START_REF] Mohamed | [END_REF], the absorber layers in high-performance solar cells [START_REF] Yan | Effect of spinel inversion on (Co x Fe 1-x ) 3 O 4 all-oxide solar cell performance[END_REF], the magnetic logic cells [START_REF] Bhowmik | Alloying of Fe 3 O 4 and Co 3 O 4 to develop Co 3x Fe 3(1-x) O 4 ferrite with high magnetic squareness, tunable ferromagnetic parameters[END_REF] and the microwave components [START_REF] Saxena | [END_REF]. The formation of periodic organizations at a nanoscale by spinodal decomposition within the miscibility gap of the CoFe 2 O 4 -Co 3 O 4 phase diagram also makes it possible to imagine their use in magnetoresistance components. Indeed, giant magnetoresistance effects have already been demonstrated in alloys structured by spinodal decomposition [6,7]. In a more prospective way, one could also hope to form structures having properties comparable to those of the magnonic crystals [8,9].

The CoFe 2 O 4 -Co 3 O 4 phase diagram has been established from experimental results [10,[START_REF] Takahashi | Coercive force of spinodally decomposed cobalt ferrite with excess cobalt[END_REF][START_REF] Takahashi | Spinodal Decomposition in the System CoFe 2 O 4 -Co 3 O 4[END_REF] and also calculated from thermodynamic data [START_REF] Jung | Thermodynamic evaluation and modelling of the Fe[END_REF][START_REF] Zhang | Thermodynamic modeling of the Co-Fe-O system[END_REF][START_REF] André | High-temperature thermochemical energy storage based on redox reactions using Co-Fe and Mn-Fe mixed metal oxides[END_REF]. An illustration of these calculated phase diagrams is given in Figure 1 in the 0.3 ≤ x ≤ 1 composition range whereas x = Co/(Co+Fe). It shows a miscibility gap below approximately 700 °C, which extends between about x = 0.37 (Co 1.1 Fe 1.9 O 4 ) and x = 0.90 (Co 2.7 Fe 0.3 O 4 ) at room temperature. Pure iron cobaltites obtained at around 900 °C in air are therefore subjected to a phase transformation during their cooling or during long-term heat CrystEngComm Accepted Manuscript DOI: 10.1039/C8CE01186A treatments at a temperature below 700 °C. This transformation initially occurs by spinodal decomposition. This process observed in miscibility gaps of many phase diagrams has been described by Cahn and Hilliard works [START_REF] Cahn | [END_REF]17] on the one hand, and schematized and experimentally illustrated by Harrison and Putnis [START_REF] Harrison | The magnetic properties and crystal chemistry of oxide spinel solid solutions[END_REF], on the other hand. For the Co-Fe-O system, it leads to the formation at the nanoscale, of pseudo-periodic alternations of cobalt rich and iron rich spinel phases in the central part of the spinel miscibility gap, i.e. in between x ≈ 0.47 (Co 1.4 Fe 1.6 O 4 ) and x ≈ 0.83 (Co 2.5 Fe 0.5 O 4 ) according to Takahashi et al. [START_REF] Takahashi | Spinodal Decomposition in the System CoFe 2 O 4 -Co 3 O 4[END_REF]. When the composition of the spinel phase exceeds these limits, i.e. for 0.37 ≤ x ≤ 0.47 in the iron rich part, and for 0.83 ≤ x ≤ 0.90 in the cobalt rich part, the spinodal decomposition is relayed by a germination growth mode. This mode allows the precipitation of two spinels of even more differentiated compositions respectively rich in cobalt (Co 2.7 Fe 0.3 O 4 ) or rich in iron (Co 1.1 Fe 1.9 O 4 ) and the grains of each phase are no longer distributed according to a periodic organization.

The alternations of cobalt rich and iron rich spinel phases due to spinodal decomposition in Co-Fe-O system have already been observed in well-crystallized thin films or powders initially synthesized at 900 °C and subsequently annealed at 700 °C [START_REF] Hirano | [END_REF]20] or in films deposited under magnetic field on single crystal substrates by pulsed laser deposition [21]. In the recent studies, the Co 1.75 Fe 1.25 O 4 thin films were directly prepared by radio-frequency sputtering [START_REF] Le Trong | [END_REF]. Thanks to this method, which generates only very moderate temperature elevations, the nano-crystallized films have been obtained.

Although the concomitant formation of iron cobaltite spinel and mixed cobalt iron monoxide tends to occur, the implementation of a low working pressure (0.5 Pa in argon pressure) for a magnetron sputtering power of 1.3 W.cm -1 , made it possible to obtain films virtually free of monoxide. The spinodal decomposition of such cobaltite films was studied by X-ray diffraction analysis as well as by Raman spectroscopy [23]. The high sensitivity of this spectroscopic technique, allowed to characterize the very beginning of the spinodal decomposition and revealed that a heat treatment at 300 °C for 2 h is enough to start the decomposition.

