Alain Dieterlen
email: a.dieterlen@univ-mulhouse.fr

Chengqi Xu

Marie-Pierre Gramain

Olivier Haeberlé

Bruno Colicchio

Christophe Cudel

Serge Jacquey

Emanuelle Ginglinger

Georges Jung

Eric Jeandidier

M-P Gramain

Validation of image processing tools for 3-D fluorescence microscopy

Keywords: Identification, 3-D Deconvolution, 3-D quantification, Fluorescence microscopy, FISH (Fluorescent In Situ Hybridization)

La microscopie 3D de fluorescence est un moyen efficace d'investigation volumique de spécimens biologiques. L'acquisition d'images tridimensionnelles repose sur le déplacement relatif de l'objet par rapport à l'objectif effectuant ainsi un "découpage optique". Les déformations dues aux singularités de la fonction de transfert optique rendent indispensable une restauration des images avant toute mesure quantitative 3D. L'identification de la réponse impulsionnelle permet de préciser les protocoles d'acquisition. La connaissance du modèle de formation d'image est nécessaire à l'optimisation de la déconvolution. Différents algorithmes de déconvolution ont été implémentés, le choix des paramètres de régularisation a été automatisé. Dans le but de faciliter l'utilisation de ces outils d'investigation aux utilisateurs, un logiciel VIEW3D a été réalisé, intégrant la visualisation des images 3D, l'identification de la fonction de transfert, la déconvolution et l'analyse des résultats. Cette boite à outils développée sous environnement PV-WAVE™ est évolutive et multi-plate-forme. L'enjeu de ce travail est de proposer des conditions analytiques reproductibles validées sur des échantillons d'intérêt clinique. Un exemple d'application en cytogénétique est présenté.

Abridged version

La microscopie photonique trouve un nouvel essor avec l'avènement de nouvelles Les premiers résultats obtenus sont prometteurs. Cependant, les études sur échantillons biologiques sont assez complexes en raison de la diversité de la distribution de l'intensité et de la morphologie des zones d'analyse. Ceci implique de rendre le processus de déconvolution adaptatif par l'utilisation de pré-traitements des zones d'intérêt biologique.

Introduction

Three-dimensional intracellular structure data are often necessary in biological study. Thus, it may be helpful to detect cell events if one can localize fluorescent probes targeted to cell compounds such as cell proteins or DNA sequences. These measurements may be obtained by multi-labeling techniques involving multi-wavelength acquisition [START_REF] Bour-Dill | Determination of intracellular Organelles Implicated in Daunorubicin Cytoplasmic Sequestration in Multidrug-Resistant MCF-7 Cells Using Fluorescence Image Analysis[END_REF]. The numbering of chromosome copies within interphase cells using alpha-satellite or locus specific fluorescent probes is a powerful approach to detect chromosomal number abnormalities [START_REF] Philip | Prenatal aneuploidy detection in interphase cells by fluorescence in situ Hybridization (FISH)[END_REF] but requires accurate optical conditions in order to localize and separate fluorescent sites within nuclei [START_REF] Bradl | A tilting device for three-dimensional microscopy: application to in situ imaging of interphase cell nuclei[END_REF]. The image formed by a conventional fluorescence microscope contains light from throughout the specimen. What is actually present in the focal plane may be blurred by this "out-offocus" fluorescence. Reduction of this side effect is the aim of 3-D microscopy [START_REF] Mcnally | Three-Dimensional Imaging by Deconvolution Microscopy[END_REF].

Particularly in conventional microscopy, this reduction is carried out computationally by deconvolution process.

The characterization of the out-of-focus light is based on the 3-D image of a point-like source, the so-called Point Spread Function (PSF). The image formation process can be described by the following convolution equation:

g(X)= h(X-X 1 ) f( X 1 ) d X 1 -∞ +∞ + b(X)
where X and X 1 are 3-D coordinates, g(X) is the recorded image, h(X) the PSF of the system and f(X) the original object. The term b(X) represents a random process which is a combination of additive noise sources due to the photon emission process and the data acquisition system. Specificities of the image formation process in an optical microscope introduce aberrations and intensity distributions, such as axial elongation and intensity artifacts [START_REF] Mcnally | Artifacts in computational optical-sectioning microscopy[END_REF][START_REF] Dieterlen | Artifacts due to shift variant and quasi-monochromatic image formation in 3D reconstruction by sectioning microscopy[END_REF]. Due to these distortions, original images from the microscope cannot be used directly for quantitative measurements like size and intensity, but need restorations before any analysis.

Image processing is a key element, and a lot of In the mind of simplifying the 3-D fluorescent images exploitation, a package integrating all the tools necessary for a complete biological analysis, called VIEW3D, has been developed. It is a package allowing in a simple environment modelization and identification of microscopy systems, and the deconvolution, and the visualization and measurements of data. Since this processing takes into account the experimental conditions, the results obtained using this method is supposed to be more reproducible, improving the reliability in medical analysis. An example of application in the fields of cytogenetics is provided in Part 4.

