WO2023170551A1

WO2023170551A1 - Device and method for characterising camera alignment in a photonic chip-based sensor

Info

Publication number: WO2023170551A1
Application number: PCT/IB2023/052089
Authority: WO
Inventors: Van Tri NGUYEN; Delfina FAINGUERSCH
Original assignee: Aryballe
Priority date: 2022-03-11
Filing date: 2023-03-06
Publication date: 2023-09-14
Also published as: FR3133474A1; FR3133474B1

Abstract

The invention relates to a device (20) for characterising camera alignment, the device comprising a sensor (28, 30) based on a photonic chip (32) and a camera (42) arranged facing a matrix arrangement of N columns and M rows of optical outputs (38) of the photonic chip. A processing unit (48) of said device is designed (60, 62, 64) to process an image, provided by the camera (42), of spots corresponding to the optical outputs (38). More specifically, it is designed to distribute the spots in the image into M groups corresponding to the M columns and to determine (60) said M columns in the image by means of linear regression; to distribute (62) the spots of the image into N groups corresponding to the N rows and to determine (62) said N rows in the image by means of linear regression; and to characterise (64) the camera alignment by said determined M columns and N rows in the image.

Description

Device and method for characterizing camera alignment in a photonic chip sensor

The present invention relates to a device, method and computer program for characterizing camera alignment in a photonic chip sensor. It also relates to a method of manufacturing and a method of calibrating a photonic chip sensor, including this method of characterizing a camera alignment.

un capteur à puce photonique comportant :
- une puce photonique à pluralité de sorties optiques organisées matriciellement en M colonnes et N lignes, et
- une caméra disposée face à cette organisation matricielle des sorties optiques ; et
une unité de traitement programmée pour traiter numériquement une image de taches correspondant aux sorties optiques de la puce photonique, cette image étant fournie par la caméra.

The invention applies more particularly to a device for characterizing a camera alignment comprising:

a photonic chip sensor comprising:
- a photonic chip with a plurality of optical outputs organized matrixly in M columns and N lines, and
- a camera placed facing this matrix organization of the optical outputs; And
a processing unit programmed to digitally process an image of spots corresponding to the optical outputs of the photonic chip, this image being provided by the camera.

Such a sensor is commonly used for the detection of compounds in a fluid, in particular the detection of volatile organic compounds. For example, the photonic chip may include, on an upper interaction face, an optical waveguide end intended to receive an optical signal coming from a VCSEL diode (from the English “Vertical-Cavity Surface-Emitting Laser » for vertical cavity laser diode emitting from the surface), a plurality of photonic olfactory sensors intended to be crossed by optical signals resulting from this optical signal and a plurality of ends of optical waveguides emitting optical signals having passed through the plurality of olfactory sensors, these ends corresponding to the optical outputs organized matrixly in M columns and N lines. This photonic chip implements for example a Mach-Zehnder interferometer matrix technology, called MZI technology (from the English “Mach-Zehnder Interferometer”), or more precisely an MZI technology with multimodal interference, called MZI/MMI technology ( from English “Multi Mode Interference”). This technology, notably taught in the article by Halir et al, entitled “Direct and sensitive phase readout for integrated waveguide sensors”, published in IEEE Photonics Journal, volume 5, no. 4, in August 2013, is particularly suitable for detection volatile organic compounds. By way of non-limiting example, for 64 different olfactory sensors arranged on the upper face of the photonic chip, MZI/MMI technology with a phase shift of 2Π/3 generates 3x64 = 192 optical outputs, advantageously arranged in a staggered manner on a matrix grid at M = 16 columns and N = 24 rows. The 192 optical outputs therefore occupy half of the 16x24 = 384 intersections of the M columns and N lines, in accordance with this staggered arrangement.

The camera receiving the optical signals emitted at the output of the photonic chip is generally equipped with multiple photoreceptors. This is, for example, a camera taking the form of a CCD (Charge Coupled Device) photographic sensor.

The manufacture of such a photonic chip sensor, and in particular its assembly, is delicate. This involves, on the one hand, ensuring the best possible regularity of the matrix organization, staggered or not, of the optical outputs of the photonic chip and, on the other hand, arranging the camera as precisely as possible facing this matrix organization at a certain predetermined distance. The alignment of the optical outputs and the parallelism of the camera must be able to be controlled during manufacturing but also during use of each photonic chip sensor. This is called camera alignment characterization. However, this is currently done mainly by visual and manual control.

However, there are automatic processes allowing camera alignment checks in olfactory sensors. An example is taught in the international patent application published under number WO 2021/053285 A1. But it applies in a context of direct imaging of olfactory capture sites by surface plasmon resonance, that is to say by an SPR type imaging system (from the English “Surface Plasmon Resonance”). ). It also involves the prior recording of a map of the olfactory capture sites intended to be superimposed on the image of spots which then represents a direct perception of the olfactory capture sites. This teaching consisting of updating the position and/or orientation of superposition of this card does not appear at all adapted to the specific situation of an image of tasks obtained using a photonic chip sensor. It is also not suitable for execution during the manufacture of an olfactory sensor.

It may therefore be desired to provide a device for characterizing a camera alignment for a photonic chip sensor which makes it possible to overcome at least part of the aforementioned problems and constraints.

un capteur à puce photonique comportant :
- une puce photonique à pluralité de sorties optiques organisées matriciellement en M colonnes et N lignes, et
- une caméra disposée face à cette organisation matricielle des sorties optiques ; et
une unité de traitement conçue pour traiter numériquement une image de taches correspondant aux sorties optiques de la puce photonique, cette image étant fournie par la caméra ;

dans lequel l’unité de traitement plus précisément conçue pour :

répartir au moins une partie des taches de l’image dans M groupes distincts correspondant aux M colonnes et déterminer ces M colonnes dans l’image de taches par régression linéaire ;
répartir au moins une partie des taches de l’image dans N groupes distincts correspondant aux N lignes et déterminer ces N lignes dans l’image de taches par régression linéaire ; et
caractériser l’alignement de caméra par ces M colonnes et N lignes déterminées dans l’image de taches.

A device is therefore proposed for characterizing a camera alignment comprising:

a photonic chip sensor comprising:
- a photonic chip with a plurality of optical outputs organized matrixly in M columns and N lines, and
- a camera placed facing this matrix organization of the optical outputs; And
a processing unit designed to digitally process an image of spots corresponding to the optical outputs of the photonic chip, this image being provided by the camera;

in which the processing unit more precisely designed for:

distribute at least part of the spots in the image into M distinct groups corresponding to the M columns and determine these M columns in the image of spots by linear regression;
distribute at least part of the spots in the image into N distinct groups corresponding to the N lines and determine these N lines in the spot image by linear regression; And
characterize the camera alignment by these M columns and N lines determined in the spot image.