The cross sections of Co 1.7 Fe 1.3 O 4 films were already observed by Scanning Transmission Electron Microscopy (STEM) and analyzed by X-ray energy dispersive spectroscopy by the present authors [23]. A rather homogeneous distribution of iron and cobalt cations in samples annealed at 600 °C for 24 h could be demonstrated. A clear segregation of cobalt-rich phases, on the one hand, and iron-rich ones, on the other hand, was revealed in samples heated to the same temperature, but treated for 96 h. It was not possible, however, to find in the samples examined the pseudo-periodic alternations of spinel phases of different compositions, characteristic of the spinodal decomposition. The superimposition of crystallites of different orientations or compositions within the transverse sections studied, could be one of the reasons why the characteristic images of a spinodal decomposition could not be observed. The influence of the small size of the constitutive crystallites in the films on the mechanisms of the spinel phases separation could also be responsible for the absence of regular alternations of phases rich in cobalt and iron.

In this work, we try to extract the upper part of the layers parallel to the surface of the Co 1.7 Fe 1.3 O 4 films which is made up of the largest and best-organized crystallites at the atomic scale. The objective was to have thin samples whereas, perpendicular to their plane, we could observe areas in which most of the crystallites would occupy the entire thickness.

Indeed, this configuration avoids the different crystalline network superimposition which is disadvantageous for a high resolution observation of very good quality and to draw clear conclusion about spinodal mechanisms in nanocrystals. This work, therefore, raises the challenge of unconventional sample preparation by the Focussed Ion Beam (FIB) cutting technique. Also, it attempts to advance in the understanding of spinodal decomposition mechanisms in nano-crystalline thin films and to reveal a possible nanoscale effect on this phase transformation process.

Experimental

CrystEngComm Accepted Manuscript DOI: 10.1039/C8CE01186A

The Co 1.7 Fe 1.3 O 4 spinel films were prepared by magnetron radio frequency (RF) sputtering using a homemade ceramic target (conditions of preparation of the target described in previous work [23]). The sputtering apparatus is an Alcatel A450 deposition system equipped with a radio frequency-generator (13.56 MHz) device, a pumping system, a gas flow controller, a water-cooled target holder and two water-cooled sample holders. The films were deposited at room temperature on water-cooled silicon substrates. A residual vacuum close to 5 × 10 -5 Pa was reached in the sputtering chamber before introducing the argon deposition gas. At the beginning, the target was pre-sputtered for 20 hours to avoid any contamination. The target was also sputtered for 10 minutes before each film deposition. The sputtering conditions (20 W of RF power, 0.5 Pa in argon pressure and 5 cm in target to substrate distance) were optimized to obtain pure Co 1.7 Fe 1.3 O 4 spinel films [23]. Deposition rates were first calculated from the deposition time and thickness of calibration films determined using a Dektak 3030ST mechanical profilometer. The thickness of the films was then fixed to 300 nm for all the studied samples in this work by adjusting the deposition time.

Ex-situ annealings were carried out in air at 600 °C for various time up to 96 h in order to promote the spinodal decomposition. The structural characterization of the films was performed at room temperature by Grazing Incidence angle X-Ray Diffraction (GIXRD) with α = 1 deg. on a Siemens D5000 diffractometer equipped with a Bruker Sol-X detector. The X-ray wavelength was that of the copper Kα rays (Kα1 = 1.5405 Å and Kα2 = 1.5443 Å). Lattice constants were refined by the Rietveld method using the Full Prof-Win Plot R program [24,25].

Raman spectra were collected under ambient conditions using a LabRAM HR 800 Jobin Yvon spectrometer with a laser excitation wavelength of 532 nm. Spectra acquisition was carried out for 200 sec using ×100 objective lens and 600 g/mm grating. During the measurement, the resulting laser power at the surface of the sample was adjusted to 0.7 mW to avoid the transformation of the ferrite thin film (especially to avoid spinodal decomposition). The examination of multiple spots showed that the samples were homogeneous.

The structure, microstructure and composition were investigated by Transmission Electron Microscopy (TEM) using a combination of high-resolution cross-sectional imaging in STEM and conventional TEM modes, Selected Area Electron Diffraction (SAED) and Xray Energy Dispersive Spectroscopy (EDS). TEM studies were carried out using a JEOL JEM-ARM200F spherical-aberration probe corrected electron microscope equipped with a cold field emission gun operated at 200 kV, an ultrafast Ultrascan 2k x 2k camera (Gatan) for TEM, bright field (BF) and high-angle annular dark field (HAADF) detectors (JEOL) for STEM imaging, a Centurio X-ray detector (JEOL) for ultra-high resolution EDS mappings. To prepare cross sectional samples for TEM studies, slices from the sample were cut, placed between spacer layers and then glued onto a grid in such a way that the interface is parallel to the slot in the grid.

This sample is then thinned by standard tripod polishing until it is a few μm thick. The final sample is thinned using a Gatan PIPS 691 ion beam miller to create an electron transparent sample. The method of preparing plan view sections by Focussed Ion Beam (FIB) was one of the focus of this work. This method is described in detail in the following section.

Results and discussion

As-deposited sample

The structural analysis of the as-deposited and annealed samples at 600 °C under air has been detailed previously [23] by the present authors. As-deposited sample is made of small crystallites of about a few tens of nanometers, with an average composition x = 0.58. No extra phase could be detected by GIXRD measurements, as shown in Figure 2a. The cubic Co 1.7 Fe 1.3 O 4 spinel phase was refined in the F d ഥ m (n°166) space group and this leads to a lattice parameter close to a = 8.26 Å. The broadening of the diffraction peaks was in good agreement with the particle size determined by atomic force microscopy (not shown here) and TEM.