Methods

System identification

In order to characterize the optical system, it is necessary to measure the PSF which describes the properties of the image acquisition system. The study of system identification has been first carried out on PSFs computed with the Gibson and Lanni model [START_REF] Gibson | Modeling the three-dimensional imaging properties of the fluorescence light microscope[END_REF], then applied to experimental PSFs. Even if a good correlation is observed between simulations and experiments, experiments are the only methods allowing to characterize optical aberrations like chromaticity and asymmetry of the system.

A specific adapted protocol may be proposed by determining the precise influence of the different experimental parameters on PSF.

Deconvolution

The main objective of deconvolution is to reduce the image distortions by redistributing the fluorescence intensities and reconstructing geometrical shape of the sample. Numerous deconvolution methods have been described in the literature [START_REF] Hunt | Prospects for imaging restoration[END_REF][START_REF] Meinel | Origins of linear and nonlinear recursive restoration algorithms[END_REF]. Up to now, we have focused our attention on methods using the PSF and technique being well adapted to fluorescence microscopy: such as non-iterative LLS (Linear Least square Solution developed by Preza [START_REF] Preza | Regularized linear method for reconstruction of three-dimensional microscopic objects from optical sections[END_REF][START_REF] Preza | Image reconstruction for 3-D light microscopy with a regularized linear method incorporating smoothness prior[END_REF]) and Maximum A Posteriori (MAP) [START_REF] Xcosm | [END_REF] algorithms, iterative ML-EM (Maximum-Likelihood Expectation Maximization developed by Holmes [START_REF] Holmes | Maximum likelihood image restoration adapted for noncoherent optical imaging[END_REF]) and the Carrington [START_REF] Carrington | Image restoration in 3D microscope with limited data[END_REF] algorithms. The reader may refer to published literature for a detailed description of these algorithms. These methods do take into account the system singularity and the presence of noise. However a number of regularization parameters should be introduced by users, which may modify the subjectivity of the results. In order to determine this parameter automatically, the sum of the image bias error and the noise variance is minimized (using VIEW3D), according to the PSF of the image acquisition system. In this way the reproducibility of measurements is improved.

Materiel

Microscope

An upright epifluorescence microscope BX-51 PROVIS (Olympus) with a 100 W xenon lamp as excitation source is used. Image acquisitions are carried out with an apochromatic, infinity corrected, 100X oil immersion objective lens (numerical aperture of 1.4).

The microscope is equipped with the EPR™ optical sectioning acquisition system: a piezoelectric translation device is inserted in between the motorized revolving nosepiece and the objective. The minimum scanning step is 0.25 µm and the maximum scanning range is 100 µm.

The image sensor used is a Peltier cooled charge coupled device CCD-1317K camera from Princeton Corporation. The pixel size is 6.8 x 6.8 µm 2 and the matrix dimension is 1317 by 1035 pixels. The intensity is 12 bits coded.

Software

Combining our 3-D fluorescence microscopy with some biologist expertise, a set of tools for the analysis of images from widefield or confocal microscopy has been developed. This all-in one toolbox VIEW3D is developed with PV-WAVE™ (Unix and Windows compatible). The graphical user interface provides direct access to various operations: image loading and visualization, deconvolution, segmentation, 3-D geometrical measurements and morphological description. The results may be collected and saved under various formats to export them to other softwares (spreadsheets or presentation tools).

VIEW3D graphical interface includes several modules (Figure 1):

• Data acquisition (Figure 2)

• XY and XZ plane by plane interactive viewing (Figure 3)

• 3-D visualization (Figure 4)

• Confocal or conventional microscope PSF computation

• Deconvolution, segmentation, connected components labeling (Figure 5 and6)

• Data analysis: shape descriptor, volume and intensity measurements (Figure 7) reassignment of thin specimens and, because of their rapidity, LLS and MAP are more adapted to routine processing [START_REF] Chomik | Quantification in optical sectionning microscopy : A comparison of some deconvolution algorithms in view of 3-D image segmentation[END_REF].

The practical application using some biological samples is currently under evaluation and preliminary results already show a significant improvement in the signal analysis after FISH, using LSP labeling of the 21q22.13-q22.2 chromosomal region. Figure 8 shows the wide intensity spread of the fluorescent spots without image processing. Figure 9 shows the same nucleus area after MAP deconvolution.

Discussion

Image restoration in 3-D fluorescence microscopy is mandatory to remove artifacts of raw data. Thus, allowing a reliable and reproducible biological analysis.