Thus, by cleverly exploiting a priori knowledge of the matrix organization in M columns and N lines of the optical outputs of the photonic chip, we are able to automatically and above all very simply determine their projections in the image of spots which turn out to be particularly relevant for characterizing the camera parallelism of the sensor during its manufacture or use. This result obtained by linear regressions also makes it possible to judge an alignment of the optical outputs.

une puce photonique à pluralité de sorties optiques organisées matriciellement en M colonnes et N lignes ; et
une caméra disposée face à cette organisation matricielle des sorties optiques ;

le procédé comportant une étape de capture, par la caméra, d’une image de taches correspondant aux sorties optiques de la puce photonique et comportant en outre les étapes suivantes :

répartition d’au moins une partie des taches de l’image dans M groupes distincts correspondant aux M colonnes et détermination de ces M colonnes dans l’image de taches par régression linéaire ;
répartition d’au moins une partie des taches de l’image dans N groupes distincts correspondant aux N lignes et détermination de ces N lignes dans l’image de taches par régression linéaire ; et
caractérisation de l’alignement de caméra par ces M colonnes et N lignes déterminées dans l’image de taches.

A method of characterizing a camera alignment in a photonic chip sensor is also proposed, this sensor comprising:

a photonic chip with a plurality of optical outputs organized matrixly in M columns and N lines; And
a camera placed facing this matrix organization of the optical outputs;

the method comprising a step of capturing, by the camera, an image of spots corresponding to the optical outputs of the photonic chip and further comprising the following steps:

distribution of at least part of the spots in the image into M distinct groups corresponding to the M columns and determination of these M columns in the image of spots by linear regression;
distribution of at least part of the spots in the image into N distinct groups corresponding to the N lines and determination of these N lines in the image of spots by linear regression; And
characterization of the camera alignment by these M columns and N lines determined in the spot image.

une première sous-étape de binarisation par seuillage de l’image de taches ;
une deuxième sous-étape de détection des taches dans l’image binarisée ;
une troisième sous-étape de filtrage des taches détectées par un critère de taille ; et
une quatrième sous-étape de regroupement automatique des taches détectées et conservées par filtrage en M colonnes et/ou en N lignes.

Optionally, the distribution of at least part of the spots in the image in M distinct groups corresponding to the M columns and/or the distribution of at least part of the spots in the image in N distinct groups corresponding to the N lines includes:

a first sub-step of binarization by thresholding of the spot image;
a second sub-step for detecting spots in the binarized image;
a third sub-step of filtering the spots detected by a size criterion; And
a fourth sub-step of automatic grouping of the spots detected and preserved by filtering into M columns and/or N rows.

Also optionally, the detection of spots in the binarized image includes a placement of bounding boxes around these spots in the binarized image, so as to associate with each spot found parameters comprising at least the coordinates of a center of the stain and a size of the stain.

un regroupement automatique des taches détectées et conservées par filtrage en M colonnes sur la base d’abscisses des centres des taches détectées et conservées par filtrage dans l’image binarisée en minimisant des variations d’abscisses intra-classes et en maximisant des variations d’abscisses inter-classes ; et/ou
un regroupement automatique des taches détectées et conservées par filtrage en N lignes sur la base d’ordonnées des centres des taches détectées et conservées par filtrage dans l’image binarisée en minimisant des variations d’abscisses intra-classes et en maximisant des variations d’abscisses inter-classes.

Also optionally, the automatic grouping of spots detected and preserved by filtering in M columns and/or in N lines includes:

an automatic grouping of the spots detected and preserved by filtering in M columns on the basis of abscissae of the centers of the spots detected and preserved by filtering in the binarized image by minimizing variations of intra-class abscissae and maximizing variations of inter-class abscissa; and or
an automatic grouping of spots detected and preserved by filtering in N lines on the basis of ordinates of the centers of the spots detected and preserved by filtering in the binarized image by minimizing intra-class abscissa variations and maximizing variations of inter-class abscissa.

calcul d’une première déviation angulaire des M colonnes déterminées dans l’image de taches par rapport aux M colonnes de l’organisation matricielle des sorties optiques de la puce photonique ;
calcul d’une deuxième déviation angulaire des N lignes déterminées dans l’image de taches par rapport aux N lignes de l’organisation matricielle des sorties optiques de la puce photonique ; et
caractérisation de l’alignement de caméra par ces deux déviations angulaires.

Also optionally, a method for characterizing a camera alignment according to the invention may further comprise the following steps:

calculation of a first angular deviation of the M columns determined in the spot image relative to the M columns of the matrix organization of the optical outputs of the photonic chip;
calculation of a second angular deviation of the N lines determined in the spot image relative to the N lines of the matrix organization of the optical outputs of the photonic chip; And
characterization of the camera alignment by these two angular deviations.

assemblage du capteur par disposition ou ajustement d’une caméra face aux sorties optiques d’une puce photonique à pluralité de sorties optiques organisées matriciellement en M colonnes et N lignes ;
application d’un procédé de caractérisation d’un alignement de caméra selon l’invention ;
calcul d’un coefficient de parallélisme défini en tant que ratio des première et deuxième déviations angulaires calculées ; et
répétition des trois étapes précédentes tant que ce ratio n’est pas compris dans un intervalle prédéterminé autour de 1.

A method of manufacturing a photonic chip sensor is also proposed, comprising the following steps:

assembly of the sensor by arrangement or adjustment of a camera facing the optical outputs of a photonic chip with a plurality of optical outputs organized matrixly in M columns and N lines;
application of a method for characterizing a camera alignment according to the invention;
calculation of a parallelism coefficient defined as the ratio of the first and second calculated angular deviations; And
repetition of the three previous steps as long as this ratio is not included in a predetermined interval around 1.

application d’un procédé de caractérisation d’un alignement de caméra selon l’invention ;
génération d’une image de masque de référence à partir des M colonnes et N lignes déterminées dans l’image de taches, cette image de masque de référence comportant une modélisation des sorties optiques sous la forme de taches de référence aux dimensions prédéterminées centrées sur des intersections des M colonnes et N lignes déterminées dans l’image de taches ;
calcul d’un coefficient d’alignement défini en tant que ratio :
- d’une somme de surfaces partielles des taches de l’image de taches incluses dans les taches de référence par superposition de l’image de taches et de l’image de masque de référence, et
- d’une somme de surfaces totales des taches de l’image de taches ; et
répétition d’un ajustement d’au moins un paramètre d’acquisition d’une nouvelle d’image de taches et des trois étapes précédentes tant que ce ratio n’est pas supérieur à un seuil prédéterminé.

