A Besnard

M R Ardigo

L Imhoff

P Jacquet

Curvature radius measurement by optical profiler and determination of the residual stress in thin films

The Stoney formula, based on the measurement of the substrate curvature, is often used for the determination of the thin films' residual stress. In this study, titanium nitride coatings were deposited by DC reactive magnetron sputtering on silicon substrates. An optical profiler was used to determine the curvature of the surface before and after coating. Two radii were then obtained, along the principal perpendicular directions of the surface curvature. A simple and efficient method to determine the experimental error on the stress calculation was developed taking into account the film thickness dispersion and the radii dispersion. Using constant deposition parameters, some samples' characteristics were tested: film and substrate thickness, size, shape and crystallographic orientations of the substrates. With the help of the developed error method, we analyzed what can be conclude about the influence of these characteristics on the calculated stress values, obtained from the experimental measurements.

Introduction

Stress determination in thin films is a common thematic in literature, as it has an impact (in a positive or a negative way) on several scientific and practical applications [START_REF] Abadias | Stress in thin films and coatings: current status, challenges, and prospects[END_REF][START_REF] Freund | Thin Film Materials: Stress, Defect Formation and Surface Evolution[END_REF]: mechanical and protective coatings, sensors, optical devices, or films with a specific physical/ chemical function (thermal, magnetic, electrical, biological…). There are three main approaches in the study of stress in thin films. The major and uncountable part of publications mentions stress only as one among other film properties. Some authors use the XRD technique [START_REF] Abadias | Stress, interfacial effects and mechanical properties of nanoscale multilayered coatings[END_REF][START_REF] Abadias | Stress and preferred orientation in nitride-based PVD coatings[END_REF][START_REF] Xi | Film thickness effect on texture and residual stress sign transition in sputtered TiN thin films[END_REF], other the curvature method [START_REF] Detor | Stress and microstructure evolution in thick sputtered films[END_REF][START_REF] Bouaouina | Correlation between mechanical and microstructural properties of molybdenum nitride thin films deposited on silicon by reactive RF magnetron discharge[END_REF], few both techniques [START_REF] Vaz | Residual stress states in sputtered Ti1-xSixNy films[END_REF]. In these studies, the calculated stress values are put in relation to film properties (i.e. hardness, grain size, thickness, composition, etc.) or to process parameters (power, temperature, pressure, etc.). Other authors work on the in-situ measurements of the stress to understand the initial stages and the origin of stress [START_REF] Abadias | Stress in thin films and coatings: current status, challenges, and prospects[END_REF][START_REF] Boyd | situ curvature measurements of epitaxial YBCO films on MgO[END_REF][START_REF] Abadias | Influence of particle and energy flux on stress and texture development in magnetron sputtered TiN film[END_REF][START_REF] Chason | Tutorial: understanding residual stress in polycrystalline thin films through real-time measurements and physical models[END_REF]. Finally, the last group of research concerns the theoretical study of the stress-strain relation with analytical models and FEM computations. In this domain, the reference publication is the one of G. G. Stoney [START_REF] Stoney | The tensions of metallic films deposited by electrolysis[END_REF] and, more recently, a review was published by Janssen [START_REF] Janssen | Celebrating the 100th anniversary of the Stoney equation for film stress: developments from polycrystalline steel strips to single crystal silicon wafers[END_REF]. In these works, the limits of the Stoney formula are studied in detail based on mechanical and theoretical considerations. However, very little information can be found in the literature about the limits of this method from a measurements point of view. In other words, plenty of models, behaviors, theories, corrections, all as much interesting and valuable as the other, can be found, but nearly nothing about determining a stress value which has a sense and can be used in this kind of analyses. Consequently, the aim and novelty of this paper is to fulfil this lack and to give some practical clues about the estimation of the relevancy of the calculated stress values. Regarding the applications, the added value consists in the possibility to make choices based on simple and reliable analyses. If the results can be trusted, it allows then to build models and to correlate then with other set of data, or to define optimum deposition condition customized for a specific application. If the error bars are too large, researches must be reoriented to eventually obtain better results. The main gain is on the time spent working on erroneous models, which could conduct to dramatic and unjustified choices. This study will be done with different substrate and film configurations, but always in the theoretical validity range of the Stoney formula and under a constant deposition process (except time), i.e. with a unique origin of stress.

The Stoney formula is detailed in Eq. ( 1):

= E 6(1 ) h h 1 R 1 R s s s 2 f 0 (1)
where E s is the Young's modulus and ν s the Poisson's ratio of the substrate; h s and h f are, respectively, the substrate and the film thicknesses;

T R 0 and R are respectively the curvature radii of the substrate before and after the deposition process. Analyzing Eq. ( 1), it is clear that the main critical point here is the measurement of the curvatures R 0 and R and, consequently, the deformed shape related to the strain. Indeed, substrate properties are well known and the film thickness can be determined with a high precision by scanning electron microscopy. In this work, a particular attention will be given to the measured radii and the stress will be calculated in relation to different film (thickness) and substrate (thickness and crystallographic orientation) properties for constant process parameters. The TiN film, a classical and well known hard coating, was chosen only as example to illustrate the analyses presented in the study.

The thermal stress, given by Eq. ( 2) [START_REF] Dehm | In situ transmission electron microscopy study of thermal-stress-induced dislocations in a thin Cu film constrained by a Si substrate[END_REF], is supposed to be negligible in our conditions:

= E ( )(T T ) th f f s S M (2)
with E f the Young modulus of the film, α f and α s the film and the substrate thermal expansion coefficients, T S the substrate temperature during deposition, T M the temperature at which the thermal stress is determined, or at which the coating system operates in service. Nevertheless, a discussion about a possible thermal effect during deposition will be presented in Section 3.4.

Material and methods

TiN coatings were deposited by DC reactive magnetron sputtering on silicon substrates using a rectangular (12 × 40 cm x cm) titanium metallic target (purity 99.99%) in an Ar + N 2 gas mixture (40 and 90 sccm respectively). The discharge voltage was about -380 V with a current density of 29 mA.cm -1 . The working pressure was 0.65 Pa and the fixed target-to-substrate distance was 9.5 cm. In these conditions the growth rate of the TiN film was about 83 nm.min -1 . The deposition time was adjusted to obtain different TiN film thicknesses from 250 to 3400 nm. Before the deposition process, the silicon substrates are cut from a one (380 μm) or two (280 μm) side mirror-polished wafer, cleaned with ethanol in an ultrasonic bath, dried and kept at room temperature. All samples are extracted from the same wafer, with respect of the thickness and crystallographic orientation.

Table 1 summarizes the combination between the different substrate characteristics and the film thicknesses.

The surfaces and the cross-sections of the samples were analyzed using a JEOL JSM-7600F scanning electron microscope (SEM) equipped with a field emission gun (FEG). The film structure was determined by X-ray diffraction (XRD) using Co Kα (λ = 1.789 Å) radiation at fixed low incidence angle (5°).

For the calculation of the residual stress, the values used for the Young's modulus (E s ) and the Poisson's ratio (ν s ) of the substrate, which depend on its crystallographic direction, are listed in Table 2 [START_REF] Wortman | Young's modulus, shear modulus and Poisson's ratio in silicon and germanium[END_REF][START_REF] França | All-optical measurement of in-plane and out-of-plane Young's modulus and Poisson's ratio in silicon wafers by means of vibration modes[END_REF][START_REF] Hess | Laser diagnostics of mechanical and elastic properties of silicon and carbon films[END_REF].