The precise determination of experimental parameters during the data acquisition is critical to the restoration process. However, many experimental parameters are both specimen dependent, (refractive indices, thickness) and optical condition-dependent (oil refractive indices, NA). The Gibson and Lanni model used in VIEW3D is an user friendly as well as efficient tool since most of the experimental parameters are clearly defined. It may be not adapted to high NA objectives, because of its scalar model of diffraction underpinning.

Therefore vectorial theories of diffraction should be more appropriate [START_REF] Török | Focusing of Electromagnetic Waves Through Dielectric Interfaces: Theory and Correction of Aberration[END_REF][START_REF] Egner | Equivalence of the Huygens-Fresnel and Debye approach for the calculation of high aperture point-spread functions in the presence of refractive index mismatch[END_REF].

Preliminary results obtained with FISH on uncultured amniotic fluid cells show a significant improvement in both fluorescent signals localization and separation as presented in Figures 8 and9. This claims for the efficiency of the method to reduce the wideness of the fluorescence intensity spread, mainly due to the poor resolution along the microscope optical axis (Z), which usually induces artifacts in spots numbering [START_REF] Bradl | A tilting device for three-dimensional microscopy: application to in situ imaging of interphase cell nuclei[END_REF].

For some biological specimens, intensity distribution and morphology may vary significantly from one sample to another. The photon noise is signal dependent. It implies that the deconvolution process should be adaptive, i.e. regularization parameters even the deconvolution algorithm have to be data driven.

Data volume characterization has been carried out. The purpose of this pre-processing is to split the entire volume into regions characterized by homogeneous statistical and morphological properties. This allows to apply separately appropriate restoration techniques to each of them.

Furthermore, it is mandatory for users to be aware of the shorts of artifacts that can arise and what must be done to reduce them.

Conclusion

An embedded 3D image processing tool the According to the nature of the studied specimen, this multi-platform compatible software allows users to choose one of the four types of implemented deconvolutions. VIEW3D has been designed in a modular way making it easy to further incorporate new modules.

Both experiments on calibrated reference beads and on biological samples are very encouraging. There is no best deconvolution method. As a general rule, methods that require more computer time yield better results. However, in some cases speed may be critical. A panel of different algorithms being "at hand" in the same package allows to choose first a rapid deconvolution method in order to select interesting samples on which time consuming algorithms will be run.

This experimental approach leads to improved reproducibility of geometrical measurements on biological samples.

Figure legends

Fig. n°1: "VIEW3D" main menu, the left buttons are dedicated to visualization and right ones to images processing tools. The icon images represent the central plane of loaded data.

  Mot clefs: Identification, Déconvolution 3D, Quantification 3D, Microscopie de fluorescence, FISH (Fluorescent In Situ Hybridization).

  technologies et passe d'observations classiques en deux dimensions à des acquisitions d'images tridimensionnelles d'échantillons biologiques vivants fluorescents. L'acquisition d'images tridimensionnelles d'objets transparents repose sur l'utilisation d'un dispositif spécifique qui permet d'effectuer un "découpage optique" (optical sectioning microscopy) de l'objet par déplacement axial de l'objectif. Les caractéristiques particulières (perte de fréquences spatiales ou ″missing cone ″, singularité à l'origine) de la fonction de transfert optique (Optical Transfer Function ou OTF) rendent indispensable la restauration (ou déconvolution) des images avant toute mesure quantitative 3D. La réponse impulsionnelle optique appelée PSF (pour Point Spread Function) caractérise le système d'acquisition. Elle peut être mesurée ou calculée à partir de modèles scalaires ou vectoriels. Utilisant le model scalaire de Gibson et Lanni associé à des données expérimentales, un outil statistique permettant l'identification de la réponse impulsionnelle a été développé. L'analyse théorique et expérimentale de l'influence des paramètres d'acquisition est essentielle pour optimiser le protocole d'acquisition sur échantillons biologiques. La restauration devra améliorer la qualité des données : corriger les distorsions géométriques et réassigner les intensités à leurs origines. Une restauration de qualité nécessite la connaissance du système d'acquisition et donc de son OTF. Des informations a priori sur le spécimen étudié peuvent aussi s'avérer utiles. Quatre algorithmes de déconvolution ont été implémentés, réalisant soit une inversion directe régularisée (LLS et MAP) soit une inversion itérative (ML-EM et l'algorithme de Carrington). Pour mener à bien la restauration, les paramètres de régularisation sont déterminés par une procédure automatique minimisant une fonction de coût adaptée. Cette automatisation doit permettre une plus grande facilité d'utilisation et une meilleure répétitivité inter-utilisateur. Si l'on décompose en quatre parties le processus d'analyse d'images en microscopie de fluorescence, on distinguera : l'acquisition des données, la déconvolution, la quantification et enfin l'interprétation des données. Les outils d'investigation ont été intégrés dans un logiciel appelé VIEW3D, dans le but de faciliter leur utilisation. Dans cet environnement sont regroupés les outils de visualisation 3D, d'identification de la fonction de transfert, quatre algorithmes de déconvolution automatisés et des outils de mesure 3D. A l'aide de billes fluorescentes de référence, ces outils sont testés en particulier pour déterminer le facteur d'élongation axiale afin d'effectuer la correction. Les analyses statistiques, géométriques ou morphologiques deviennent alors significatives. Pour illustrer ces travaux, cette méthodologie est appliquée sur des images de noyaux cellulaires marqués par une molécule fluorescente constituée d'une sonde d'ADN marquée à la rhodamine utilisée en cytogénétique.