A method for calibrating a photonic chip sensor is also proposed, comprising the following steps:

application of a method for characterizing a camera alignment according to the invention;
generation of a reference mask image from the M columns and N lines determined in the spot image, this reference mask image comprising a modeling of the optical outputs in the form of reference spots with predetermined dimensions centered on intersections of the M columns and N lines determined in the spot image;
calculation of an alignment coefficient defined as a ratio:
- a sum of partial surfaces of the spots of the spot image included in the reference spots by superposition of the spot image and the reference mask image, and
- a sum of total areas of the spots of the spot image; And
repetition of an adjustment of at least one parameter for acquiring a new image of spots and of the three previous steps as long as this ratio is not greater than a predetermined threshold.

Optionally, the adjustment of said at least one acquisition parameter includes the adjustment of an exposure time of the camera.

répartition d’au moins une partie des taches de l’image dans M groupes distincts correspondant aux M colonnes et détermination de ces M colonnes dans l’image de taches par régression linéaire ;
répartition d’au moins une partie des taches de l’image dans N groupes distincts correspondant aux N lignes et détermination de ces N lignes dans l’image de taches par régression linéaire ; et
caractérisation d’un alignement de la caméra par ces M colonnes et N lignes déterminées dans l’image de taches ;

lorsque ledit programme est exécuté sur un ordinateur.There is also proposed a computer program downloadable from a communications network and/or recorded on a computer-readable medium and/or executable by a processor programmed to digitally process an image of spots corresponding to the optical outputs of a photonic chip with plurality of optical outputs organized matrixly in M columns and N lines, this image being provided by a camera arranged facing the matrix organization of the optical outputs of the photonic chip, the computer program comprising instructions for executing the following steps :

distribution of at least part of the spots in the image into M distinct groups corresponding to the M columns and determination of these M columns in the image of spots by linear regression;
distribution of at least part of the spots in the image into N distinct groups corresponding to the N lines and determination of these N lines in the image of spots by linear regression; And
characterization of an alignment of the camera by these M columns and N lines determined in the spot image;

when said program is executed on a computer.

la représente schématiquement la structure générale d’un dispositif de caractérisation d’un alignement de caméra, selon un mode de réalisation de l’invention,
la représente schématiquement en vue de côté la structure générale d’un capteur à puce photonique du dispositif de la ,
la représente schématiquement en vue de dessus un exemple d’organisation matricielle de sorties optiques de la puce photonique du capteur de la ,
la illustre un exemple d’image de taches en niveaux de gris pouvant être produite par une caméra du capteur à puce photonique de la ,
la illustre un exemple de superposition de diagrammes temporels de signaux de réponse, ou sensorgrammes, pouvant être obtenus par le dispositif de la , dans des conditions maîtrisées de mesure multivariée,
la illustre, sous la forme d’un diagramme circulaire, un exemple de signature olfactive normée pouvant être calculée par le dispositif de la à partir de sensorgrammes tels que ceux de la ,
la illustre les étapes successives d’un procédé de caractérisation d’un alignement de caméra, selon un mode de réalisation de l’invention,
la illustre le résultat d’une binarisation de l’image de taches de la ,
la illustre le résultat d’une recherche et d’une détermination de zones d’intérêt dans l’image binarisée de la ,
la illustre le résultat d’une détermination de M colonnes par régression linéaire dans l’image binarisée de la ,
la illustre le résultat d’une détermination de N lignes par régression linéaire dans l’image binarisée de la ,
la illustre la superposition de l’image binarisée de la et d’une image de masque résultant des M colonnes et N lignes des figures 10 et 11,
la illustre les étapes successives d’une sous-étape de regroupement automatique du procédé de la , selon un mode de réalisation de l’invention,
la illustre les étapes successives d’un procédé de fabrication d’un capteur à puce photonique, selon un mode de réalisation de l’invention, et
la illustre les étapes successives d’un procédé d’étalonnage d’un capteur à puce photonique, selon un mode de réalisation de l’invention.

The invention will be better understood with the help of the description which follows, given solely by way of example and made with reference to the appended drawings in which:

there schematically represents the general structure of a device for characterizing a camera alignment, according to one embodiment of the invention,
there schematically represents in side view the general structure of a photonic chip sensor of the device of the ,
there schematically represents in top view an example of matrix organization of optical outputs of the photonic chip of the sensor of the ,
there illustrates an example of a grayscale spot image that can be produced by a camera of the photonic chip sensor of the ,
there illustrates an example of superposition of temporal diagrams of response signals, or sensorgrams, which can be obtained by the device of the , under controlled multivariate measurement conditions,
there illustrates, in the form of a circular diagram, an example of a standardized olfactory signature that can be calculated by the device of the from sensorgrams such as those of the ,
there illustrates the successive stages of a method for characterizing a camera alignment, according to one embodiment of the invention,
there illustrates the result of a binarization of the image of spots of the ,
there illustrates the result of a search and determination of areas of interest in the binarized image of the ,
there illustrates the result of a determination of M columns by linear regression in the binarized image of the ,
there illustrates the result of a determination of N lines by linear regression in the binarized image of the ,
there illustrates the superposition of the binarized image of the and a mask image resulting from the M columns and N rows of Figures 10 and 11,
there illustrates the successive stages of an automatic grouping sub-stage of the method of , according to one embodiment of the invention,
there illustrates the successive stages of a process for manufacturing a photonic chip sensor, according to one embodiment of the invention, and
there illustrates the successive steps of a method for calibrating a photonic chip sensor, according to one embodiment of the invention.

The device 20 for multivariate measurement of the presence of compounds present in a fluid represented schematically on the , also fulfilling a function of characterizing a camera alignment according to a possible embodiment of the present invention, is a non-limiting example of a device integrating a photonic chip sensor for a non-limiting application of odor identification. It comprises a measuring chamber 22 intended to receive a fluid to be analyzed, for example a gas such as ambient air. To do this, it includes a suction device 24 designed to suck in the air located inside the measuring chamber 22 and let it exit outside. It further comprises an air inlet 26 which can be selectively closed to keep the ambient air in the measuring chamber 22 or open to allow the evacuation of the ambient air from the measuring chamber 22 and its renewal by the activation of the suction device 24. It is thus equipped with means for controlling incoming and outgoing flows.

In its measuring chamber 22, the multivariate measuring device 20 comprises several olfactory sensors 28, distributed respectively over as many reactive sites, for example around sixty, designed to interact with compounds likely to be present in the measuring chamber 22 when the device 20 is placed near a fluid to be analyzed emitting these compounds, particularly when the air inlet 26 is close to the fluid in question. The compounds emitted are generally volatile organic compounds but the present invention is not limited to such compounds.

Each olfactory sensor 28 is itself, for example, a biosensor designed to interact with compounds from a particular family of volatile organic compounds. In practice, each olfactory sensor 28 may comprise a molecule, such as a peptide immobilized on a substrate or a polymer covering a surface, complementary to the compounds of the family associated with this olfactory sensor 28.