Annealed samples and first characterisation of the spinodal decomposition

CrystEngComm Accepted Manuscript

DOI: 10.1039/C8CE01186A
With the annealing time at 600 °C under air, a clear split of the most intense diffraction peaks (i.e. ( 311) and ( 440)) occurs. This split corresponds to the apparition of two spinel phases that evolve with more and more different lattice constants. The main diffraction peaks observed on the as-deposited film shift towards the higher diffraction angles during the first hours of annealing, as pointed out by the green arrow in Figure 2a and confirmed by the decreasing of the refined lattice parameter (Figure 2b). It corresponds to the progressive growth of the cobalt rich phase induced by the spinel miscibility gap. It is confirmed by the apparition of the iron rich counterpart phase characterized by a second set of diffraction peaks (red arrow) which 1) appears at lower angles and 2) shift towards the lower angles during the annealing process. Based on reference lattice parameters refined on eight homemade powders elaborated at 900 °C and quenched down to room temperature to obtain pure spinel phase in the 0.33 ≤ x ≤ 1 range (not shown here), the Vegard law allows to add on Figure 2b the limit of the miscibility gap (i.e. 0.37 ≤ x ≤ 0.90 according to thermodynamic calculations [START_REF] Jung | Thermodynamic evaluation and modelling of the Fe[END_REF]) as well as the limit of the coherent spinodal domain (i.e. 0.47 ≤ x ≤ 0.83 according to Takahashi et al. [START_REF] Takahashi | Spinodal Decomposition in the System CoFe 2 O 4 -Co 3 O 4[END_REF]). One can note on Figure 2b that, for duration smaller than 24 h, the lattice parameters of both spinels phases obtained by spontaneous decomposition correspond to compositions within the coherent spinodal domain, i.e. with an expected pseudoperiodic alternation of cobalt rich and iron rich spinel phases. For duration greater than 24 h, the lattice parameters exceed these limits, and the spinodal decomposition is supposed to be relayed by a germination growth mode with a concomitant disappearance of the periodic organization at the nanoscale. The lattice parameters of the film annealed for 96 h tend to those of the powder samples at the limit of the miscibility gap proposed by Jung et al. [START_REF] Jung | Thermodynamic evaluation and modelling of the Fe[END_REF], but not completely. This could be due to 1) the very slow kinetic of the decomposition process as demonstrated, for instance, by the very small changes of the cobaltrich phase parameter (8.16 Å for 24h down to 8.15 Å for 96h) observed in Figure 2b, 2) an inaccuracy in the spinel miscibility gap limits, as many authors did not report exactly the same values (see for instance thermodynamic calculations [START_REF] Jung | Thermodynamic evaluation and modelling of the Fe[END_REF][START_REF] Zhang | Thermodynamic modeling of the Co-Fe-O system[END_REF] plotted in Figure 1, or experimental data [10,[START_REF] Takahashi | Spinodal Decomposition in the System CoFe 2 O 4 -Co 3 O 4[END_REF]), or 3) limitations due to nanocrystalline and/or stress phenomena in thin film materials.

The spontaneous decomposition is also confirmed by the Raman spectroscopy, as shown in Figure 3a. The main peaks related to the spinel oxides and already identified in previous studies [26,27,28] are present and these main peaks evolve with the duration of the heat treatment. More specifically and using the terminology of Z. Wang et al. [START_REF] Wang | [END_REF] and O.N. Shebanova et al. [30],

these changes concern the very strong growth of the T 2g (1) and A 1g peaks at around 180 and 650 cm -1 , respectively, and the emergence of low-intensity E g and T 2g (2) peaks at around 290 and 450 cm -1 , respectively. The Raman peak intensities after 96 h of annealing at 600 °C under air could be convolved with the ones of the x = 0.37 and x = 0.90 powder reference samples (Figure 3b),

i.e. it converges to the complete decomposition of the pristine phase. For the intermediate annealing duration of 12 and 24 h, the Raman peak evolution is on-going and confirms the step of spinodal decomposition that is in good agreement with the previous XRD studies and previous works [23].

In the XRD and Raman studies, in addition to the pristine as-deposited film, two annealed samples were identified as key samples in the characterization of this spontaneous decomposition. The first one is the sample annealed for 24 h, because it was identified to be among those where the spinodal decomposition ends, i.e. it could be the most appropriate sample to highlight the pseudo-periodic alternations of cobalt rich and iron rich spinel phases. The second one is the sample annealed for 96 h, because it corresponds to the longest annealing time, i.e. the best sample to highlight the microstructure changes induced by the germination-growth mode change. To have samples observable by TEM, both annealed films were prepared according to the specific thinning process described above.

TEM studies

First TEM characterizations carried out on cross-section samples (already published by the present authors [23]) reveal that no clear contrast between the two phases formed by spinodal transformation at 600 °C for 24 h, could be highlighted. Local chemical

CrystEngComm Accepted Manuscript

DOI: 10.1039/C8CE01186A

analyses were also unable to reveal distinct phases, as shown by the homogeneous distribution of the iron and cobalt species in the cross section STEM view.