The 3 -

 3 D data can be acquired by Optical Sectioning Microscopy (OSM) which is performed by axial stepping of the object versus the objective. A stack of 2-D images is the optical equivalent of a series of microtome slices, allowing a 3-D reconstruction of a specimen. Most of the three-dimensional fluorescence microscopy techniques now use a confocal microscope [4]. Compared to the conventional fluorescence microscope, the confocal microscope design reduces haze from out of focal-plane objects. Although confocal microscopy has many advantages, it does have some limitations. The amount of excitation light required to produce a confocal image may be a problem for fixed specimens labeled with several different dyes and for long-term 3-D imaging of living samples.

  efforts have been devoted to develop specialized computer tools. To summarize, one can split this process in four steps: image acquisition, deconvolution, feature extraction which give various data: number, size and intensity distributions of fluorescent sites for example, and eventually at the end the biological interpretation of processed data. The data acquisition requires full hardware control of the acquisition system (filter selection, stage scanning, illumination and image acquisition synchronization). As a consequence, specialized softwares are provided by manufacturers (EPR, DeltaVision) of the processing system. A tremendous work has been dedicated to fluorescence image deconvolution in recent years. This has led to the development of special image processing softwares (XCOSM, Bit-Plane…). The data analysis is often performed using more general tools like Noesys or Analyze. So, to analyze a single specimen the biologists often have to use several platforms and/or softwares.

  The three-dimensional Fourier transform of the PSF is called the Optical Transfer Function (OTF) of the system. The OTF describe the spatial frequency distribution. The PSF is function of different instrumental parameters such as the numerical aperture (N.A.), the emission wavelength (λ em ), the refractive indices of the different media along the optical path and optical aberration of the lens[START_REF] Gibson | Experimental test of an analytical model of aberration in an oilimmersion objective lens used in three-dimensional light microscopy[END_REF]. Consequently, it is useful to quantify the variations in the PSF distribution induced by different conditions of observation. A PSF can be either measured or computed. Experimental PSFs are obtained by imaging small fluorescent latex beads (Interchim or Molecular Probes). Ideally the microspheres should be as small as possible, but the smallest ones are weak in intensity and bleach rapidly. A trade-off must be found to define the microsphere diameter according to the characteristics of the microscope objective in use. Usually beads with a diameter of about one third of the Rayleigh criterion are used[9]. Computed PSFs are noiseless, however they do rely on a simplified model of the microscope. Furthermore, the assumed experimental parameters in the computational model may differ from their actual values. Statistical tools have been developed to quantify the variation of the PSF as function of the optical parameters: fluorescence wavelength, N.A. of the objective, refractive index of the immersion oil, optical tube length and specimen thickness. They are based on higher order statistical moments, known as excellent descriptor of energy distribution and symmetries of the PSFs as well as the aberrations of the system[START_REF] Castaneda | Two-dimensional evaluation of point spread functions using centered reduced moments[END_REF].

  VIEW3D has been developed to analyze fluorescent images of 3-D biological specimen. It enables the possibility to perform all quantification steps within the same environment. It integrates most of functions required in 3-D biological measurements, i.e., 3-D specimen representation, system identification to define experiment protocol, raw data restoration for artifacts correction, segmentation, geometrical and morphological measurements of fluorescent sites (necessary for specimen analysis).

Fig. n°2 :

 n°2 Fig.n°2: View box for raw data acquisition, the user is invited to precise header size, data format and volume size.

Fig. n°3 :

 n°3 Fig. n°3: Section visualisation of a cell nucleus. The 17 th XY plane is presented above and the 27 th XZ section below.Fig. n°4: 3-D visualisation of a cell nucleus. The embedding box is indexed by the X, Y and Z coordinates.

Figure n°5 :

 n°5 Figure n°5: Section visualisation of a cell nucleus after MAP deconvolution. the 17 th XY plane is presented above and the 27 th XZ section below.

Figure n°6 :

 n°6 Figure n°6: Section visualisation of the deconvolved cell nucleus after segmentation. The 17 th XY plane is presented above and the 27 th XZ section below.