Alternatively, the multivariate measuring device 20 could be adapted to be brought into contact with any fluid, liquid or gas, other than ambient air. According to a particularly simple version, it could also not include the suction device 24 and the air inlet 26, or even the measuring chamber 22. In this simple version, the olfactory sensors 28 are then likely to be directly placed in contact with the fluid to be analyzed without flow control.

The general idea remains to functionalize reactive sites using olfactory sensors 28 (ie biosensors, polymers, carbon nanotubes, etc.) in such a way that they adsorb and desorb volatile organic compounds in a differentiated manner, forming a differentiated molecular interaction response of the olfactory sensors, and amplifying the response in the form of a sequence of electrical measuring signals S using a physical transduction device.

The olfactory sensors 28 are therefore associated with at least one transducer 30 with which they interact. This transducer 30 is arranged and configured to measure any change in physical property caused by an interaction of the olfactory sensors 28 with the fluid to be analyzed. It provides the sequence of electrical measurement signals S which characterizes this fluid since it is representative of the volatile organic compounds with which the olfactory sensors 28 can interact in the measuring chamber 22.

More precisely in the technical context of the present invention, the transducer 30 is a photonic chip system, advantageously an optical index variation amplification system by Mach-Zehnder interferometry, for example according to interferometer matrix technology. of Mach-Zehnder, called MZI technology (from the English “Mach-Zehnder Interferometer”), or more precisely an MZI technology with multimodal interference, called MZI/MMI technology (from the English “Multi Mode Interference”). Such a system is configured to measure any change in a refractive index due to an interaction of the fluid studied with at least one of the olfactory sensors 28, each olfactory sensor being positioned on two arms of the same interferometer, thanks to a detectable phase shift between one of the two arms called the reference arm of the interferometer relating to this arbitrary olfactory sensor and the other of the two arms called the detection arm of the interferometer on which the reactive site of this olfactory sensor whatever is arranged. The resulting photonic chip transducer provides the sequence of measurement signals S which is in the form of a sequence of images of spots materializing the phase shifts, expressed in Radian, of the olfactory sensors 28. The assembly made up of the sensors olfactory 28 and the transducer 30 form a photonic chip sensor.

As illustrated on the , the transducer 30 comprises more precisely a photonic chip 32 whose optical elements are represented very schematically for a functional understanding of their arrangement. According to a preferred embodiment, it comprises a lower face 32A for placing on a support and an upper face 32B with an optical waveguide end 34 receiving an optical signal O, with a plurality of interferometers 36 forming respective supports for the olfactory sensors 28 and intended to be crossed by optical signals from the optical signal O, and with a plurality 38 of ends of optical waveguides emitting optical signals having passed through the plurality of interferometers 36. These ends 38 form the optical outputs of the photonic chip 32 and are organized matrixly in M columns and N rows.

A staggered matrix organization of the plurality of optical outputs 38 is illustrated on the . In the aforementioned example of 64 interferometers 36 and as many different olfactory sensors 28 arranged on the upper face 32B of the photonic chip 32, the MZI/MMI technology with a phase shift of 2Π/3 generates 192 optical outputs 38, arranged here in a staggered manner on a matrix grid with M = 16 columns C ₁ , …, C _M and N = 24 lines L ₁ , …, L _N . Note that the distance between the columns C ₁ , …, C _M has no reason to be equal to that between the rows L ₁ , …, L _N . In the example shown, the columns are further apart than the rows.

Back to the , the transducer 30 further comprises a VCSEL diode 40 which produces the optical signal O and is optically connected to the receiving end 34 of the photonic chip 32. Finally, the transducer 30 comprises a CCD camera 42 provided with multiple photoreceptors 44, arranged facing the matrix organization of the plurality of optical outputs 38 at a predefined distance. The interferometers 36 with olfactory sensors 28 of the photonic chip 32 are arranged in the central part of its upper face 32B, in contact with any fluid to be analyzed in the measuring chamber 22, while the receiving end 34 and the plurality of optical outputs 38 are on the outskirts.

An example of a grayscale spot image IM that can be produced by the CCD camera 42 is illustrated in the . The CCD camera 42 of the transducer 30 is able to output an entire sequence S of such images when a fluid to be analyzed is introduced into the measuring chamber 22.

Back to the , the multivariate measuring device 20 further comprises several functional modules which will be described below. In the example described, these modules are software in nature. Thus, the device 20 comprises a computer type element 46 comprising a processing unit (for example a processor) 48 and an associated memory area 50 (for example a RAM memory or other) in which several computer programs or several functions of the same computer program are recorded. These computer programs include instructions designed to be executed by the processing unit 48 in order to carry out the functions of the software modules. They are presented as distinct, but this distinction is purely functional. They could just as easily be grouped in all possible combinations into one or more software programs. Their functions could also be at least partly micro-programmed or micro-wired into dedicated integrated circuits, such as digital circuits. Thus, as a variant, the computer 46 could be replaced by an electronic device composed solely of digital circuits (without a computer program) for carrying out the same functions.

The multivariate measuring device 20 thus comprises first of all a software module 52, intended to be executed by the processing unit 48, for controlling the suction device 24 (if it is provided in the device 20), the air inlet 26 (if it is also provided in device 20) and the transducer 30.

It further comprises in an optional but advantageous manner a software module 54, intended to be executed by the processing unit 48, for selection, among the olfactory sensors 28 of the multivariate measuring device 20, of a subset of sensitive sensors to volatile components characteristic of a sought-after olfactory imprint. These characteristic volatile components can vary from one application or fluid studied to another so that the selection of olfactory sensors made by the software module 54 can also vary and be parameterized. The selected subset comprises for example M ≥ 1 olfactory sensor(s), in particular advantageously several olfactory sensors (M ≥ 2).

The multivariate measuring device 20 further comprises a software module 56, intended to be executed by the processing unit 48, to extract M sensorgrams respectively representative of the interactions of the M olfactory sensors selected with the volatile organic compounds concerned from the values specific to these M olfactory sensors in the sequence of images S provided by the transducer 30. These sensorgrams take the form of phase shift signals and are expressed in Radian since the transducer 30 is an MZI or MZI/MMI amplification system.

les capteurs olfactifs 28 sont tout d’abord exposés à un environnement fluidique de référence à fluide porteur sans présence des composés cibles d’un fluide à analyser au cours d’un premier état de référence identifiable par une première portion PH1 des sensorgrammes,
ils sont ensuite exposés au fluide à analyser au cours d’un deuxième état analytique d’adsorption déclenché par une injection maîtrisée de ce fluide dans la chambre de mesure 22, ce deuxième état étant identifiable par une deuxième portion PH2 des sensorgrammes, et
ils sont finalement exposés de nouveau à l’environnement fluidique de référence au cours d’un troisième état final de désorption par une évacuation maîtrisée du fluide à analyser hors de la chambre de mesure 22, ce troisième état étant identifiable par une troisième portion PH3 des sensorgrammes.