The thickness of the substrates (h s ) was given by the suppliers and the thickness of each film (h f ) was measured by SEM. The surface curvature γ was measured by an optical profiler (WYKO NT1100) before and after TiN deposition, considering the whole surface of the sample. The curvature analysis was made by Gwyddion software [18], which allows determining the principal curvatures of the surface along the orthogonal directions. The principal curvatures in an Euclidian space correspond to the minimum and the maximum of the curvatures of a surface and are sufficient to describe a surface.

After surface measurements, two analyses are available:

• determining separately the initial and final surfaces characteristics:

principal directions (φ 0.1 , φ 0.2 and φ 1 , φ 2 ) and corresponding radii before and after coating (R 0.1 , R 0.2 and R 1 , R 2 respectively) and subtracting the inverse of the radii in the Stoney formula;

• subtracting the initial and final surfaces images and determining the characteristics of the equivalent surface corresponding to the deformed shape from a pure flat state (φ eq1 , φ eq2 and R eq1 , R eq2 ).

The second way is more relevant for the stress calculation and minimizes the measurement errors, due to the lower number of operations. Indeed, in the first way, the principal directions of curvature are in most of the case different before and after coating. There is here the question of the relevancy of subtracting two values (here the curvature) which are not applied at the same point nor within the same direction. It is possible that the error remains low, especially if the substrate presents a near-spherical curvature, but from a mechanical point of view it has no sense.

From the equivalent surface, two radii are extracted. If the fifth hypothesis of the Stoney theory is respected (i.e. spherical deformation) [START_REF] Ngo | Thin film/substrate systems featuring arbitrary film thickness and misfit strain distributions. Part I: analysis for obtaining film stress from non-local curvature information[END_REF], R eq1 , R eq2 are equal; this is mostly not the case, whatever the reason. Because the aim of this work is to obtain a global stress calculation and because the Stoney formula needs only one radius value, a choice has to be done by the user. Either the maximum radius, which will lead to the minimum stress, or the minimum radius, which will lead to the maximum stress, or, as last choice, the radius corresponding to the average curvature (Eq. ( 3)):

= + R R R R R
2 eq eq eq eq 1 2

1 2

(3)

The average radius

= + R R R 2 eq eq 1
2 is only a mathematical value that has no physical sense for the description of the surface curvature. In most of the cases, the error remains small (e.g. for a relative radius

error = 100% R Rmin , the relative function error R R R is about 12.5%),
but in order to minimize the stress error, only R should be used. The dispersion of the calculated stress values is determined with both experimental dispersion of the film thickness and radii using Eqs. ( 4) and [START_REF] Xi | Film thickness effect on texture and residual stress sign transition in sputtered TiN thin films[END_REF].

= E h R 6(1 ) 1 min s s s fmax eq max 2 (4) = E h R 6(1 ) 1 max s s s fmin eq min 2 (5)
The stress is therefore flanked by two minimal and maximal values, reflecting the precision of both thicknesses and radii measurements. This novel and simple method, directly derived from the experimental, gives a good indication of the relevancy of the calculated stress value. With this experimental error, the possible stress evolution with a given parameter can be investigated correctly.

Results and discussion

Structural characterizations of the coated samples

XRD analyses of the surfaces of the coated samples confirmed the formation of TiN with a (111) preferred orientation. A secondary and minor (202) orientation was also detected. For the thicker TiN film (3400 nm) the preferred orientation is still (111), but with traces of (311), independently of the thickness and the crystallographic orientation of the substrate.

SEM cross-section and surface morphologies of the 2200 (a), 1050 (b) and 475 (c) nm TiN coatings on 380 μm (001) Si substrate are shown in Fig. 1.

Cross-section observations show a columnar-type structure. For all thicknesses the coating is rather compact and homogeneous. The columns' head can be observed on SEM top view images (Fig. 1 Insets). In agreement with Abadias et al. [START_REF] Vaz | Residual stress states in sputtered Ti1-xSixNy films[END_REF], they are formed by pyramid-shaped (111) grains, whose size increases proportionally to the coating thickness. Following Chason et al. [START_REF] Boyd | situ curvature measurements of epitaxial YBCO films on MgO[END_REF], it means that the stress should decrease with the increase of the film thickness. This behavior is obviously not due to the film thickness, but to the growth mechanisms drove by the deposition conditions and the quantity of energy available per impinging atom.

Influence of the sample's shape and size

Fig. 2 shows the calculated residual stress versus the radius R va- lues for the 2200, 1050 and 475 nm films deposited on 380 μm (001) silicon substrates of different shapes and sizes.

The black lines correspond to the Stoney equation function of the radius for a given film thickness. Triangle marks with the tip up correspond to the maximum stress value calculated from the overall R eqmin , while triangle marks with the tip down is the minimum value calculated from the overall R eqmax . Error bars represents here the influence of the thickness variation applied to Stoney formula with R . Due to the curve progression of the Stoney equation the values of the error bars increase with a decreasing radius. The stress domain defined by σ min and σ max is rather constant and comprised between 75 MPa and 225 MPa and thus whatever the thickness the films. In contrary, considering only the stress calculated from R , the results show a in- fluence of the film thickness: the thicker the film is, the lowest the dispersion of the calculated stress for the different samples is. For a thickness of 2200 nm, the average stress is about 137 MPa with a standard deviation of 4 MPa, for a thickness of 1050 nm, the average stress is about 131 MPa with a standard deviation of 18 MPa and for a thickness of 475 nm the average stress is about 158 MPa with a standard deviation of 25 MPa. If the stress seems to increase with the decrease of the film thickness, this conclusion is weakened by the increase of the dispersion. Considering the size and shape of the samples, no general trends could be reported for the stress evolution in comparison to the film thickness evolution: decrease for the 10 × 10, increase for 15 × 15 and 10 × 20, decrease and then increase for 10 × 13 and 10 × 15. To conclude on this influence, the dispersion in the radii measured for all sample is examined. Hence, Fig. 3 represents the coefficient of variation between R eq1 and R eq2, averages for the three coating thicknesses.

A shape effect can be clearly observed: when the shape of the sample approaches to a strip (corresponding to the 10 × 20 mm x mm sample in our case) the deformation after coating tends to become more spherical, satisfying Stoney's hypothesis. This behaviour is in agreement with the FEM results of Guyot et al. [START_REF] Guyot | The role of the sample shape and size on the internal stress induced curvature of thin-film substrate systems[END_REF]. These results were presented in a different way in a previous study [START_REF] Ardigo | Stoney formula: investigation of curvature measurements by optical profilometer[END_REF], and the experiments were reproduced by Shiri et al. [START_REF] Shiri | Evaluation of Stoney equation for determining the internal stress of DLC thin films using an optical profiler[END_REF] in the case of DLC films, adding roundshape substrates. In opposition to the present results, they found for all substrate shapes a constant stress value of 760 MPa ± 100 MPa, about 5 times higher than in our study. Higher stress implies more deformation and consequently a smaller radius and a higher sagitta. Whatever the substrate size, the curvature measurement can be performed with a sufficient accuracy. Moreover, due to the hyperbolic shape of the Stoney equation (Fig. 2), for high stress level the possible radius range is shorter, i.e. the deformation becomes spherical. It means that, whatever the substrate shape, its influence will be small. In summary, more the film is stressed, more the substrates shape and size can be neglected. If the stress level is previously known (and sufficiently high), any substrate shape could be used. In a prospective study, the user should prefer the greater strip-like substrate as possible. In general, small substrates with arbitrary shapes (e.g. sample extracted from the wafer sides) must be avoided. Finally, it is also obvious, with the dispersion reported here, that the substrate dimensions are given at the half millimeter, or the millimeter. What is important is working with a large enough internal surface and not specific and precise external dimensions.