Figure n°7 :

 n°7 Figure n°7: Results in the box of measurements obtained after processing are presented. 5 different fluorescent sites are detected, surfaces and volumes are calculated.

Figure n°8 :

 n°8 Figure n°8: Sections of amniotic cell nucleus labeled with rhodamine labeled DNA probe targeted to the 21q22.13-q22.2 chromosome region before deconvolution.

Figure n°9 :

 n°9 Figure n°9 : Same sections of amniotic cell nucleus labeled with rhodamine labeled DNA probe targeted to the 21q22.13-q22.2 chromosome region after MAP deconvolution.

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Acknowledgement

This work has been partially financially supported by an CNRS PCV98/082 project (100KFr), in collaboration with the Department of membrane protein and receptors (CNRS UPR 9050) directed by Dr. Jean-Luc Galzi.

We also thank the biologists from the research laboratory in oncology of the "Centre Alexis Vautrin" (Vandoeuvre-les-Nancy, France) directed by Pr. Jean-Louis Merlin, especially Corinne Bourg-Dill for her contribution on biological applications. The authors are grateful to Dr. D. Dumas (Laboratoire d'AngioHématologie-Hémorhéologie, Faculté de Médecine, Vandoeuvre-les-Nancy, France) for his contribution to 3-D microscopy acquisitions.

The following deconvolution algorithms are available : LLS, MAP, ML-EM and Carrington's methods. Segmentation is performed by intensity thresholding or via mathematical morphology. The 3-D labelling is provided for analysis and measurements of fluorescence sites. Volumes, surfaces, compactness factors and up to 4 th order centered moments (mean, variance, skewness and kurtosis) are implemented. These informations allow quantitative measurements (volumic and intensity distributions) of the fluorescence sites, thus facilitating specimen classification.

Biological samples

Human uncultured amniocytes have been obtained by amniocentesis and spread on microscope slides after centrifugation of 3 ml of amniotic fluid at 1,000 rpm for 8-10 minutes.

Fluorescent in situ hybridization (FISH) probes were commercial products available from Vysis (Downers Grove, IL, USA) and applied according to the manufacturer's instructions.

Rhodamine labeled locus specific probes (LSP) D21S259, D21S341 and D21S342, spanning the region 21q22.13-q22.2 have been assayed in order to numerate the chromosome 21 copies on interphasic nucleus.

Results

To validate our program, VIEW3D was firstly experimented using images of calibrated fluorescent beads.

Identification tools allow to characterize aberrations in order to determine if the system is under the design conditions. Some parameters, such as the refractive oil immersion index, the specimen depth and the NA, are the most interesting parameters in order to get an optimal acquisition [START_REF] Gramain | Caractérisation tridimensionnelle d'un microscope à épifluorescence[END_REF].

Deconvolution methods have been compared regarding to intensity reassignment, geometrical reconstruction and speed. As previously published [START_REF] Gramain | Conventional 3-D fluorescence microscopy : Experimental elongation determination[END_REF], the Z elongation and dissymmetries are the main distortions due to both index mismatch and specimen thickness. The 4 methods, implemented in VIEW3D, improve image contrast and reduce the axial elongation. However, ML-EM is more appropriate for volume reconstruction, Carrington's method for intensity


J Dupuis

E Fourmond

J F Lelièvre

D Ballutaud

M Lemiti

Impact of PECVD SiON stoichiometry and post-annealing on the silicon surface passivation

Keywords: SiON, surface passivation, solar cells, XPS, FTIR

Hydrogenated silicon oxynitride (SiON) could be used in combination with silicon nitride (SiN) to create multilayer antireflection coatings for silicon solar cells. It could also be used as a passivation layer, especially on the back side of the cell. This work deals with the passivation effect obtained on silicon surface by SiON layer deposited by Low Frequency Plasma Enhanced Chemical Vapour Deposition (LF-PECVD).

SiON layers of different compositions have been deposited by varying the gas flow mixture (NH 3 , SiH 4 and N 2 O) in the reactor. Infrared and X-Ray photon-electron spectroscopy were made to determine the chemical structure of SiON layer. Minority carrier lifetimes were measured by the photoconductance decay method (PCD) before and after a rapid thermal anneal. Effective lifetime, measured on 5 ohm.cm FZ-silicon wafers, can reach up to several hundreds microseconds, depending on the stoichiometry of the SiON layer. Low oxygen content samples (close to SiN layer) exhibit a good surface passivation of 250 µs but after annealing, this value is critically reduced to 6 µs. The opposite situation is observed for oxygen-rich layers: the effective lifetime increases from 10 µs to 150 µs. These behaviours could be partly explained by the composition of SiON, the evolution of the main peaks values of the FTIR spectra and the disappearance of Si-H bonds with anneal.