Figure 5 thus illustrates the superimposed temporal diagrams of around sixty sensorgrams obtained using an MZI or MZI/MMI amplification system such as the transducer 30 over a period of approximately 190 seconds according to a known and well-controlled measurement protocol, involving control of the suction device 24 and of the air inlet 26, in which:

the olfactory sensors 28 are first exposed to a reference fluid environment with a carrier fluid without the presence of the target compounds of a fluid to be analyzed during a first reference state identifiable by a first portion PH1 of the sensorgrams,
they are then exposed to the fluid to be analyzed during a second analytical adsorption state triggered by a controlled injection of this fluid into the measuring chamber 22, this second state being identifiable by a second portion PH2 of the sensorgrams, and
they are finally exposed again to the reference fluidic environment during a third final state of desorption by a controlled evacuation of the fluid to be analyzed from the measuring chamber 22, this third state being identifiable by a third portion PH3 of the sensorgrams.

Returning to Figure 1, the multivariate measuring device 20 further comprises a software module 58, intended to be executed by the processing unit 48, to obtain in a well-known and non-detailed manner a GIS characterization of the composition of the fluid to analyze from M sensorgrams . This GIS characterization can take the form of a standardized olfactory signature as illustrated in the in the form of a circular diagram.

In accordance with the general principles of a possible embodiment of the present invention, the multivariate measuring device 20 further fulfills the aforementioned function of characterizing an alignment of the CCD camera 42 with respect to the plurality of optical outputs 38.

For this, the processing unit 48 which receives the sequence of images S is capable of extracting at least one, for example the image IM of the , to process it digitally.

The multivariate measuring device 20 then comprises a software module 60, intended to be executed by the processing unit 48, to distribute at least part of the spots of the image IM into M distinct groups corresponding to the M columns according to a method of automatic classification which will be detailed with reference to the , determine these M columns in the IM spot image by linear regression and, optionally but advantageously, calculate a first angular deviation α with respect to the M columns of the matrix organization of the optical outputs 38 of the photonic chip 32.

It further comprises a software module 62, intended to be executed by the processing unit 48, to distribute at least part of the spots of the IM image into N distinct groups corresponding to the N lines according to an automatic classification method which will be detailed according to different variants with reference to Figures 7 and 13, determine these N lines in the image of spots IM by linear regression and, optionally but advantageously, calculate a second angular deviation β with respect to the N lines of the matrix organization optical outputs 38 of the photonic chip 32.

It finally includes a software module 64, intended to be executed by the processing unit 48, to characterize the camera alignment by these M columns and N lines determined in the image of spots IM, and in particular by comparing their respective angular deviations α and β, according to a method of calculating score(s) which will be detailed with reference to the . The first or second angular deviation, α or β, effectively makes it possible to characterize a possible problem of positioning the CCD camera 42 around an axis orthogonal to the plane of the plurality of optical outputs 38, while a comparison of these two deviations angular, for example the ratio α/β, makes it possible to characterize a possible problem of positioning the CCD camera 42 around at least one axis parallel to the plane of the plurality of optical outputs 38.

According to another possible embodiment conforming to the general principles of the present invention, the function of characterizing an alignment of the CCD camera 32 can be performed software by a computer 66 external to the multivariate measuring device 20. In this case, it it is enough that the computer 66 can receive the image of spots IM and has software means to process it, in particular a memory capable of storing the software modules 60, 62 and 64 and a processor capable of executing them. In this case also, these software modules may not be recorded in memory area 50. The device for characterizing a camera alignment then consists of at least the photonic chip sensor 28, 30 and the computer 66.

A method of characterizing a camera alignment implemented by the multivariate measuring device 20 of the will now be detailed with reference to the . This process could just as easily be implemented by the computer 66.

During a first step 100, an image of spots in gray levels is captured by the CCD camera 42 and supplied to the processing unit 48. This is for example the image IM of the which is a representation of the 192 optical outputs 38 distributed staggered over M = 16 columns and N = 24 lines of an arrangement matrix on the upper face 32B of the photonic chip 32.

During a following step 102 carried out by execution of the software module 60, the spots of the image IM are distributed into M = 16 distinct groups corresponding to the M = 16 columns of the matrix organization of the optical outputs 38. An example not limiting implementation of such a distribution, but advantageous in terms of simplicity and efficiency, comprises a first sub-step 102-1 of binarization by thresholding of the IM image, a second sub-step 102-2 of spot detection in the binarized image, a third sub-step 102-3 of filtering the spots detected by a size criterion, a fourth sub-step 102-4 of automatically grouping the spots detected and preserved by filtering in M columns.

In the example described, the first substep 102-1 firstly comprises thresholding of the IM image. An optimal threshold is thus chosen by analyzing its histogram. For example, the threshold selection method described in Otsu's article, entitled “A threshold selection method from gray-level histograms”, published in IEEE Transactions on Systems, Man, and Cybernetics, Vol. SMC-9, No. 1, in January 1979, is used. Thus, the optimal threshold is determined by minimizing an intra-class intensity variance, or equivalently by maximizing an inter-class intensity variance. All pixels which have a luminance (ie a gray level) lower than this threshold are considered to belong to the background and set to a first minimum binary value while all pixels which have a luminance greater than or equal to this threshold are considered to belong to a spot and set to a second maximum binary value. We thus obtain the binarized image IMb of the in which the spots are shown in white and the background in black.

In the example also described, the second substep 102-2 comprises a search and a determination of areas of interest in the binarized image IMb. These areas of interest are the 192 spots corresponding to the 192 optical outputs 38 of the photonic chip 32. This can be done by a known method of searching and placing bounding boxes such as that carried out by executing the “regionprops” function of the “skimage” digital library. Not all stains are necessarily found during this operation. Additionally, artifacts may be detected as forming spots by mistake. On the other hand, each spot found is associated with parameters obtained in a manner known in itself thanks to the bounding box which surrounds and corresponds to it: coordinates of a center of the spot, size of the spot, minor principal axis, major axis main, possibly orientation. A representation of bounding boxes associated with the spots of the binarized image IMb of the is illustrated in the . The corresponding parameters are expressed in a reference frame (x,y) with horizontal (abscissa) and vertical (ordinate) coordinates.