Influence of the coating thickness

Taking into account the previously presented results, we chose to work only with 10 × 20 mm 2 samples, for the further analyses. In a first time, we focused on the 380 μm (001) Si substrate, one side mirrorpolished. Several TiN thicknesses were considered: the first set with 2200, 1050 and 475 nm and an additional set with 3400, 430 and 250 nm. Fig. 4 shows the calculated residual stress versus the radius R for the different coating thicknesses.

As previously observed for different sample geometries and in agreements with the grain size evolution (Fig. 1), the stress tends to increase when the film thickness decreases. Moreover, the error concerning the residual stress values calculated becomes more important for the thinnest coatings. The dispersion values concerning the 250 nm TiN thickness are not reported on the graph. Indeed, for this thickness, a near saddle-like surface is observed (R 1 = 30 m and R 2 = -280 m) and leads to a variation in stress of ± 0.321 GPa. It implies at the same time high tensile stress (0.724 GPa) and low compression stress (-0.078 GPa) in the film. But, it as to be noted that the R 2 value is really high, and consequently not really relevant from an experimental point of view, due to the high measurement uncertainty, and in particular the sign of the radius.

It can be concluded that not only the shape of the sample, but also the film thickness is a critical parameter for the stress calculation, and this, without any consideration about the real stress state. They both have to be taken into account carefully when the Stoney formula is used, to ensure the relevance of the studies.

Even if a stress value can be calculated (and it is always possible to enter data in the Stoney formula) the results can be meaningless. Following the theoretical stress curves (Fig. 4), it is possible to determine two experimental problematic cases (values are given as order of magnitude, not as precise points):

• low thicknesses (< 250 nm) and high radii (> 100 m), • high thicknesses (> 2 μm) and small radii (< 5 m).

In this both cases, the relative errors (in thickness and radii) can be important, leading to a high relative stress error. Because, for a given film synthesized during a given process, the stress (and consequently the value of the measured radius) could not be predicted, the only way to control the relevancy of the stress calculation is the use of Eqs. ( 4) and [START_REF] Xi | Film thickness effect on texture and residual stress sign transition in sputtered TiN thin films[END_REF].

Without this precaution, and depending of the choice (user dependent) of the radius value (minimal, maximum, average radius or radius of the average curvature), all behaviors could be found for the influence of thickness on the stress: from no influence with a constant stress value around 150 MPa to strong influence with an increase of the stress from 50 MPa to > 500 MPa with the decrease of the thickness and any other intermediate behavior. This problem is present for this kind of global analyses, where the stress is calculated ex-situ, but not for in-situ measurements, where the evolution of the deformation is measured. As a consequence, in ex-situ measurements, any stress evolution must be completed by several other parameters to reach a trend of behaviors and conclude.

Influence of the temperature during the process

The previous results, according to a common analysis in the literature, tend to show an increase of the stress with the decrease of the film thickness. But because thickness is, like the stress, a consequence of the film growth in relation with the deposition parameters, an examination of the substrate temperature was conducted during dedicated deposition, keeping constant other process parameters: pressure, voltage, current density and consequently deposition speed. For the 3 thinnest films, a strong temperature increase is noted from ambient to 200 °C, corresponding to the transient mode of the heat transfer in the sputter system (energetic particles and radiation from the plasma as heat source, cooling system of the chamber as limit conditions and the substrate holder as exchange system). For longer deposition times, the system enters in the steady-state mode with a progressive stabilization of the temperature near 300 °C. Shaginyan et al. [START_REF] Shaginyan | Evolution of film temperature during magnetron sputtering[END_REF] report similar substrate temperature evolution and introduce the Hot Thin Surface Layer, which can be more relevant in term of microstructural evolution (and their consequence on the stress) than the thermal stress.

Compared to the substrate temperature, the stress presents an obvious linear decrease with a chi-square of 0.9. This result seems to imply that the thermal conditions are responsible of the stress in the films at different levels. Firstly, the increase of the substrate temperature will increase the adatoms mobility and thus reinforce the increase of the grain size with the increase of the film thickness. Secondly, the duration at high temperature (deposition time and natural cooling down before the chamber opening) could have act as an annealing with stress relieving. Thirdly, the thermal stress should probably not be neglected as supposed in the introduction. But, the main limit to determine the thermal stress is the need of the film properties: Young modulus and thermal expansion coefficient. Without experimental measurements of these properties of the films, data of the literature could be used, even if the large dispersion of these values doesn't really allow this calculation. As an example, Table 3 presents the range of possible thermal stress with the extreme values given by the literature. The substrate thermal expansion coefficient used in the calculation is 3.6 10 -6 K -1 [START_REF] Dehm | In situ transmission electron microscopy study of thermal-stress-induced dislocations in a thin Cu film constrained by a Si substrate[END_REF]. The limit temperatures used in the calculations are 67 and 290 °C.

It is nearly impossible to conclude from the data reported in Table 3. Indeed, following Vaz [START_REF] Vaz | Residual stress states in sputtered Ti1-xSixNy films[END_REF] for the thermal expansion coefficient, the thermal stress is compressive, where with Pierson [START_REF] Pierson | Handbook of Refractory Carbides and Nitrides[END_REF] it is tensile. In the first case, the intrinsic stress tends to decrease less with the temperature, tending to a constant intrinsic stress state. In the second case, the intrinsic stress is tensile for small thicknesses and becomes compressive for higher thicknesses. The choice of the Young Modulus only changes the magnitude of the calculated thermal stress. However, one should note that the values given by Pierson are those of the bulk TiN and thin films properties are commonly known to present lower values. For this reason, the values given by Vaz, with a film thermal expansion coefficient of 2.9 10 -6 K -1 and a Young modulus between 100 and 200 GPa are chosen in the present study. Nevertheless, taking into account this thermal stress in the previous calculations, even with the highest Young modulus values, only lowers the slope of the graph (Fig. 5) and doesn't change the previous conclusions. The resulting increase in the intrinsic stress remains, in all cases, smaller than the error bars.

Influence of the substrate thickness and orientation

Because a silicon wafer doesn't present isotropic characteristics, the crystallographic orientation should have an influence on the strain and consequently on the calculated stress [START_REF] Chason | Tutorial: understanding residual stress in polycrystalline thin films through real-time measurements and physical models[END_REF]. In the previous parts of this paper, we used classical (100) silicon substrate; in this part we added the (111) orientation, more isotropic. The supplementary changes, i.e. substrate thickness and number of polished sides, are due to the supplier. The surface finishing of the back side of the wafer should not have an influence. Or if it has, a serious reassessment of all the literature using the curvature measurements must be done. The substrate thickness is not a problem because it is taken into account in the formula. Fig. 6 shows the evolution of the R values (a) and the residual stress calculated (b) in function of the thickness and the crystallographic orientation of Si substrates having the same shape and size (10 × 20 mm 2 ) for two film thicknesses.

For a thick coating (3400 nm), a decreasing of the substrate thickness is accompanied by a diminishing of the R value; no influence of the crystallographic orientation of the substrate can be observed (Fig. 6(a)), in the case of equivalent substrates. When the residual stress is considered (Fig. 6(b)), no significant differences between the three silicon substrates can be observed. This proves the relevancy of the Stoney formula: a given deposition process produces films with a constant stress level. For a constant crystallographic orientation, a change in the substrate thickness is balance by a change in the curvature. For a constant substrate thickness, the small increase in the biaxial modulus is also balanced by a small increase of the curvature.