Introduction

Hydrogenated silicon oxynitride (SiO x N y :H abbreviated SiON afterwards) can be deposited at low temperature with Plasma Enhanced Chemical Vapour Deposition (PECVD). It exhibits low absorbance and adjustable refractive index between 1.45 and 1.9 [1] by changing the ratio of precursor gas flow during deposition. SiON is thus suitable for optoelectronic devices such as waveguides, and has become a standard in optoelectronics industry [2,3].

However, these properties can also be suitable for photovoltaic industry especially for antireflection multi-layers in combination with hydrogenated silicon nitride (SiN x :H abbreviated SiN afterwards). Adjusting carefully optical indexes and thicknesses of the film could optimise the penetration of solar photon in the solar cells. Thus double SiON/SiN layers seem to give a better transmission coefficient than a single SiN layer [4]. Gradual antireflection layers could also be imagined, with refractive index varying from SiN to SiO value to increase solar cells efficiency [5].

Antireflection layer is not the only possible application of SiON. Due to the increasing price of silicon material, standard solar cells are getting thinner and the quality of the rear surface passivation is becoming more and more crucial to maintain high solar cell efficiency [START_REF] Hofmann | 19 th European Photovoltaic Solar Energy Conference Proceeding[END_REF]. The passivation effect of SiON is very promising for solar cell rear surface and could replace the actual aluminium back surface field, responsible for high constraint on thin solar cells.

Moreover SiON can also be included in a stack system with SiN and SiOx:H (abbreviated SiO afterwards) for optimum rear surface passivation [START_REF] Hofmann | 19 th European Photovoltaic Solar Energy Conference Proceeding[END_REF]. Finally, low SiON indexes make this layer suitable to act as a rear surface reflector. Indeed, high wavelength photons are absorbed less efficiently by silicon and increasing their reflection at the back side of the solar cells could increase the optical path length through the silicon wafer and give them a better chance to be absorbed. This work aims to study electronic and structural properties of SiON deposited by PECVD for solar cells applications. The passivation impact of the layers is quantified by minority carrier lifetime (MCL) measurements, performed for different stoichiometries of SiON. The chemical structure of SiON is analysed by X-Ray photon-electron spectroscopy (XPS) and Fourier Transform Infrared spectroscopy (FTIR). All theses characterisations have been made before and after a rapid thermal anneal (RTA).

Experimental

SiON layers have been deposited with ammonia, pure silane and nitrous oxide (respectively NH 3 , SiH 4 and N 2 O) as precursors gases in a semi-industrial Low Frequency PECVD reactor (LF-PECVD) at 440 kHz. The reactor used has been described elsewhere [4]: it was developed by SEMCO-Engineering and presents a vertical configuration chamber. The deposition was made at 370°C, with a power of 0.26 W per cm 2 and a pressure of 1500 mTorr. Silane flow and total gas flow have been set respectively to 25 sccm and 800 sccm and maintained constant.

Different SiON stoichiometries were obtained by varying the "Ox" ratio which is defined as:

2 2 3 N O Ox N O NH = +
The Ox ratio is related to the oxygen content of the SiON layer (Ox=0 if it is pure SiN and Ox=1 if it's pure SiO). After deposition, refractive index, extinction coefficient and thickness were measured by spectroscopic ellipsometer (Jobin Yvon UVISEL) using Tauc Lorentz dispersion model [START_REF] Gellison | [END_REF].

Then XPS have been performed on SiON samples to obtain their atomic composition.

Substrates were 2" CZ n-type silicon (100 oriented) with a resistivity of 1-10 Ohm.cm. The SiON layer thickness is around 70nm. The XPS measurements were carried out in a VG 220i

XL system, with a base pressure of 5x10 -10 torr and using AlK (1486.5 eV) X-ray monochromatized radiation with pass energy of 20 eV (resolution 0.2 eV). Energy levels of XPS were calibrated with Au single crystals. The spectra were processed using the VG Eclipse Datasystem. The background contribution is included in the fitting process, using the Shirley method.

Infrared measurements were also carried out to study structural bonds in SiON samples. For that case, substrates were FZ 2" p-type silicon with both sides polished and 4-7 ohm.cm resistivity (100 oriented). The thickness of deposited SiON films is around 200nm. The experimental setup is a Fourier-Transform infrared spectrometer (FTS60 A, Bio-Rad Inc.) associated with a DTGS detector operating in the spectrum range 1.6-20 micrometer.

Next, minority carrier lifetime measurements (MCL) have been carried out to evaluate the surface passivation quality. MCL have been determined by photoconductance decay measurements using a WCT-100 Photoconductance Tool from Sinton Consulting [8]. SiON layers were deposited on both sides of polished 1-10 ohm.cm silicon wafers n-type and p-type (100 oriented) with a thickness around 70 nm. The high quality of the silicon wafer implies that the effective measured lifetime depends essentially on the surface recombination velocity (bulk minority carrier lifetime is superior to 3ms). After all these measurements, FTIR and MCL samples were annealed at 800°C in a lamp furnace with a nitrogen atmosphere during several seconds, to simulate the firing of the contacts on both sides of solar cells and then new measurements were made.