However, the complete segregation, which occurs after a much longer annealing time (96 h) at 600 °C, displays cobalt rich and iron rich regions of about 50 nm in diameter, as shown in green and red areas, respectively, in Figure 4a. The random distribution of the two phases in these regions along and perpendicular to the columns reveals, however, that the spinodal transformation is replaced by a nucleation and growth process for such a very long time of treatment at 600 °C. Some iron-rich and cobalt rich areas could be highlighted (Figure 4c). Semi-quantitative determination of the chemical composition of the phases can be done by EDS in these are also visible from the 300 nm linear analysis shown in Figure 4d, but cannot be exactly defined at lower scale (i.e. within the column diameter). This is due to the limited resolution of the apparatus, which implies to select small areas of few tens of nm 2 at minimum, for EDS quantification, but also to the superimposition of the columns in the thickness of the sample studied. The cross section coming from the standard thinning process, leads to thickness of few tens of nanometers, which is a little bit bigger than the diameter of the columns. Consequently, it's quite impossible to observe single crystalline domains, and then, even more to highlight nanoscale organisation induced by spinodal decomposition within the columns. In the STEM images (Figure 4b) and corresponding EDS mapping (Figure 4a), of the 600 °C -96 h annealed sample, a homogeneous distribution of Co and Fe could be seen in more than a third of the total length of the columns, i.e. for 100 -150 nm (see examples of Co-rich and Fe-rich areas drawn in Figure 4a and Figure 4b). As a result, a second set of samples, dedicated to TEM studies, were then cut from the upper part of the thin films, in a direction parallel to their plane. This very delicate preparation capable of revealing monocrystalline domains, has been implemented as follows.

The TEM specimens of 24 h and 96 h annealing time were then prepared using a FEI Helios Nanolab600i dual beam SEM FIB fitted with an Easylift micromanipulator for in situ lift-out. The ion column was operated at 30 kV for all steps, except for final cleaning of the specimen, for which tensions of 5 kV and 2 kV were used. Beam currents varied between 47 nA and 15 pA. Figure 5 shows the main stages in the production of plan-view TEM specimens of thin films (300nm). The sample was first mounted horizontally in the chamber and a region located at the edge of the thin film was chosen for the future observation (Figure 5a). The reason for choosing an area at the edge of the film was that the edge could be used as the top surface for the final prepared sample and that it could be easily grasped and removed. The electron beam was used to deposit a layer of Pt 200 nm thick over an area of 30 µm wide and 15 µm tall. The ion beam was then used to deposit a 3 µm thick layer over the same area (Figure 5b). A preliminary regular cross section pattern was defined so that it overlaps with the bottom edge of the Pt strip. Then, a cleaning cross section was milled (Figure 5c). Next, the sample was removed from the chamber and re-mounted vertically with the edge of the film that was identified in the previous step now topmost (Figure 5d). The same area that was coated previously, was located and a 20 µm x 2 µm layer of ion beam 3 µm thick platinum was deposited on top (Figure 5e). The preparation could now proceed in a similar fashion to standard TEM specimen preparation, except material only had to be removed from one side of the specimen. This was done using a standard cross-section milling pattern, to reach a final depth of around 15 µm (Figure 5f). The specimen was then partially cut-out and a Pt needle was fixed (Figure 5g). The sample was lifted out in situ (Figure 5h) and then attached to a copper support grid (Figure 5i). Thinning of the specimen could then proceed using a gradual cross-section cleaning milling pattern, with a final milling beam current of 80 pA (Figure 5j). Finally, the 5 kV and 2 kV cleaning steps were carried out on each side (Figure 5k).

The great care is topical in making specimens from thinner films so that the film was not lost during the thinning process.

First, the 96 h annealed film was analysed and compared to previous studies [23] and Figure 4. The TEM results are shown in Figure 6.

In the STEM image (Figure 6a), the previously observed columnar structure is evidenced by grains of around 20 nm in diameter, which correspond to the column section. The high resolution imaging in HAADF-STEM and BF-STEM modes (Figure 6d, and Figure 6e) confirms that most of the selected areas are single crystalline, i.e. that the thinning process was successful and allow to characterize the upper part of a single column. No specific crystallographic orientation could be found and this is in good agreement with the previous XRD studies and the cubic symmetry of the spinel structure.

CrystEngComm Accepted Manuscript

The average Co 1.8 Fe 1.2 O 4 composition deduced from the total spectrum (Figure 6a) is close to the nominal Co 1.7 Fe 1.3 O 4 . The slight difference is due to the limited analysed area (1.3 µm 2 ) and clear segregation of cobalt-rich phases, on the one hand, and iron-rich, on the other hand, which were evidenced by EDS mapping (Figure 6b and Figure 6c). The homogeneous Co-rich and Fe-rich compositions could be observed and they reach up the Co 2.3 Fe 0.7 O 4 (Figure 6b) and Co 1.2 Fe 1.8 O 4 (Figure 6c) compositions, respectively, in selected areas smaller than the columns diameter. These compositions correspond to the average ones recorded on twelve different selected areas.

The Fe-rich composition with a Co/(Co+Fe) ratio of 0.4 is almost the one expected by the phase diagram (Figure 1). This ratio is below the one of the limit of the spinodal transition and could explain the relative homogeneity of the composition within the grain evidenced by atomic scale elemental analysis plotted in Figure 6g. This is also in good agreement with the XRD results (Figure 2).