In the case of the 430 nm TiN coating thickness, a decrease in the R value with the decreasing of the substrate thickness can be observed the Si crystallographic orientation is the same (001) (Fig. 6(a)). For the 280 μm (111) Si substrate R value is the same than that Table 3 Thermal stress (MPa) for different TiN Young modulus and thermal expansion coefficients at the temperatures of 67 and 290 °C.

Thermal expansion coefficient (10 -6 K -1 ) 2.9 [START_REF] Vaz | Residual stress states in sputtered Ti1-xSixNy films[END_REF] 9.4 [START_REF] Pierson | Handbook of Refractory Carbides and Nitrides[END_REF] Young modulus E f (GPa) 80 [START_REF] Vaz | Residual stress states in sputtered Ti1-xSixNy films[END_REF] σ th = -3.1 to -19.8 MPa σ th = 25.9 to 163.8 MPa 250 [START_REF] Pierson | Handbook of Refractory Carbides and Nitrides[END_REF] σ th = -9. measured for the 380 μm (100) Si substrate, higher than the same thickness substrate, in opposition with the previous case. Concerning the residual stress (Fig. 6(b)) the calculated value does not vary for the substrates having the same crystallographic orientation, but decreases for the 280 μm (111) Si substrate. For (100) Si substrate, this behavior is consistent with the previous analyses: the change in substrate thickness is balanced by the change in curvature. And once again, the stress dispersion increases with the decrease of the film thickness. For the (111) Si substrate, the stress appears to decrease compared to the (100) Si substrate values. If this trend is relevant, which is not sure in regards to the overlapping of the error bars, it could confirms the influence of the crystallographic orientation on the strain. There are not enough results here to conclude definitely. The question is more: could the influence of the crystallographic orientation be measured with relevancy, rather than: does the crystallographic orientation have an influence? Indeed, following the mechanic properties at an atomic scale, it should theoretically be. In all cases, the influence seems not to be obviously predominant.

Conclusions

In this study the influence of several parameters on the curvature radius measurement and consequently on the calculated residual stress of TiN-coated silicon was evaluated. First of all, we gave an efficient and simple method to estimate the experimental relevancy of the calculated residual stress. The dispersion took into account the two main error sources: the film thickness measurement and the curvature radii measurement. With the help of this method, some samples characteristics were investigated. The aim of the present work was not to elaborate behaviors models, that can be find elsewhere in a more theoretical way, but to test the sensibility of the curvature method for the residual stress determination to these experimental parameters.

In the case of medium stress state (lower than 300 MPa), we put in evidence that the shape and the size of the substrate have an influence on the curvature radius and the calculated stress. Indeed, more the sample is strip-like and big enough; more the deformation after coating tends to become spherical, in agreement with the available literature data and with Stoney's hypothesis. In the case of higher stress, the size and shape of the substrate do not really matter.

Secondly, it was shown that, for the same sample geometry, the curvature radius value increases when the thickness of the coating decreases and that, in the same time, the dispersion of the values increases too, making sometimes difficult the interpretation of the results. The increase of the stress with a decreasing film thickness has three origins: a limit in the accuracy of the measurement of large radii, a thermal effect for high thicknesses with stress relieving and grain widening, and, in a small part, a contribution of the thermal stress. For this range of thicknesses, far from the initial growth stages, the thickness has no influence on the calculated stress. It is perfectly predicted by the theory on stress generation in thin film, but could not be the conclusion, if no precautions are taken for the analysis.

Finally, the influence of the thickness and the crystallographic orientation of the substrate has been investigated. The results shown that, in function of the coating thickness, two situations are possible: for thick coatings, or high stress, the strain is high enough, and the influence of the wafer crystallographic orientation is not visible. For thin coatings, even if the stress dispersion is higher, the substrate orientation seems to have a slight influence on the strain. As expected, the substrate thickness has no influence and is balanced by the change in radius.

Fig. 1 .Fig. 2 .Fig. 3 .

 123 Fig. 1. Cross section and surface morphologies of the TiN films on (001) silicon substrates: (a) 2200, (b) 1050 and (c) 475 nm coating thicknesses.

Fig. 4 .

 4 Fig. 4. Calculated residual stress for different film thicknesses deposited on 10 × 20 mm 2 380 μm (001) Si substrates.

Fig. 5 .Fig. 6 .

 56 Fig. 5. Calculated residual stress for the different film thicknesses compared to the substrate temperature.

Table 1

 1 Samples parameters.

	Substrate			Film
	Shape	Size (mm × mm)	Thickness and orientation	Thickness (nm)
	Square	10 × 10	380 μm (001)	475, 1050, 2200
		15 × 15
	Rectangle	10 × 13
		10 × 15
		10 × 20	280 μm (001)	250, 430, 3400
			280 μm (111)

Table 2

 2 Silicon Young's modulus (E s ) and Poisson's ratio (ν s ) for two crystallographic orientations.

	Crystallographic orientation	Young's modulus (GPa)	Poisson's ratio
	(001)	130	0.28
	(111)	160	0.27


Clément Dalloux

Vincent Claveau

Natalia Grabar
email: natalia.grabar@univ-lille.fr

Speculation and negation detection in french biomedical corpora

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

INTRODUCTION

The detection of speculation and negation in texts has become one of the unavoidable pre-requisites in many information extraction tasks. Both are common in language and provide information on factuality and polarity of facts, which is particularly important for the biomedical field (Elkin et al., 2005b;[START_REF] Denny | Identifying qt prolongation from ecg impressions using natural language processing and negation detection[END_REF][START_REF] Gindl | Syntactical negation detection in clinical practice guidelines[END_REF][START_REF] Chapman | A simple algorithm for identifying negated findings and diseases in discharge summaries[END_REF]. Indeed, negation and speculation provide there crucial information for detecting patient's present, speculated or absent pathologies and co-morbidities, detecting whether a particular medication has been, may have been, or has not been prescribed or taken, defining the certainty of diagnosis, etc. In order to efficiently identify speculation and negation instances, it is first necessary to identify their cues, i.e., words (or morphological units) that express speculation and negation, and then their scopes, i.e., tokens within the sentence which are affected by the negation or speculation. In this paper, we present two French datasets annotated with negation and speculation cues and their scope. We also propose machine learning and deep learning based systems to tackle the automatic detection of cues and scopes that achieve high performance.

EXPRESSION of NEGATION and SPECULATION

In French, the expression of negation and speculation have some specifics that are described below.

NEGATION

The negation cues may consist of one word or prefix, or of multiple words. Moreover, negation can be expressed via a large panel of cues which can be morphological (an, in, im, ir, dis), lexical (absence de(absence of), à l'exception de(excepting)), and grammatical (non, ne...pas, ni...ni). In the following examples, we present sentences with instances of negation (the cues are underlined).

1. En alternative des traitements locaux (chirurgie, radiothérapie, radiofréquence, cryoablation) peuvent être indiqués mais ils ne sont pas toujours faisables. (Alternatively, local treatments (surgery, radiotherapy, radiofrequency, cryoablation) may be indicated but are not always feasible.)

2. Il n'existe toujours pas aujourd'hui de consensus quant à une définition précise de ce phénomène hétérogène ou des modalités de sa prise en charge. (There is still no consensus today on a precise definition of this heterogeneous phenomenon or the modalities of its management.)

3. il n'y a pas de traitement curateur de la maladie en dehors de l'allogreffe de moelle.

(there is no curative treatment for this disease apart from bone-marrow homograft) Examples (1-2) show the possible effect of frequency adverbs, here toujours (always). In the first sentence traitements locaux (chirurgie, radiothérapie, radiofréquence, cryoablation), the content would be negated without toujours(always). In the second sentence, with or without toujours, the meaning of the sentence does not change, therefore, the scope of the negation remains the same.