Ellipsometric measurements

Refractive

XPS measurements

The complete composition of SiON samples is shown in table 1. globally constant beyond 20 percent of oxygen in the film and suggest that nitrogen atoms bonded with silicon are replaced by oxygen atom. This behaviour has also been reported using N 2 O and SiH 4 gas precursors [9,10].

Moreover, the oxygen concentration in the layer varies linearly with the refractive index.

Therefore, there is a linear connection between the Ox ratio and the atomic oxygen concentration found by XPS.

In the plasma, oxygen radicals react in priority with Si radicals because the most favourable chemical reaction is Si-O bonding. N-H, Si-H and Si-N bonding seems to form when oxygen becomes deficient [9].

The hydrogen content could not be detected by XPS measurements but we can estimate its concentration around 30% for SiN layer and several percent for SiO layer [10,11]. For photovoltaic applications, this hydrogen is beneficial, particularly for multicrystalline silicon wafers for which a rapid thermal anneal can lead to the diffusion of atomic hydrogen and the subsequent passivation of defects in the silicon bulk [4,12].

Fourier transform infrared spectroscopy

FTIR spectra are shown in figure 2.

The predominant absorption band is localised between 800 cm -1 and 1200 cm -1 and can be resolved into Si-N and Si-O groups [9,13]. Other absorption peaks are less significant and the 2000-4000 cm -1 spectral region was magnified five times for appreciation. Going from Ox=0

(SiN) to Ox=1 (SiO), the main peak moves from the Si-N stretching mode to the Si-O stretching and rocking modes. These peaks values are shifting with the modified chemical environment from 870 cm -1 to 1030 cm -1 with the increase of oxygen content in the layer. The peaks localised at 1180 cm -1 and 3350 cm -1 correspond to N-H bending and stretching vibration respectively [14]. These peaks disappear with the decrease of nitrogen content in the layer. Si-H stretching mode stands at 2200 cm -1 and is disappearing too with the increase of oxygen in the sample. Finally, the small peak at 3500 cm -1 which appears for rich oxygen layer could be related to H-O-H stretching vibration [15].

With the increase of oxygen content, the layer chemical configuration is deeply changed. The variation in peaks intensity with the Ox ratio could be related to the privileged Si-O bond formation. This is why Si-N, N-H and Si-H related peaks are all decreasing whereas Si-O is increasing. (1075cm -1 ) , respectively [16,17]. The framed area points out the change in Si-H stretching bond according to the Ox ratio, before and after the rapid thermal anneal step.

After the rapid thermal anneal (RTA), no change is observed on optical index or thickness.

Annealing is normally followed by the densification of the SiON films [15] but the RTA seems too short and the temperature seems too low to densify SiON layers. FTIR spectra (Fig. 2b) are very similar to the previous ones. Still, Si-O stretching vibration tends to shift to a higher wavenumber for oxygen-rich sample. For example, looking at the Ox=1 spectrum, Si-O peak shifts from 1027cm -1 to 1040cm -1 . On the other hand, the reduction of Si-H stretching vibration is clearly visible for nitride-rich sample.

The energy brought by RTA leads to bonding re-arrangement within the layers. Si-H bonds are sufficiently weak (3,3eV) to be broken easily by the RTA, contrary to N-H (4,0eV) or Si-N (3,7eV) which stays almost unaffected [4] [18]. Therefore, hydrogen might escape from the layer and the shifts of all Si-O stretching vibration could be explain by the silicon dangling bonds which bind to oxygen atoms in priority because in SiON, Si-O bond is more stable and more thermodynamically privileged than other bonds.

Minority carrier lifetime measurements (MCL)

The evolution of MCL according to the refractive index is represented in figure 3. For comparison, MCL of SiN obtained in the same PECVD reactor has been plot with SiON results. SiN results are discussed elsewhere [START_REF] Lelièvre | 15 th International Photovoltaic Science and Engineering Conference & Solar Energy Exhibition[END_REF].

1, 5 2,0 2,5 3,0 10 -6 10 -5 10 -4 10 -3 10 -2
Minority carrier lifetime (sec)

Refractive index

N type before RTA N type after RTA P type before RTA P type after RTA SiN SiON Figure 3. Evolution of minority carrier lifetime for different SiN and SiON layers according to the refractive index at λ=633nm and for an excess carrier concentration of 7.10 14 cm -3 .

Lines are guide for the eye.

Before RTA, MCL is decreasing with the refractive index for N and P substrates. MCL is always higher for n-type silicon. This behaviour could be explain by of the higher cross capture section of electron in p-type silicon [START_REF] Wolf | [END_REF].