Oppositely, the Co-rich composition only reach a Co/(Co+Fe) ratio of 0.8, which corresponds to the limit of the spinodal and germination growth mode transition. During the first step of the decomposition process, the pristine spinel phase tends to form the Co-rich spinel phase, as shown by XRD. To compensate, the Fe-rich counterpart segregates to the grain boundary and, then, it forms the Fe-rich spinel phase in surrounding grains/columns. This increase in Fe content is evidenced on the right side of line scan 1 (Figure 6g) and on the central part of line scan 2 (Figure 6i).

The 24 h annealed film was then analysed in order to identify, within the column width, the specific atomic arrangement due to the spinodal decomposition process. The well-organized and single phase domains are predominantly present in the whole film as demonstrated by a ൣ111 ത തതത ൧ zone axis oriented grain (HAADF-STEM image in Figure 7d and corresponding to Fast Fourier Transform (FFT) pattern in Figure 7e. Typical inter-planar distance of 2.9 Å could be measured in good agreement with the d 220 of cubic spinel with lattice constant close to a = 8.2 -8.3 Å.

The average composition deduced from the total EDS spectrum over the limited area of 0.01 µm² was found to be Co 1.8 Fe 1.2 O 4 (Figure 7a). This composition is close to the nominal one, as for the 96 h annealed sample.

The analyses carried out on very small selected area with typical size smaller than the column diameter allow determining the extreme Fe-rich and Co-rich areas. These areas are selected from the EDS mapping, as shown, for example, in Figure 7b and Figure 7c. The Fe-rich and Co-rich compositions reach up a Co/(Co+Fe) ratio of 0.4 and 0.7, respectively. These limits (0.4 and 0.7) obtained after 24h of annealing treatment are similar or smaller than the ones (0.4 and 0.8) obtained after 96h. With the annealing time increase, the Co content evolves in the Co-rich part that is in good agreement with the XRD studies and consolidates the fact that the transition is still on going. Nevertheless, the Fe-rich composition is found to be outside the predicted limit of the spinodal transition domain, which supposes that the expected pseudo-periodic alternations of cobalt rich and iron rich spinel phases have disappeared. This is in good agreement with the fact that in each crystallized domain the composition is fairly homogeneous.

Further study, however, shows crystallites in which composition heterogeneities are observed for the sample treated at 600 °C for 24 h. This case is encountered for crystallites with the largest sizes. Figure 7 illustrates this. If the chemical composition is homogeneous according to the small size of the crystallite (Figure 7f and Figure 7h), it is not the same in the direction of elongation (Figure 7f and Figure 7g). The upper part of the grain exhibits cobalt excess with a Co/(Co+Fe) ratio close to 0.8 as the lower part exhibits iron excess with a Co ratio close to 0.4.

These results demonstrate that the sample treated for 24 h, for which it is expected to observe pseudo-periodic composition variations induced by the spinodal transformation, rarely shows this characteristic. From the previous observations, this sample is at the limit of the spinodal transformation and nucleation-growth domains. The infrequent occurrence of pseudo-periodic alternations of phases, respectively rich in iron and cobalt, could potentially result from the state of progress of the formation of the two spinel phases. However, the characteristic alternation of the phases is observed only when the crystallites have at least one

CrystEngComm Accepted Manuscript

DOI: 10.1039/C8CE01186A

dimension greater than the average crystallite size of the film. Moreover, the compositional anomalies observed elsewhere in the crystallite boundaries, seem to indicate that the latter hamper the establishment of pseudo-periodic composition variations. As a result, the pseudo-periodic composition variations can then only be observed in the largest crystallites, as illustrated by Figure 7f, 7g and 7h. In this case, one approaches the crystallization states of the bulk oxides (iron cobaltites or manganese aluminates, for example [START_REF] Takahashi | Spinodal Decomposition in the System CoFe 2 O 4 -Co 3 O 4[END_REF][START_REF] Cherepanova | Low and high temperature oxidation of Mn 1.5 Al 1.5 O 4 in relation to decomposition mechanism and microstructure[END_REF]) in which the pseudo-periodic alternations of phases generated by the spinodal transformation have already been demonstrated. It, therefore, seems reasonable to say that the absence of pseudo-periodic variations of composition within the crystallites is mainly due to the smallness of their size.

It should now be recognized that the development of pseudo-periodic alternations, generally induced by the spinodal transformation, can be inhibited by the nano-structure of the material. To our best knowledge, this observation was not previously reported in the literature.

Conclusions

Until now, the pseudo-periodic alternations of different composition spinel phases were demonstrated only in iron cobaltites preannealed at high temperatures (# 900 °C). These oxides, therefore, consisted of crystallites of large sizes. The thin films studied by our group were, however, prepared by sputtering, not so far from room temperature. In these films, it was not possible to observe such a pseudo-periodic microstructure even for samples processed under conditions conducive to spinodal decomposition and whose multiphase nature was clearly demonstrated by X-ray diffraction or Raman spectroscopy. This failure was attributed to sample preparation method that failed to isolate individual crystallites.