Example (3) shows how the preposition en dehors de(apart from), can stop the scope of negation. Many other prepositions such as à part, à l'exception de or excepté, with more or less the same meaning than en dehors de (apart from), would have the same effect on the negation scope. However, these prepositions can also play the role of negation by themselves.

Examples (4) show that cues can also be included in medical concepts such as non hodgkinien (non-Hodgkin's). In our work, we chose to label hodgkinien as part of the negation scope.

Finally, example (5) shows the context in which the ambiguous word pas, meaning both no/not and footstep, has the non-negation meaning. In this example, pas is part of the idiomatic expression faire les cent pas (pacing, walking around). Another ambiguity is related to the adverb plus meaning either more or, in conjunction with ne, no more.

SPECULATION

The expression of speculation can be even more complex than negation. Indeed, speculation can be triggered by many specific sequences of words. We describe several of them below.

1. En effet, l'arrêt du traitement antituberculeux en soi pourrait permettre un rétablissement de la fonction normale des héptocytes en éliminant la source de l'atteinte hépatique. Example (1) shows a typical occurrence of pourrait (could). As in English, where can, could, may, etc. express speculation, in French, pouvoir can occur in many forms.

Example (2) shows the effect of reste à (remains to) combined with an infinitive verb, here démontrer. Other infinitive verbs, such as expliquer (to explain) or vérifier (to verify), associated with reste à trigger speculation.

Example (3) shows how the conditional tense triggers speculation. In English, would triggers speculation in this case, which is simpler to detect. Indeed, in French, the conditional tense is expressed via suffixes (-ais, -ais, -ait, -ions, -iez, -aient), which makes the detection harder, especially for supervised learning techniques.

RELATED WORK

We present several corpora and methods that have been proposed in the existing work to tackle the tasks of speculation and negation detection.

DATA

In the recent years, several specialized corpora in English have been annotated with speculation and negation, which has resulted in models for their automatic detection. These corpora can be divided into two categories: (1) corpora annotated with cues and scopes, such as Bioscope [START_REF] Vincze | The bioscope corpus: biomedical texts annotated for uncertainty, negation and their scopes[END_REF] or *SEM-2012, and (2) corpora focusing on concepts and named entities, such as I2B2 and Mipacq. We briefly describe these corpora. The Bioscope corpus [START_REF] Vincze | The bioscope corpus: biomedical texts annotated for uncertainty, negation and their scopes[END_REF] contains reports of radiological examinations, scientific articles, and abstracts from biomedical articles. Each sentence and each negation and speculation cue/scope pair receives unique identifier. The *SEM-2012 corpus [START_REF] Morante | sem 2012 shared task: Resolving the scope and focus of negation[END_REF] consists of a Sherlock Holmes novel and three other short stories written by Sir Arthur Conan Doyle. It contains 5,520 sentences, among which 1,227 sentences are negated. Each occurrence of the negation, the cue and its scope are annotated, as well as the focus of the negation if relevant. In this corpus, cues and scopes can be discontinuous. The I2B2/VA-2010 challenge [START_REF] Uzuner | 2010 i2b2/va challenge on concepts, assertions, and relations in clinical text[END_REF] featured several tasks using US clinical records. One task aimed the detection of statements and of their scope. Medical concepts had to be associated with the corresponding statement: present, absent, possible, conditional, hypothetical or not associated with the patient. Mipacq [START_REF] Albright | Towards comprehensive syntactic and semantic annotations of the clinical narrative[END_REF] is another corpus with clinical data in English annotated with syntactic and semantic labels. Each detected UMLS entity has two attribute locations: negation (true or false) and status (none, possible, HistoryOf or FamilyHistoryOf ).

EXPERT SYSTEMS

Among the rule based systems dedicated to the negation detection, NegEx [START_REF] Chapman | A simple algorithm for identifying negated findings and diseases in discharge summaries[END_REF] pioneered the area. It uses regular expressions to detect the cues and to identify medical terms in their scope. It was later adapted to various languages including French [START_REF] Deléger | Detecting negation of medical problems in french clinical notes[END_REF]. ConText [START_REF] Harkema | Context: an algorithm for determining negation, experiencer, and temporal status from clinical reports[END_REF], derived from NegEx, covers more objectives: negation, temporality, and the subject concerned by this information in the clinical texts. It has been adapted to French [START_REF] Abdaoui | French context: Détecter la négation, la temporalité et le sujet dans les textes cliniques franc ¸ais[END_REF]. In another work, medical concepts may receive additional labels (positive, negative or uncertain) Elkin et al. (2005a). [START_REF] Özgür | Detecting speculations and their scopes in scientific text[END_REF]; Øvrelid et al. (2010); [START_REF] Kilicoglu | A high-precision approach to detecting hedges and their scopes[END_REF] exploit lexical, grammatical and syntactic information to detect speculation and its scope. ScopeFinder [START_REF] Apostolova | Automatic extraction of lexicosyntactic patterns for detection of negation and speculation scopes[END_REF] detects the scope of negation and speculation with rules built automatically from BioScope (lexico-syntactic patterns extraction). NegBio [START_REF] Peng | Negbio: a high-performance tool for negation and uncertainty detection in radiology reports[END_REF] detects both negation and speculation in radiology reports with rules based on universal dependency graphs.

SUPERVISED LEARNING

To our knowledge, [START_REF] Light | The language of bioscience: Facts, speculations, and statements in between[END_REF] is the first work to include supervised learning for speculation detection. It relies on SVM to select speculative sentences in MEDLINE abstracts. [START_REF] Tang | A cascade method for detecting hedges and their scope in natural language text[END_REF] proposes a cascade method based on CRF and SVM classifiers to detect speculation cues and another CRF classifier to identify their scopes. [START_REF] Velldal | Speculation and negation: Rules, rankers, and the role of syntax[END_REF] proposes a SVM-based cue detection system, trained on simple n-grams features computed on the local lexical context (words and lemmas). This system offers a hybrid detection of the scope, which combines expert rules, operating on syntactic dependency trees, with a ranking SVM that learns a discriminative ranking function over nodes in constituent trees. It was further improved by [START_REF] Read | Uio 1: Constituent-based discriminative ranking for negation resolution[END_REF]

FRENCH MEDICAL CORPORA

We manually annotated two corpora from the biomedical field. Table 1 presents some statistics on these corpora: the number of words, the variety of the vocabulary, the number of sentences, the number of negative sentences with one or more negations. The Inter Annotator Agreement (IAA) on negation annotation is high (Cohen's κ=0.8461).

ESSAI: FRENCH CORPUS with CLINICAL TRIALS

One corpus contains clinical trial protocols in French. They were mainly obtained from the National Cancer Institute registry1 . The typical protocol consists of two parts: the summary of the trial, which indicates the purpose of the trial and the methods applied; and a detailed description of the trial with the inclusion and exclusion criteria. ). In clinical cases, the negation is frequently used for describing the patient signs, symptoms, and diagnosis. Speculation is present as well but less frequently.

ANNOTATION LAYERS

These two corpora are Part-of-Speech tagged and lemmatized with TreeTagger [START_REF] Schmid | Probabilistic part-ofispeech tagging using decision trees[END_REF]. For the creation of the reference data necessary for machine learning, both corpora were annotated manually to mark up negation and speculation cues and their scope. However, the 200 annotated clinical cases did not include enough examples of speculation for a machine learning models to train properly. Therefore, speculation detection is either trained and tested with ESSAI alone or with ES-SAI and CAS. Table 2 presents an annotated sentence from CAS: No dyspnea.. The corpora also includes two additional columns (sentence number and token position).