Increasing oxygen content leads to decrease nitrogen and hydrogen content in the layers and form in priority Si-O bonds. But, contrary to thermal oxide, it seems that Si-O bonds formed at low temperature do not create a passivation effect as good as other bonds like Si-N or Si-H : the value of MCL is a hundred time lower for SiO layer than for SiN. Therefore, it seems that the increase of oxygen is not good for the surface passivation.

The deterioration of the passivation could be explained by the fact that the field effect due to the fixed charges density in SiON is lower than for SiN, in particular because of the decrease of K+ centres [22].

Moreover, the interface state density D it , which is a critical parameter for passivation, has been reported to be thirty time higher for SiN film than for thermal SiO 2 film [21]. In SiN layer, a part of the passivation quality depends on the hydrogen content in the layer which could limits D it value [4]. However, hydrogen content in SiON films has been reported to decrease when oxygen increases [10] [11]. This may explain an increase of D it which could not be neutralised by the as-deposited oxide: it would result of a continuous decrease of MCL as oxygen content increases in the layer.

After RTA the situation is completely different. MCL of nitrogen-rich SiON layers become very low whereas it is the opposite in oxygen-rich SiON samples.

FTIR measurements have shown a slight shift for all Si-O stretching bonds towards the Si-O wavenumber value in thermal oxide. These shifts could suggest a modified environment of Si-O stretching vibration and maybe a better passivation behaviour. This could explain the observed MCL improvement after RTA for oxygen-rich SiON layers. On the other hand, the re-arrangement of oxygen bonds seems to be insufficient to explain by itself these high MCL improvements. Moreover, for nitrogen-rich sample, the sudden decrease of MCL with RTA remains difficult to explain. A possible explanation could be that for SiN or nitrogen-rich SiON, FTIR spectra shows that RTA leads to the break of Si-H bonds and free hydrogen in the layer. This hydrogen could exodiffuse or combine with elements of the layer. However, bonds formations which occur at the interface between the film and the substrate could be beneficial for the surface passivation quality (decrease Dit). Therefore, it is possible that the decrease of silicon concentration for Ox ratio inferior to 0.20 (see table 1) leads to a higher porosity of the SiON film compared to SiN layer [11]. This would enhance the exodiffusion of hydrogen, and thus limit the passivation effect of the layer [START_REF] Dekkers | 20th European Photovoltaic Solar Energy Conference[END_REF].

Conclusion

Silicon oxynitride (SiON) were grown by LF-PECVD technique by varying flows of SiH 4 , NH 3 and N 2 O gas precursors. Refractive indexes obtained vary between 1.54 and 1.84 and absorption is inexistent in the full visible spectral range. The composition of these layers determined by XPS shows the increase of oxygen content related to the increase of N 2 O gas flow : oxygen radicals react mainly with silicon, prior to hydrogen or nitrogen. The refractive index has been found directly proportional to the oxygen content. FTIR spectra have confirmed this tendency and have pointed out the re-arrangement of SiON films with a rapid thermal anneal (RTA). Minority carrier lifetime (MCL) measurements have shown that oxygen and hydrogen play a major role in the passivation quality of the SiON layer. High oxygen content leads to a bad surface passivation before RTA maybe because of the poor passivation properties of deposited oxide and because the reduced amount hydrogen leads to increase D it . After RTA the situation is totally opposite: oxygen-rich samples exhibit a better MCL because the Si-O properties change a little and seems to tend to the thermal oxide behaviour. One the other hand nitrogen-rich samples have a very low lifetime, which is difficult to interpret. This could be explained by the relatively high porosity of the SiON layer, leading to hydrogen exodiffusion. C-V measurements, hydrogen desorption and RTA with different temperatures are in progress to understand better these behaviours.

Finally, MCL results show that oxygen-rich SiON films are very promising to be used as an alternative passivation layer of the rear surface of silicon solar cells.

Figure 1 .

 1 Figure 1. Wavelength-dependant refractive index of different SiON layers according to the

Figure 2 .

 2 Figure 2. FTIR spectra of SiON layers before (a) and after (b) anneal as function of Ox ratio

Table 1 .

 1 Atomic composition of the SiON layer according to XPS measurements and analysis

	Ox ratio	Atomic composition (
		O%	N%	Si%
	0,00	6,3	40,2	53,5
	0,20	23,5	31,4	45,1
	0,58	47,1	11,9	41,0
	1,00	60,8	1,2	38,0

As expected, the increase of N 2 O flow leads to high oxygen content in the SiON layer while nitrogen is decreasing in the same proportion. On the other hand silicon concentrations remain

20 nm depth in the layer)

Acknowledgements

The authors want to thanks J. Randrianalisoa from the CETHIL for FTIR characterization.