The third substep 102-3 then includes a filtering of the spots detected in the form of bounding boxes by a size criterion. A statistical calculation of AREA_MEAN mean and AREA_STD standard deviation is for example carried out on the size parameters obtained in the previous sub-step and all the spots presenting a size which does not fall within the range of values [AREA_MEAN - AREA_STD ; AREA_MEAN + AREA_STD] are excluded by filtering. Other size criteria may be taken into consideration to exclude stains. For example, an analysis of a diagram of increasing sizes of the spots found can extract at least one of two values MIN_AREA and MAX_AREA corresponding to two possible points of maximum curvature of this diagram. In this case, it can be considered that if MIN_AREA is less than AREA_MEAN - AREA_STD then we only exclude by lower filtering the spots of size less than MIN_AREA and if MAX_AREA is greater than AREA_MEAN + AREA_STD then we exclude by higher filtering than spots larger than MAX_AREA. Depending on the case, this results in an exclusion by filtering of all the spots having a size which does not fall within the range [AREA_MEAN - AREA_STD; AREA_MEAN + AREA_STD], or in the range [MIN_AREA; AREA_MEAN + AREA_STD], or in the range [AREA_MEAN - AREA_STD; MAX_AREA], or in the range [MIN_AREA; MAX_AREA].

In the example also described, the fourth sub-step 102-4 uses a method of automatic grouping of spots by considering the a priori knowledge of M groups of columns corresponding to the M known columns of the matrix organization of the optical outputs 38 of the photonic chip 32. This grouping is advantageously carried out on the basis of the abscissae of the centers of the spots detected and preserved by filtering in the binarized image IMb by minimizing intra-class abscissa variations and maximizing inter-class abscissa variations. classes. This principle is for example implemented in the Jenks optimization method (“Jenks optimization method” or “Jenks natural breaks classification method”) applicable here.

During a following step 104 carried out by execution of the software module 60, the M columns in which the spots have been distributed by automatic grouping are concretely determined in the form of straight lines in the image IM or in the binarized image IMb by regression linear. For each of the M columns to be determined, this linear regression is advantageously carried out on the basis of the coordinates of the centers of the spots having been grouped in this column. This can for example be done by executing the “polyfit” function of the “numpy” digital library. We obtain the binarized image IMb of the in which the M columns C ₁ , …, C _M are represented as determined.

During a following step 106 carried out by execution of the software module 60, a first angular deviation α of the M columns thus determined in the image IM or in the image IMb relative to the M columns of the matrix organization of the optical outputs 38 of the photonic chip 32 is calculated. This involves, for example, calculating an average angular deviation of the M columns determined in the image IM or in the image IMb relative to a vertical reference axis of the image IM or the image IMb such that y axis of the ordinates.

During a step 108 carried out by execution of the software module 62, the spots of the image IM are distributed into N = 24 distinct groups corresponding to the N = 24 lines of the matrix organization of the optical outputs 38. It can be carried out as for step 102, and independently of the latter, in four sub-steps: a first sub-step 108-1 of binarization by thresholding of the IM image identical to sub-step 102-1, a second sub-step step 108-2 of detecting spots in the binarized image identical to substep 102-2, a third substep 108-3 of filtering the spots detected by a size criterion identical to substep 102-3 , a fourth sub-step 108-4 for automatic grouping of spots detected and preserved by filtering into N lines.

If step 108 is executed after steps 102, 104, 106, or in any case at least after step 102 as is the case in the example illustrated by , substeps 108-1, 108-2 and 108-3 do not need to be executed, as indicated in broken lines in the . It is enough to exploit the result of substep 102-3. As for the fourth sub-step 108-4, in a possible embodiment when the optical output lines 38 are sufficiently distant from each other, it can proceed from the same principle as step 102-4, that is- i.e. by automatic grouping operating on the basis of the ordinates of the centers of the spots detected and preserved by filtering in the binarized image IMb by minimizing intra-class ordinate variations and maximizing inter-class ordinate variations according to the Jenks optimization method.

But when the optical output lines 38 are much closer together than the columns, it is actually advantageous to execute step 108 after steps 102, 104 and 106 to exploit the result of step 106, namely the knowledge of the first angular deviation α of the columns determined in the image IM or in the binarized image IMb. Indeed, the rapprochement of the lines between them and the first angular deviation α can be such that two spots whose centers have very close or even identical ordinates can in fact belong to different lines taking into account a slight rotation of the organization matrix of the optical outputs 38 in the image IM. In this case, a variant is proposed for substep 108-4. According to a first fairly general possibility, this variant can include a prior vertical angular registration of the image IM or the binarized image IMb according to an angle -α opposite to the first angular deviation α, to then proceed from the same principle as the step 102-4. But if this is not sufficient to obtain a satisfactory result, other possibilities specific to the matrix organization of the optical outputs 38 can be considered. In the very specific example of the , in which the optical outputs are arranged in a staggered manner along lines that are much closer together than the columns, a specific and clever method for executing substep 108-4 is proposed. It will be detailed later with reference to the .

During a following step 110 carried out by execution of the software module 62, the N lines in which the spots have been distributed by automatic grouping are concretely determined in the form of straight lines in the image IM or in the binarized image IMb by regression linear. For each of the N lines to be determined, this linear regression is advantageously carried out on the basis of the coordinates of the centers of the spots having been grouped in this line. This can for example be done by executing the “polyfit” function of the “numpy” digital library. We obtain the binarized image IMb of the in which the N lines L ₁ , …, L _N are represented as determined.

During a following step 112 carried out by execution of the software module 62, a second angular deviation β of the N lines thus determined in the image IM or in the binarized image IMb relative to the N lines of the matrix organization of the outputs optics 38 of the photonic chip 32 is calculated. This involves, for example, calculating an average angular deviation of the N lines determined in the image IM or in the binarized image IMb relative to a horizontal reference axis of the image IM or the image IMb such that l x-axis of abscissa.

le parallélisme est acceptable si et seulement si σ_p ≤ Sp ≤ 1/σ_p, avec σ_p proche de 1 par valeur inférieure, par exemple égal à 0,95.

During a following step 114 carried out by execution of the software module 64, a parallelism score Sp is calculated, for example Sp = α/β. In addition, a threshold value σ _p can be predetermined to judge good parallelism between the matrix organization of the optical outputs 38 and the CCD camera 42, according to the following criterion:

parallelism is acceptable if and only if σ _p ≤ Sp ≤ 1/σ _p , with σ _p close to 1 by lower value, for example equal to 0.95.

During a following step 116 carried out by execution of the software module 64, a mask image IMref is generated from the M columns and N lines determined by linear regressions in the image IM or in the binarized image IMb. Reference spots with predetermined dimensions are arranged there, being centered on the intersections of lines and columns corresponding to the optical outputs 38 of the photonic chip 32, that is to say staggered in the example of the . These reference spots are for example elliptical and of the same dimensions. The two images IMref and IMb are then superimposed to match the reference elliptical spots and the spots of the binarized image IMb as illustrated by the .