METHODOLOGY

As indicated on Figure 1, our methods rely on specifically trained word vectors and supervised learning techniques (BiLSTM and CRF). The objective is to classify each word as being part or not of the negation/speculation cue and/or scope.

WORD VECTOR REPRESENTATIONS

In the recent years, several models have been introduced to generate vector representations of words helping machine learning approaches to better capture their semantics. The models used in the negation/speculation detection task are the following ones. word2vec [START_REF] Mikolov | Distributed representations of words and phrases and their compositionality[END_REF] is a predictive model to learn word embeddings from plain text. The embeddings can be calculated using two model architectures: the continuous bag-of-words (CBOW) and Skip-Gram (SG) models. In this work, we use use the SG model; it treats each context-target pair as new observation, which is suitable for large datasets.

fastText [START_REF] Bojanowski | Enriching word vectors with subword information[END_REF] addresses the Word2vec's main issue: the words, which do not occur in the vocabulary, cannot be represented. Hence, this algorithm uses subword information: each word is represented as a bag of all possible character n-grams it contains. The word is padded using a set of unique symbols which helps singling out prefixes and suffixes. The full sequence is added to the bag of n-grams as well. The vector now denotes every char n-gram and the word vector is the sum of its char n-gram vectors. Since the char n-gram representations across words are often shared, rare words can also get reliable representations.

These two word embedding models are trained using the Skip-Gram algorithm, 100 dimensions, a window of 5 words before and after each word, a minimum count of five occurrences for each word and negative sampling. The training data are composed of the French Wikipedia articles and biomedical data. The latter includes the ES-SAI and CAS corpora, the French Medical Corpus from CRTT2 and the Corpus QUAERO Médical du franc ¸ais3 [START_REF] Névéol | The quaero french medical corpus: A ressource for medical entity recognition and normalization[END_REF]. These models are trained using the Gensim4 (Rehurek and Sojka, 2010) python library.

RECURRENT NEURAL NETWORK

Recurrent neural network takes into account the previously seen data in addition to the currently seen data. This is implemented with loops in the architecture of the network, which allows the information to persist in memory. Among the RNNs, long short-term memory networks (LSTM) [START_REF] Hochreiter | Long short-term memory[END_REF] are the most efficient for the learning of long-term dependencies and are therefore more suitable to solve the problem of discontinuous scope, which is typical for the negation. LSTM cells are also more efficient at retaining useful information during backpropagation.

We use a bidirectional LSTM, which operates forward and backward on the sentence, to detect cues and scopes. The backward pass is relevant for the scope detection because the scope may be before or after the cue. Prediction is computed by either a softmax or a CRF (suitable for the sequence labeling) output layer. We use embeddings of dimension k = 100 and a dimensionality of the output space of 400 units per layer (backward/forward) with 0.5 dropout. 50 epochs achieve the highest F 1 score on the validation sets.

CONDITIONAL RANDOM FIELDS

Conditional random fields (CRFs) [START_REF] Lafferty | Conditional random fields: Probabilistic models for segmenting and labeling sequence data[END_REF] are statistical methods used to label word sequences. By training a model on appropriate features and labels to be predicted, the CRFs generally obtain good results with much lower training time than neural networks.

We performed the gradient descent using the L-BFGS (Limited-memory BFGS) method with 0.1 L1 penalty and 0.01 L2 penalty. We only experiment with CRFs for the cue detection task, in comparison with BiLSTM-CRF.

EVALUATING LABELING SYSTEMS

We use standard evaluation measures: precision P , recall R, and F 1 score. The scope detection is evaluated in two ways: (1) on individual scope tokens which is the standard evaluation, and (2) on exact scopes to assess more strictly how efficient our models are. For the latter, we use the available evaluation script5 . Each corpus is randomly segmented into the training set (80%, 20% for validation), and the test set (20%).

CUE DETECTION

The speculation and negation cue detection is the first step of the task. To tackle this problem, we experiment with two supervised learning approaches. First, we train a CRF using several features (words, lemmas and POS-tags) with empirically defined window over features. Our second approach uses a BiLSTM with a CRF output layer, which is trained on the same features. We did not use any pre-trained embeddings for this task.

Table 3 presents the results obtained with our approaches on the ESSAI and CAS corpora. We can see that cue detection shows high evaluation values: 93.92 to 97.21 F-measure for negation cues, and 86.88 to 91.30 F-measure for speculation cues. Although there is little room for improvement on negation cue detection, indeed 10kfold cross-validation with our CRF reaches more than 95 F-measure on both corpora, speculation cue detection would benefit from more training examples. Indeed, the potential number of cues and the numerous contexts in which they appear and do or do not express speculation makes them harder to detect and will require more annotated examples. For both negation and speculation cue detection, our CRF is slightly more efficient than the BiLSTM-CRF when the CAS corpus is involved, which indicates that the CAS corpus contains less complex examples than ESSAI.

SCOPE DETECTION

In all the scope detection experiments proposed, we only train the neural networks on nega- tive/speculative sentences. The base system takes as input an instance I(w, c, t), where each word is represented by: w vector (word-embedding), c vector (cue-embedding), indicating if the word is a cue or not, t vector (POS-tag-embedding). Preliminary tests showed that adding lemmas as features only decreases the F-measure. For each system, we use the same empirically defined hyperparameters given before. During training, embeddings weights are updated.

Table 4 indicate the results obtained for the scope detection task. One can see that it is easier to predict the scope of negation cues (up to 90.81 F-measure) than of speculation cues (up to 86.73 F-measure). Results show that using pre-trained embeddings improves F-measures for exact scope detection by up to 6 points for both negation and speculation. Moreover, the CRF output layer either outperforms the softmax layer or reaches an equivalent F-measure for exact scope detection. In another experiment, we trained the models on one corpus and tested them on the other (Table 5): the models trained on ESSAI are more efficient, and the negation and speculation structures are more stable in the CAS corpus. However, even though CAS (speculation) was only trained on 226 examples, the model still shows decent results in scope tokens detection.

ERROR ANALYSIS

An analysis of the results makes it possible to isolate frequent types of errors. In the following examples, the speculation and negation cues are underlined, the scope is between brackets, while the segments in bold correspond to predictions errors.

NEGATION

In the first example, the prediction fails at labeling rénale (renal). In the majority of cases in the reference data, the scopes associated to the cue sans often only include one token, which may be causing this error that impacts recall:

• GOLD: Le patient sortira du service de réanimation guéri et sans [insuffisance rénale] après huit jours de prise en charge et cinq séances d'hémodialyse.

• PRED: Le patient sortira du service de réanimation guéri et sans [insuffisance] rénale après huit jours de prise en charge et cinq séances d'hémodialyse.

(The patient will be discharged from the intensive care unit without renal failure after eight days of management and five hemodialysis sessions.)

The second example illustrates the error that impacts precision. Here, the model wrongly predicts that all tokens in the sentence are within the scope. In the reference data, the cue aucun (any, no) often occurs at the beginning of sentences, and in sentences with many instances of negation. The model, mostly trained on this kind of examples, may try to reproduce these structures which causes bad prediction in some cases. (Special stains (PAS, Ziehl-Neelsen stain, Grocott stain) showed no pathogens.)

In the third example, the error impacts both precision and recall. In this example, we have two instances of negation with the same cues: n...pas. Usually, its scope follows, however, in the first instance it precedes. As we do not have many examples of this kind to train on, the model fails to correctly label the sequence. In the second negation instance, the scope may be shorter than usual, which impacts precision. (The removal of the implicated osteosynthesis material is not systematic, which explains why it has not been proposed to our asymptomatic patient.)