In this work, we have developed a specific preparation method which has made it possible to obtain sections parallel to the film plane. In these sections, individual crystallites can be observed and studied by different electron microscopy techniques. For a sample previously treated at 600 ° C for 96 h, it was possible to confirm its transformation by a germination-growth process, leading to oxide phases rich in iron or cobalt. The microscopic observations revealed, however, that the core of crystallites was richer in cobalt than the areas near the grain boundaries. The semi-quantitative evaluation of the chemical compositions of the formed phases also showed that the germination -growth mechanism was not completed. The compositions observed do not reach the maximum values indicated in the literature. For the sample treated at 600 ° C for 24 h under conditions favourable to the establishment of a spinodal decomposition and for which X-ray diffraction and Raman spectroscopy characterizations confirm a two-phase state, the spatial organization of the phases is similar to that observed for the 96 h treated sample. In particular, iron enrichment occurs at the grain boundaries and their proximity. However, in the largest crystallites or along their axis of elongation, it is possible to demonstrate the alternation of a cobalt-rich phase and an iron-rich phase reminiscent of the classical characteristics of the spinodal transformation.

All these observations suggest that the small crystallite size inhibits the formation of pseudo-periodic spinel phases alternations. A crystallite size effect on the development of the spinodal transformation is therefore highlighted. To our best knowledge, such effect was not previously reported in the literature. Takahashi [START_REF] Takahashi | Spinodal Decomposition in the System CoFe 2 O 4 -Co 3 O 4[END_REF].

Figure Caption list

  areas. The compositions close to Co 1.3 Fe 1.7 O 4 and Co 2.1 Fe 0.9 O 4 have been obtained. They sensitively differ from the average Co 1.7 Fe 1.3 O 4 composition of the as-deposited film. The local variations of iron and cobalt at the 50 nm scale (i.e. the column width)

DOI: 10 .

 10 1039/C8CE01186A

Figure 1 :

 1 Figure 1: Calculated phase diagram of the Co-Fe-O system in air according to thermodynamic models of I.H. Jung et al. [13] (black line) and W.W. Zhang [14] (red line). Dashed blue line represents the composition Co 1.7 Fe 1.3 O 4 of the films studied in the present work. Dashed black bold lines represent the limit of the estimated coherent spinodal domain according to M. Takahasi [12].

Figure 2 :

 2 Figure 2: a) XRD patterns of as-deposited Co 1.7 Fe 1.3 O 4 film and annealed ones at 600 °C under air for various durations. b) Refined cubic lattice parameter of different spinel phases obtained after various annealing time at 600 °C. The value reported at zero corresponds to the as-deposited film. Black lines correspond to the limit of the miscibility gap. Dashed black lines represent the limit of the estimated coherent spinodal domain according to M.

Figure 3 :

 3 Figure 3: a) Raman spectra of as-deposited Co 1.7 Fe 1.3 O 4 film and annealed ones at 600 °C under air for 12, 24 and 96 h. Iron rich Co 1.1 Fe 1.8 O 4 (x =0.37) and cobalt rich Co 2.7 Fe 0.3 O 4 (x = 0.90) powder samples are reported for comparison. b) Comparison of the calculated spectrum from weighted sum of x = 0.37 and x = 0.90 powder spectra (dot blue) with the one of 96 h annealed Co 1.7 Fe 1.3 O 4 film (black line).

Figure 4 :

 4 Figure 4: a) EDS maps obtained using iron (red) and cobalt (green) Kα and b) corresponding cross section STEM micrographs of the Co 1.7 Fe 1.3 O 4 films annealed at 600 °C for 96 h. Local EDS analyses in iron-rich and cobalt-rich area are highlighted in spectra c). Atomic composition along the horizontal axis of area 3 is plotted in d).

Figure 5 :Figure 6 :

 56 Figure 5: Secondary electron and ionic SEM images showing the different stages of producing a plan view TEM specimen of the Co 1.7 Fe 1.3 O 4 thin films (a) using FIB. First an area of film at the edge of the substrate is coated with Pt (b) and cross-section milled (c). Then the sample is removed from the chamber and re-mounted vertically (d). After a new lateralPt coating (e), a trench is milled away from the back side (f) leaving a thick lamellar which is then lifted out in-situ (g, h). The sample is then attached to a copper support grid (i) and thinned ready for examination (j, k).

Figure 7 :Figure 1 :Figure 2 :Figure 3 :Figure 4 :Figure 5 :Figure 6 :Figure 7 :

 71234567 Figure 7: TEM analysis of the 24 h annealed Co 1.7 Fe 1.3 O 4 thin film with a) in-plane STEM views and EDS mapping using b) cobalt (green) and c) iron (red) Kα rays, d) HAADF STEM image and e) corresponding FFT-pattern, f) BF-STEM view and g) h) EDS analysis with the corresponding compositions in at%.

Acknowledgements

This work was supported by a Scholarship from the Ministry of Education and Training of the Socialist Republic of Vietnam.


Bernd Giese
email: bernd.giese@boku.ac.at

Johannes L Frieß

Nicholas H Barton

Philipp W Messer

Florence Débarre

Marc F Schetelig

Nikolai Windbichler

Harald Meimberg

Christophe Boëte

Gene Drives: Dynamics and Regulatory Matters-A Report from the Workshop "Evaluation of Spatial and Temporal Control of Gene Drives," April 4-5, 2019, Vienna

Keywords: gene drive, modeling, population control, population dynamics

Gene Drives are regarded as future tools with a high potential for population control. Due to their inherent ability to overcome the rules of Mendelian inheritance, gene drives (GD) may spread genes rapidly through populations of sexually reproducing organisms.