During a following step 118 carried out by execution of the software module 64, an alignment score Sa is then calculated. This calculation consists first of all in calculating for each spot denoted Ti of the binarized image IMb: (1) the total surface area of this spot, denoted Si and for example expressed in the number of white pixels belonging to this spot; (2) the partial surface of this spot included in the corresponding elliptical reference spot in the mask image IMref by superposition, denoted δSi and for example also expressed in the number of white pixels belonging to this spot and to the elliptical spot of corresponding reference. We obtain the Sa score by the following equation:

We observe that the score Sa is always included in the interval [0; 1]. The closer it is to 1, the better the alignment, the closer it is to 0, the worse the alignment. A predetermined threshold value for acceptability of the alignment can be set, for example at σ _a = 0.6.

Note that steps 114, 116 and 118 are two non-limiting examples of exploitation of the two angular deviations α and β calculated in steps 106 and 112. More generally, the calculation of these two angular deviations makes it possible to characterize the camera alignment in accordance with the general principles of the present invention. Even more generally, the determination of the M columns and N lines in the image of spots by linear regressions makes it possible to characterize the camera alignment in accordance with the essential principles of the present invention. These essential principles are the executions of steps 102, 104, 108 and 110 mentioned above.

As mentioned previously, the illustrates a possible and very specific embodiment of the automatic grouping sub-step 108-4 when the optical outputs 38 are arranged in a staggered manner along lines that are significantly closer together than the columns and in accordance with the arrangement shown schematically in the .

During a first step 200 of this sub-step 108-4, only the even columns of spots detected in sub-step 102-4 are selected to take into account the staggered arrangement of the optical outputs 38 and thus detect the odd lines of spots. A priori knowledge of the arrangement of the odd lines is used to group the detected spots, such as: the distance d between two successive odd lines of the optical outputs; the expected ordinate y ₀ of the first spot in each even column, that is to say the expected ordinate of each spot in the first odd row. Furthermore, a tolerance Δy around a target spot ordinate in each even column is predefined, an odd row index i is initialized to 0 and all the spots of the binarized image IMb are selectable.

During a following step 202, the selectable spot with the smallest ordinate of each even column is selected. It is verified for this spot that its y ordinate is indeed between y ₀ + idtan(α) - Δy and y ₀ + idtan(α) + Δy. If this is the case, the task considered is classified in the (i+1)-th odd line and deleted from the list of selectable tasks. If this is not the case, it is not classified in the (i+1)-th odd line but remains in the list of selectable tasks.

During a following step 204, the index i is incremented by one unit.

During a following test step 206, as long as the index i _MAX = N/2 of the last odd line has not been reached, we return to step 202. Otherwise we go to a step 208.

Step 208 is similar to step 200, but only the odd columns of spots detected in substep 102-4 are selected to take into account the staggered arrangement of the optical outputs 38 and thus detect the even rows of spots . Again, a priori knowledge of the arrangement of the even lines is used to group the detected spots, such as: the distance d between two successive even lines of the optical outputs; the expected ordinate y ₀ of the first spot in each odd column, that is to say the expected ordinate of each spot in the first even row. Furthermore, a tolerance Δy around a target spot ordinate in each odd column is predefined, an index i of even row is initialized to 0 and all the spots of the binarized image IMb are selectable.

During a following step 210 similar to step 202, the selectable spot with the smallest ordinate of each odd column is selected. It is verified for this spot that its y ordinate is indeed between y ₀ + idtan(α) - Δy and y ₀ + idtan(α) + Δy. If this is the case, the task considered is classified in the (i+1)-th even line and deleted from the list of selectable tasks. If this is not the case, it is not classified in the (i+1)-th even line but remains in the list of selectable tasks.

During a following step 212 identical to step 204, the index i is incremented by one unit.

During a following test step 214 similar to step 206, as long as the index i _MAX = N/2 of the last even line has not been reached, we return to step 210. Otherwise the automatic grouping substep 108-4 is considered completed (final step 216).

A process for manufacturing a photonic chip sensor will now be detailed with reference to the , according to a possible embodiment of the present invention.

During a first step 300, the photonic chip sensor 28, 30 is assembled in particular by arrangement of the CCD camera 42 facing the optical outputs 38 organized matrixly in M columns and N lines on the upper face 32B of the photonic chip 32 .

During a following step 302, an image of spots is acquired, such as the image IM of the .

During a following step 304, steps 102 to 112 of the method of characterizing an alignment of the CCD camera 42 of the are executed to obtain the angular deviations α and β.

During a following step 306, step 114 of the method of is executed to obtain the parallelism score Sp.

During a following test step 308, the parallelism score Sp is compared to the interval [σ _p ; 1/ _σp ]. If Sp is not in this interval, then the manufacturing process returns to step 300 to improve the positioning of the CCD camera 42 relative to the optical outputs 38 by adjusting its arrangement. If Sp is in this interval, then the manufacturing process passes to a step 310 of information about the good parallelism of the CCD camera 42 and the end of the camera adjustment for continued manufacturing of the photonic chip sensor of manner known per se.

A photonic chip sensor calibration method will now be detailed with reference to the , according to a possible embodiment of the present invention.

During a first step 400, an image of spots is acquired, such as the IM image of the .

During a following step 402, steps 102 to 112 of the method of characterizing an alignment of the CCD camera 42 of the are executed to obtain the angular deviations α and β.

During a following step 404, steps 114, 116 and 118 of the method of are executed to obtain the alignment score Sa and the parallelism score Sp.

During a following test step 406, the alignment score Sa is compared to the threshold value σ _a = 0.6. If Sa is strictly less than σ _a , then the calibration process passes to a step 408 of adjusting at least one parameter for acquiring a new image of spots then returns to step 400. An example of acquisition parameter which can be adjusted is the exposure time of the CCD camera 42. As a non-limiting example, for the acquisition of an image at 256 gray levels, it can be a priori adjusted to reach a maximum gray level value of the pixels of the IM spot image at 150. It can then be adjusted upwards in step 408 to reach a maximum value of 170 or more, while setting an upper limit at 200 for example.

If Sa is greater than or equal to σ _a during test 406, then the calibration process passes to a step 410 of informing of the correct alignment of the optical outputs in the spot image acquired in step 400 and the end of calibration.

During a test step 412 which also follows step 404, the parallelism score Sp is compared to the interval [σ _p ; 1/ _σp ]. If Sp is not in this interval, then the calibration process passes to a step 414 of information about the poor parallelism of the CCD camera 42 with a view to possible readjustment of the latter. If Sp is in this interval, then the calibration process passes to a step 416 of informing of the correct parallelism of the CCD camera 42.

It clearly appears that a device/process for characterizing a camera alignment such as that described above makes it possible to automatically and above all very simply characterize the parallelism of the camera of a photonic chip sensor during its manufacture or use, as well as as the correct alignment of the optical outputs of the photonic chip.