SPECULATION

In the first example, the scope of the speculation has been predicted up to the end of the preposition while in the reference data, the scope covers the verbal group. This typically impacts precision. However, most of our errors impact recall, like in the following example.

• GOLD: Elles possèdent les caractéristiques de la tumeur et pourraient [permettre à l'avenir de faire le diagnostic de tumeur sans biopsie ainsi que de suivre l'évolution de la tumeur traitée], les CTC disparaissant quand le traitement fonctionne.

• PRED: Elles possèdent les caractéristiques de la tumeur et pourraient [permettre à l'avenir de faire le diagnostic de tumeur] sans biopsie ainsi que de suivre l'évolution de la tumeur traitée, les CTC disparaissant quand le traitement fonctionne. Several examples show errors where both precision and recall are impacted. Usually, the reason is that they are rare cases in our corpora where the scope is reversed. For instance, in the example below, most of the scope of devrait precedes it, while in the reference data, its scope follows this cue.

• GOLD: L'objectif de cet essai est d'évaluer [la diminution des complications postopératoires de l'oesophagectomie qui] devrait [être obtenue] en réalisant une partie de l'intervention sous coelioscopie, chez des patients ayant un cancer de l'oesophage résécable.

• PRED: L'objectif de cet essai est d'évaluer la diminution des complications postopératoires de l'oesophagectomie qui devrait [être obtenue en réalisant une partie de l'intervention sous coelioscopie], chez des patients ayant un cancer de l'oesophage résécable.

(The objective of this trial is to evaluate the decrease in postoperative complications of esophagectomy that should be achieved by performing part of the procedure under laparoscopy, for patients with resectable esophageal cancer.)

CONCLUSION and FUTURE WORK

The interest for the automatic detection of speculation and negation in English with supervised machine learning has increased in the recent years.

Yet, the lack of data for other languages and for specialized domains hampers the further development of such approaches. In our work, we presented new bodies of biomedical data in French annotated with negation and speculation (cues and their scope). Prior to the dissemination to the research community, the French clinical trial protocols corpus will be finalized through the integration of new data and the computation of the inter-annotator agreement. The French CAS corpus will be distributed as more clinical cases are manually annotated, as we need more speculative sentences to train supervised learning models on this dataset. Another contribution of our work is the study of different types of word vector representations and recurrent neural networks for the detection of negation and speculation. There has not been much work of this type on French corpora, especially for the biomedical domain which contains specific negation and speculation phenomena. We showed that a CRF layer yields better performance than softmax on exact scope match. Finally, the models have been applied in a cross-corpus context. Besides, we plan to improve our neural network performance by providing richer feature. In particular, recent embedding techniques, such as BERT or ELMO [START_REF] Devlin | Bert: Pre-training of deep bidirectional transformers for language understanding[END_REF][START_REF] Matthew E Peters | Deep contextualized word representations[END_REF] may provide more accurate representation of the sentences. Moreover, in order to provide more accurate features, we plan to move from TreeTagger, which makes a substantial number of mistakes on our datasets, to a POS tagger/lemmatizer dedicated to French biomedical texts. Syntactic parsing of sentences may also provide useful features for the detection of scope.

(

  Indeed, stopping TB treatment per se could restore normal hepatocyte function by eliminating the source of liver damage.) 2. Le bénéfice de l'association lénalomide + R-CHOP au rapport par rapport au R-CHOP reste à démontrer. (The benefit of the combination of lenalomide + R-CHOP compared with R-CHOP remains to be demonstrated.) 3. Elle aurait eu la pose à 2 reprises d'un dispositif intrautérin (DIU). (She would have had the pose of an intrauterine device (IUD) twice.)

Figure 1 :

 1 Figure 1: Our bidirectional RNN uses LSTM cells with either a softmax or CRF output layer. Features are either words/lemmas/PoS for cue detection or words/PoS/Cue information for scope detection.

•

  GOLD: Les colorations spéciales (PAS, coloration de Ziehl-Neelsen, coloration de Grocott) ne [mettaient en évidence] aucun [agent pathogène]. • PRED: [Les colorations spéciales (PAS, coloration de Ziehl-Neelsen, coloration de Grocott]) ne [mettaient en évidence] aucun [agent pathogène].

(

  It has the characteristics of the tumor and could in the future be used to diagnose the tumor without biopsy and to follow-up the evolution of the treated tumor, the CTC disappearing when the treatment is efficient.)

  Elle n'est soulagée que par la marche et doit donc écouter la télévision en faisant les cent pas dans son salon. (She is only relieved by walking and must therefore listen to the TV pacing in her living room.)

4. Lymphome non hodgkinien à cellules B ma-

tures récidivant/réfractaire. (Relapsed/refractory mature B-cell non-Hodgkin's lymphoma.)

5.

Table 1 :

 1 and is used Statistics on the two French corpora as a fall-back by Packard et al. (2014) when the main MRS (minimal recursion semantics) Crawler cannot parse the sentence. Qian et al. (2016) addresses the scope detection with an approach based on a convolutional neural network which extracts features from various syntactic paths between the cues and the candidate tokens in constituency and dependency parsed trees.[START_REF] Fancellu | Neural networks for negation scope detection[END_REF] uses neural networks to solve the problem of negation scope detection. One ap-

		French	French
		clin. trials clin. cases
	Documents	-	200
	Sentences	6,547	3,811
	Tokens	150,084	87,487
	Vocabulary (types)	7,880	10,500
	Negative sentences	1,025	804
	Speculative sentences	630	226

proach uses Feed-forward neural network, while the other, which appears to be more efficient for the task, uses a bidirectional Long Short-Term Memory (BiLSTM) neural network. Given the results from the latter approach, it inspired our work.

Table 3 :

 3 Precision, Recall and F 1 -score for the cue detection task on the two corpora (bold: best scores).

		System	Corpus		window size	P	R	F 1
	Negation	CRF BiLSTM-CRF CRF BiLSTM-CRF	ESSAI CAS		(4) None (4) None		96.05 91.89 93.92 95.10 94.58 94.84 97.05 97.37 97.21 97.02 97.02 97.02
	Speculation	CRF BiLSTM-CRF CRF BiLSTM-CRF	ESSAI ESSAI+CAS	(4) None (4) None		91.43 82.76 86.88 91 83.84 87.27 93.93 88.82 91.30 91.22 87.92 89.54
					Scope tokens	Exact scope match
	Corpus	System		WE	P	R	F 1	P	R	F 1
				WI	86.21 82.85 84.50 100 55.61 71.47
		BiLSTM-S	W2V 83.54 83.68 83.61 100 56.59 72.27
	ESSAI			FT WI	80.79 86.41 83.51 100 56.59 72.27 84.65 84.09 84.37 100 59.51 74.62
		BiLSTM-CRF	W2V 83.86 83.10 83.48 100 61.95 76.51
	Negation			FT WI	82.38 84.84 83.59 100 59.51 74.61 93.72 87.30 90.40 100 73.21 84.54
		BiLSTM-S	W2V 93.03 88.69 90.81 100 75.59 86.10
	CAS			FT WI	91.50 88.69 90.08 100 72.02 83.74 91.87 88.59 90.20 100 68.45 81.27
		BiLSTM-CRF	W2V 91.47 88.29 89.85 100 76.19 86.49
				FT	94.82 87.10 90.80 100 78.57 88.00
				WI	89.27 82.14 85.56 100 52.76 69.07
		BiLSTM-S	W2V 88.61 84.42 86.47 100 56.69 72.36
	ESSAI			FT WI	85.84 84.58 85.21 100 58.27 73.63 89.77 83.03 86.27 100 51.97 68.39
		BiLSTM-CRF	W2V 87.85 83.77 85.76 100 55.91 71.72
	Speculation			FT WI	91.04 79.61 84.94 100 57.48 73.00 88.90 83.94 86.35 100 57.56 73.06
		BiLSTM-S	W2V 86.20 85.19 85.69 100 58.14 73.53
	ESSAI+CAS		FT WI	85.15 87.46 86.29 100 59.30 74.45 89.49 81.16 85.12 100 56.98 72.59
		BiLSTM-CRF	W2V 88.48 85.04 86.73 100 65.12 78.87
				FT	89.15 83.14 86.04 100 62.79 77.14