DOI: 10.1002/bies.201900151

transgenes into wild populations and thereby increase the number of GDOs. The rapid development in this field and its focus on wild populations demand a prospective risk assessment with a focus on exposure related aspects. Presently, it is unclear how adequate risk management could be guaranteed to limit the spread of GDs in time and space, in order to avoid potential adverse effects in socio-ecological systems.

The recent workshop on the "Evaluation of Spatial and Temporal Control of Gene Drives" hosted by the Institute of Safety/Security and Risk Sciences (ISR) in Vienna aimed at gaining some insight into the potential population dynamic behavior of GDs and appropriate measures of control. Scientists from France, Germany, England, and the USA discussed both topics in this meeting on April 4-5, 2019. This article summarizes results of the workshop.

Population Biology and Control Options

Nick Barton emphasized that while GDs are based on simple technology, developing ways to use them safely is far more complex and uncertain. In nature, complex mechanisms have evolved to suppress viruses and transposons, including elaborate epigenetic mechanisms that suppress selfish genes. Although we have evolved alongside a variety of natural drives, application of artificial drives may be dangerous. Most eukaryote genomes carry a substantial burden from defunct transposons, and devote substantial genetic resources to combating selfish elements; those elements may well be an important cause of extinction. Barton raised the question whether the application of synthetic drives could ever be a local, rather than global, decision, given the high risk of unintended dispersal: GDs may be able to jump to nontarget species, just as insecticide resistance can move between species (e.g., from Anopheles gambiae to A. coluzzii). [1] If the driver reduces fitness, selection favors resistance and a GD may quickly become attenuated. Thus, a "parliament of genes" may stabilize Mendelian rules, despite the prevalence of selfish genes. [START_REF] Leigh | Adaptation and Diversity : Natural History and the Mathematics of Evolution[END_REF] Although modeling may give valuable insight into population dynamics-as Barton showed for a Wolbachia release in Australia [START_REF] Schmidt | [END_REF] -the predictions of a model will be correct only under the circumstances and variables used. Models may seriously mislead if applied when key factors are unknown, or too numerous to be taken into account.

Philipp W. Messer took up the tendency of resistance development against GDs. For clustered regularly interspaced short palindromic repeats (CRISPR)-drives in particular, resistance represents a serious obstacle that should be most prominent in suppression-drive applications. He explained that the evolution of resistance is still difficult to predict as a few mismatches can be enough to destroy a target sequence for homing endonucleasebased GDs. Here, the multiplexing-approach may help lower resistance rates, but should not be overestimated, as first results indicate: Champer et al. [START_REF] Champer | Proc. Natl. Acad. Sci[END_REF] have shown that multiplexing is not actually multiplicative and resistance could not be suppressed as effectively as initially thought. With regard to the genetic status of the population, the spread of resistance alleles can also represent a genomic alteration induced by the drive. Those individuals are not necessarily equivalent to the wild-type but rather, constitute a fraction of the population that influences spread of the drive. In an individual-based simulation aimed to model the spread of a suppression drive against an invasive rodent population on an island, Messer illustrated the importance of conducting spatially simulations. The dynamics observed in such spatial models can be fundamentally different from the models of "well-mixed" populations currently used in many studies, revealing new phenomena such as "chasing dynamics" where wild-type individuals can quickly recolonize empty areas and the drive never succeeds. These and other complexities that arise in realistic models complicate predictions on the fate of concerned populations.

Florence Débarre brought a deeper investigation of potential control options for GDs into the discussion. She asked whether the release of reversal drives could stop a drive that was either accidentally released or had to be neutralized due to unanticipated effects. Débarre presented mathematical models of the potential population dynamics, first in well-mixed population, and second, considering a population over a spatially continuous environment. [START_REF] Girardin | [END_REF] The model referred to a reversal drive of the socalled "CATCHA"-type. CATCHA, a "Cas9-Triggered Chain Ablation" of Cas9 is based on a gRNA that cleaves the Cas9 gene and inserts its own gRNA-coding sequence against Cas9. [START_REF] Wu | [END_REF] The model focusses on allele frequencies over time. Deterministic versions indicate that the drive can only be stopped if it has an introduction threshold, i.e., if there is a frequency above which the drive has to be introduced in a wild-type population in order to spread. In the absence of such a threshold, the introduction of a reversal drive leads to rock-paper-scissors like dynamics, the frequencies of the original drive and the reversal drive oscillating over time. These oscillations are of large amplitude, however, so that either construct can be lost by chance, often leading to the recovery of the wild-type population in stochastic versions of the nonspatial model.

Applications of Gene Drives

Marc Schetelig, in his contribution to agricultural applications for GDs, also referred to options for control as a dependence of a GD on a second product. Sensitizing or optogenetic drives may be used in this regard where optogenetic genes, e.g., induce susceptibility to light beams. According to Schetelig, effective control of pests for a specific time should be the goal of most GD applications. In contrast, suppression drives with the aim of species elimination and systems that are prone to resistance should be avoided. Also, resistance races with chains of GDs against the other drives might not be reasonable in terms of economic and control efficiency. Concerning regulation Schetelig noted that a case-by-case evaluation would represent the most appropriate solution because the term "gene drive" represents a multitude of possible strategies-technically, ethically, and in terms of applicability. For each system, the state-of-the-art tools should be compared for their suitability and compared in a risk assessment to new tools. This is different from the current risk assessment in Germany and many other countries, which evaluates only the novel tool without comparing it to possibly inferior "state-of-theart" control options.