It should also be noted that the invention is not limited to the embodiments described above. It will indeed appear to those skilled in the art that various modifications can be made to the embodiments described above, in light of the teaching which has just been disclosed to him. In the detailed presentation of the invention which is made previously, the terms used should not be interpreted as limiting the invention to the embodiments set out in the present description, but must be interpreted to include all the equivalents for which the prediction is within the reach of those skilled in the art by applying their general knowledge to the implementation of the teaching which has just been disclosed to them.

Claims

Device (20) for characterizing a camera alignment comprising:

a photonic chip sensor (28, 30) comprising:

a photonic chip (32) with a plurality of optical outputs (38) organized matrixly in M columns and N lines, and

a camera (42) arranged facing this matrix organization of the optical outputs (38); And

a processing unit (48) designed (60, 62, 64) to digitally process an image of spots (IM) corresponding to the optical outputs (38) of the photonic chip (32), this image being provided by the camera (42) ;

characterized in that the processing unit (48) is more precisely designed to:

distribute (60) at least part of the spots in the image (IM) into M distinct groups corresponding to the M columns and determine (60) these M columns in the image of spots (IM) by linear regression;

distribute (62) at least part of the spots in the image (IM) into N distinct groups corresponding to the N lines and determine (62) these N lines in the spot image (IM) by linear regression; And

characterize (64) the camera alignment by these M columns and N lines determined in the spot image (IM).
Method for characterizing camera alignment in a photonic chip sensor (28, 30), this sensor comprising:

a photonic chip (32) with a plurality of optical outputs (38) organized matrixly in M columns and N lines; And

a camera (42) arranged facing this matrix organization of the optical outputs (38);

the method comprising a step of capturing (100), by the camera (42), an image of spots (IM) corresponding to the optical outputs (38) of the photonic chip (32) and being characterized in that it comprises in addition the following steps:

distribution (102) of at least part of the spots of the image (IM) in M distinct groups corresponding to the M columns and determination (104) of these M columns in the image of spots (IM) by linear regression;

distribution (108) of at least part of the spots of the image (IM) in N distinct groups corresponding to the N lines and determination (110) of these N lines in the image of spots (IM) by linear regression; And

characterization (114, 116, 118) of the camera alignment by these M columns and N lines determined in the spot image (IM).
Method for characterizing a camera alignment according to claim 2, in which the distribution (102) of at least part of the spots of the image (IM) in M distinct groups corresponding to the M columns and/or the distribution ( 108) of at least part of the spots of the image (IM) in N distinct groups corresponding to the N lines includes:

a first sub-step (102-1, 108-1) of binarization by thresholding of the spot image (IM);

a second sub-step (102-2, 108-2) for detecting spots in the binarized image (IMb);

a third sub-step (102-3, 108-3) of filtering the spots detected by a size criterion; And

a fourth sub-step (102-4, 108-4) for automatically grouping the spots detected and preserved by filtering into M columns and/or N lines.
A method of characterizing a camera alignment according to claim 3, wherein the detection (102-2, 108-2) of spots in the binarized image (IMb) comprises placing bounding boxes around these spots in the binarized image (IMb), so as to associate with each spot found parameters comprising at least the coordinates of a center of the spot and a size of the spot.
Method for characterizing a camera alignment according to claim 4, in which the automatic grouping (102-4, 108-4) of the spots detected and preserved by filtering in M columns and/or in N lines comprises:

an automatic grouping (102-4) of the spots detected and preserved by filtering in M columns on the basis of abscissae of the centers of the spots detected and preserved by filtering in the binarized image (IMb) by minimizing variations of intra-abscissae classes and maximizing inter-class abscissa variations; and or

an automatic grouping (108-4) of the spots detected and preserved by filtering in N lines on the basis of ordinates of the centers of the spots detected and preserved by filtering in the binarized image (IMb) by minimizing intra-abscissa variations classes and maximizing inter-class abscissa variations.
Method for characterizing a camera alignment according to any one of claims 2 to 5, further comprising the following steps:

calculation (106) of a first angular deviation of the M columns determined in the spot image (IM) relative to the M columns of the matrix organization of the optical outputs (38) of the photonic chip (32);

calculation (112) of a second angular deviation of the N lines determined in the spot image (IM) relative to the N lines of the matrix organization of the optical outputs (38) of the photonic chip (32); And

characterization (114) of the camera alignment by these two angular deviations.
Method for manufacturing a photonic chip sensor (28, 30), comprising the following steps:

assembly (300) of the sensor (28, 30) by arrangement or adjustment of a camera (42) facing the optical outputs (38) of a photonic chip (32) with a plurality of optical outputs (38) organized matrixly in M columns and N lines;

application (302, 304) of a method for characterizing a camera alignment according to claim 6;

calculation (306) of a parallelism coefficient defined as the ratio of the first and second calculated angular deviations; And

repetition of the three previous steps (300, 302, 304, 306) as long as this ratio is not included in a predetermined interval around 1.
Method for calibrating a photonic chip sensor (28, 30), comprising the following steps:

application (400, 402) of a method for characterizing a camera alignment according to any one of claims 2 to 6;

generation (404) of a reference mask image (IMref) from the M columns and N lines determined in the spot image (IM), this reference mask image (IMref) comprising a modeling of the optical outputs under the shape of reference spots with predetermined dimensions centered on intersections of the M columns and N lines determined in the spot image (IM);

calculation (404) of an alignment coefficient defined as a ratio:

a sum of partial areas of the spots of the spot image (IM) included in the reference spots by superposition of the spot image (IM) and the reference mask image (IMref), and

a sum of total areas of the spots of the spot image (IM); And

repetition of an adjustment (408) of at least one parameter for acquiring a new spot image (IM) and of the three previous steps (400, 402, 404) as long as this ratio is not greater at a predetermined threshold.
Method for calibrating a photonic chip sensor (28, 30) according to claim 8, in which the adjustment (408) of said at least one acquisition parameter comprises the adjustment of an exposure time of the camera (42).
Computer program (60, 62, 64) downloadable from a communications network and/or recorded on a computer-readable medium (50) (46; 66) and/or executable by a processor (48) programmed to digitally process a image of spots (IM) corresponding to the optical outputs (38) of a photonic chip (32) with a plurality of optical outputs (38) organized matrixly in M columns and N lines, this image (IM) being provided by a camera (42 ) arranged facing the matrix organization of the optical outputs (38) of the photonic chip (32), the computer program (60, 62, 64) being characterized in that it comprises instructions for the execution of the steps following:

distribution (102) of at least part of the spots of the image (IM) in M distinct groups corresponding to the M columns and determination (104) of these M columns in the image of spots (IM) by linear regression;

distribution (108) of at least part of the spots of the image (IM) in N distinct groups corresponding to the N lines and determination (110) of these N lines in the image of spots (IM) by linear regression; And

characterization (114, 116, 118) of the camera alignment by these M columns and N lines determined in the spot image (IM);

when said program (60, 62, 64) is executed on a computer (46; 66).