Table 4 :

 4 Precision, Recall and F 1 -score for the scope detection task (bold: best scores).

	Scope tokens	Exact scope match

Table 5 :

 5

Cross-corpora Precision, Recall and F 1 -score for the scope detection task (bold: best scores).

https://www.e-cancer.fr

https://bit.ly/2LOJfEW

https://quaerofrenchmed.limsi.fr/

https://radimrehurek.com/gensim/

https://github.com/ffancellu/NegNN

ACKNOWLEDGMENTS

This work was partly funded by the French government support granted to the CominLabs LabEx managed by the ANR in Investing for the Future program under reference ANR-10-LABX-07-01.


How? How? How? How?

A first solution, more sophisticated methods.

Definitions Definitions

Objectives vs Fitness/Heuristic is a way to inform the search about the direction to a goal. It provides an informed way to guess which path is promising. The heuristic function can be direct or indirect measurement, for example by providing only an approximation of the distance between a measurement and the optimum.

Heuristic function

Here's an algorithm for driving to someone's house :

Here's an heuristic for getting to someone's house :

Definitions Definitions

Objectives vs Fitness/Heuristic

The objective function can be considered as a form of heuristics, except that it is a direct measure of the potential of an aspect of the solution. In this sense, the objective function often requires more expertise on the system than the heuristics.

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  e, f , h} {d} {c, d, h} {c, d} {a, b, c} {a} {a, b, c, k, l} {a,

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  e, f , h} {d} {c, d, h} {c, d} {a, b, c} {a} {a, b, c, k, l} {a,

  minimize 2 freedom degree / parameters (genome) 200 islands (parallelism) of 5 minutes each 20000 evolutions performed by all the islands (200 island * 100 iteration ) 1 hook to save population ZombieLand ZombieLand val humanFollowProbability = Val[Double] val humanInformedRatio = Val[Double] val humanInformProbability = Val[Double] val result = zombieInvasion( humanFollowProbability : strategy to compare data (real/sim.) ? OpenMolisation : function to aggregate simulated data optimize perform with sum of distance criterium doesn't capture the overall dynamic to it's full extent Open question : introduce additional criterium ? change the aggregate function (MSE ?) Public policies, try to predict by optimisation Application to Zombieland Application to Zombieland val result = zombieInvasion( humanFollowProbability = vhFP, humanInformedRatio = vhIR + (formInform * vhIR), humanInformProbability = vhIP -(formInform * vhIP), humanExhaustionProbability = physic.humanExhaustionProbability -(runFast * physic.humanExhaustionProbability), humanRunSpeed = physic.humanRunSpeed + (runFast * physic.humanRunSpeed)need to choose a compromise The criteria are compatibles The criteria are either : totally independent in a "non constraining relationship" It still converge toward good solutions.


Kévin Da Silva

Nicolas Pons

Magali Berland

Mathieu Florian Plaza Oñate

Mathieu Almeida

Pierre Peterlongo

From genomics to metagenomics: benchmark of variation graphs

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From genomics to metagenomics: benchmark of variation graphs

Kévin Da Silva 1,2 , Nicolas Pons 2 , Magali Berland 2 , Florian Plaza Oñate 2 , Mathieu Almeida 2 , Pierre Peterlongo 1 In the metagenomics field, the classical approach for quantitative analysis of sequencing data consists into aligning sequence reads to a non redundant reference gene catalogue that represents a specific ecosystem [START_REF] Li | An integrated catalog of reference genes in the human gut microbiome[END_REF]. However this approach lacks flexibility and exhaustiveness. To overcome those biases, the reads should be aligned to a more informative reference structure covering the variants encountered in the population and also more complete with a full genome catalogue. Recently, the pangenome concept has been increasingly used as it opens new ways to investigate multiple genomes of close individuals, as for characterizing the different strains of a species. Erik Garrison et al. have developed "vg", a toolkit for creating variation graphs, bidirected DNA sequence graphs that represents multiple genomes, including their genetic variation [START_REF] Garrison | Variation graph toolkit improves read mapping by representing genetic variation in the reference[END_REF]. With a perspective towards metagenomics, we foresee vg as a tool enabling to build a catalogue of pangenomes from metagenomic samples. • Execution time with increasing number of genomes was recorded (left).

Background

• Time linear, 20 minutes for constructing the graph from 6 genomes. • Contigs as inputs:

• Complete genomes of two of the strains were cut into parts ("contigs"). Parts were overlapping on 20 kbp to ensure there was no ambiguity on the order of the parts. • Execution time with increasing number of cuts was recorded (right).

Time linear, 4 hours using both genomes cut in 100 parts each.

• A read is assigned to a path if the whole read matches a set of successive nodes belonging to the same path. • Three different ways of counting the number of reads assigned to a path:

• Total Count (read is counted for each path it is assigned to)

• Unique Count (read is counted if it is assigned to a unique path)

• Adjusted Count (Unique Count is used to define the relative abundance of the strains in the sample, this ratio is then applied to Total Count)

Mapping and counting using real data 4

Results on real data: 2 samples randomly selected from a collection after the German outbreak of Shiga-toxigenic E. coli (STEC) O104:H4 in 2011 mapped on the graph → 1 sample positive for STEC (STEC+) where O104:H4 is expected and 1 negative (STEC-) where O104:H4 should be absent.

In the STEC+ sample, as opposed to the STEC-, the O104:H4 strain has a higher signal, demonstrating that we can identify and quantify a strain among a whole metagenomic sample composed of several species. As a proof of concept, we constructed a pangenome graph for Escherichia coli with three main objectives: evaluating time complexity to take it into consideration for future scaling, defining paths (successive nodes) in the graph corresponding to strains, and identifying and quantifying strains in a sample by mapping its reads on the graph.

Objectives

The strains and their original abundance are correctly identified.

Assigning reads to paths and counting 2 Mapping and counting using simulated data 3

 23 Results on simulated data: 100 000 reads of length 200 bp were generated without errors using a mix of O104:H4 and IAI39 E. coli strains at different ratios and then mapped on the graph.UnitéMetaGenoPolis www.mgps.eu contact@mgps.eu Unité GenScale team.inria.fr/genscale/ 1 Univ Rennes, CNRS, Inria, IRISA, -UMR 6074, F-35000 Rennes 2 MetaGenoPolis, INRA, Université Paris-Saclay, 78350, Jouy-en-Josas, France Contact: kevin.da-silva@inria.fr Graph construction and ̕ time complexity 1 Data • Inputs: 6 strains of E. coli → O157:H7, IAI39, BL21, SE15, O104:H4, K-12 • Graph construction using VG Graph construction • Complete genomes as inputs:

2

 2 Compute relative abundance 𝑟 𝑖 for each i strain and apply it to the Total Count