Nikolai Windbichler elucidated current developments in the development of transmission-blocking GDs for malaria control.

From this viewpoint as a potential applicator, four challenges need to be addressed to bring replacement drives to the field. First, how can antimalarial effector molecules, characterized under laboratory conditions and never exposed to heterogeneous and genetically diverse Plasmodium parasites, been tested? Second, what is the long-term persistence of genetic constructs in the target population? [7] Third, which issues arise from the molecular complexity of the GD constructs that currently envision incorporation of multiple large transgenes? For a CRISPRdrive in particular, the compound fitness cost of expressing Cas9 and a cargo gene may reduce the efficiency and persistence of the drive. Lastly, regulatory complexity poses an additional challenge for GD releases. For addressing these challenges, a strategy for the design, phased testing and roll-out of transmissionblocking GDs was presented: The construction of minimal, genetically simple GD components out of mosquito genes as well as the molecular separation of the drive and effector functions allows the testing of these in the absence of a full GD. [8] Therefore, a cargo gene could be driven non-autonomously along with the drive component, where the cargo interferes with pathogen transmission. In first releases, a nondriving effector component alone would be tested for its transmission-blocking effect and its expected behavior in the field. In a second phase, a nondriving Cas9-strain would be co-released to enable local spread of the cargo gene. Finally, both effector and drive strains now resulting in full GD could be released in a third phase of this approach.

Reasons for Concern

Harald Meimberg shifted the focus to the question how GDs affect the integrity of ecosystems and to what extent they may fit with the concept of protection goals. Protection goals are considering generally a) the protected goods, b) biodiversity, and c) human well-being. Especially problematic is the treatment of biodiversity and ecosystem services as synonym in the context of risk assessment. Hence, if all ecosystem services are protected, so is biodiversity. Functional redundancy might allow the conclusion that a species can be removed from an ecosystem without perturbing the function of the system as a whole. Therefore, with this concept, if reduced to its function the value of a species is lower, if another species shows a similar function. This is especially relevant for GD applications, because here, a removal of single species from the ecosystem is attempted. To rely on potential compensation by other species hides the fact that the initial status of the system cannot be reestablished. In addition, stressors often interact and produce combined effects on biodiversity or ecosystem services.

Hype and Concern in the Discourse

Christophe Boëte highlights the fact that GD is highly transformative and shows signs of post-normal science (applications are associated with uncertainty and concerned with high stakes). GD is indeed characterized as a potentially powerful emerging technology that is confronted with multiple ethical dilemmas. There are dual concerns about this technology: the research itself and its potential applications. Concerns over potential application, in the light of the current rudimentary control options, clearly make it a candidate for the precautionary principle. Another burden for this new technology is the as yet open question of whether the potential benefits outweigh the potential costs. As seen for other innovations, communication is often a key aspect in current research but as discussed by B. Wynne, lay people are not so lay, and can have expertise too, making the public one of the stakeholders. [9] Clearly, this calls for inclusion, dialogue, and co-construction, instead of the development of communication strategy. This is a question of how to organize discussion versus how to obtain a consent and in that sense, this task implies great responsibilities. One of them is transparency and it has been already been chipped away as revealed by the publication of the so-called "gene drive files" (cp. http://genedrivefiles.synbiowatch.org/). Regarding communication of the technology, the independence of the actors involved is also key and the implication of the International Life Science Institute (ILSI), financed by Monsanto and CoopLife Int. and banned by the WHO in 2006 is not reassuring, [10] knowing the current interest of the agro-business for GD approaches. Given the potential of GD and the associated risks, the implication of the UN agencies in public health (WHO), in food safety (FAO), and in environment (UNEP) is highly needed to avoid the stranglehold of lobbyists and non-state actors on research and applications.

Perspectives

This workshop shed some light on issues that may become relevant for efforts to achieve control and to limit exposure to GD. At the current stage of development, prediction of limits in spread and persistence is severely restricted by insufficient knowledge of the behavior of GD. Orientation on the potential exposure related characteristics of GD can be obtained from models in well-defined configurations, but they remain highly dependent on a realistic choice of the potentially relevant parameters. With regard to the effect of a GD, we have to be aware of the crucial significance of biodiversity for the preservation of the "services" of ecosystems that we rely on. In the light of the insight that initial states cannot be fully reestablished after an intervention, application plans for GD have to consider the precautionary principle. Furthermore, achieving a high level of control as well as abilities to limit the spread of GDs should represent integral parts of responsible research and development.

BioEssays 2019, 1900151 © 2019 The Authors. BioEssays Published by Wiley Periodicals, Inc

Acknowledgements

The authors are grateful to Samson Simon and Mathias Otto from the German Federal Agency for Nature Conservation (BfN) for fruitful cooperation during the preparation of this workshop. The workshop was organized as part of the project "Risk Assessment of Synthetic Gene Drive Systems" funded by the German Federal Agency for Nature Conservation (BfN) (support code 3518 84 0500).

Conflict of Interest

The authors declare no conflict of interest.