Method for determining a thickness map of a layer to be measured

Abstract

The invention relates to a method for determining a thickness map of a layer to be measured, using a camera and a source. The method comprises the following steps: A acquiring a measurement image of the layer illuminated by the source using the camera; B providing a reference image of a reference reflector illuminated by the source using the camera; C determining a normalised measurement image, wherein each pixel of the normalised measurement image has at least three co-ordinates defining a normalised measurement vector; D providing, for each pixel, a predetermined normalised theoretical vector comprising at least three co-ordinates, depending on a theoretical thickness of a theoretical layer; E identifying, for each pixel, the normalised theoretical vector closest to the normalised measurement vector, wherein the thickness associated with the closest normalised theoretical vector corresponds to the measured thickness of the layer for the point of the layer imaged in the pixel.

Description

Method for determining a thickness map of a layer to be measured. TECHNICAL FIELD

[0001] The invention relates to the field of thin films, and more particularly to optical mapping of thin film thickness. BACKGROUND

[0002] The phenomenon of interference amplifies the variations in the reflectivity of a thin film with wavelength. When a thin film is illuminated with white light, the reflected light is no longer white, and the perceived color will then depend on its refractive index and its thickness. The perceived color varies with thickness. Knowing one allows us to deduce the other. The refractive index of a material is a quantity that can be precisely measured and is known and documented for many thin films. In principle, it is therefore possible to use the phenomenon of interference to map the thickness of a thin film.

[0003] Color charts exist that list the color perceived by the naked eye as a function of thickness for a given material. Perceived color is an interpretation of the relative intensities perceived by the eye or camera across three wavelength ranges, and not a color defined by its wavelength. The color of a layer therefore allows us to determine its thickness by eye. However, these color charts do not allow for the precise determination of thickness.

[0004] It is also possible to use a color camera to determine the thickness of a layer by perceiving color through the camera rather than with the eye. A color camera operates on a principle inspired by the human eye. Each pixel of the camera's detector generally comprises sub-pixels equipped with three filters: red, green, and blue. These filters allow the collected light to be filtered, thus enabling coarse spectroscopy across three different wavelength ranges. Figure 1a illustrates an example of the spectral sensitivities of a camera (in arbitrary units): SCR, SCG, SCB, associated respectively with three red, green, and blue sub-pixels, as a function of the wavelength λ. The spectral sensitivity associated with each color, typically called "quantum efficiency," is specific to a given camera and is usually provided by the manufacturer.This is typically the product of the spectral sensitivity of the camera without colour filters (black and white camera) and the transmission of the associated colour filter.

[0005] In the example in Figure 1a, the camera also has an ICR infrared filter that cuts wavelengths above 650 nm (also shown in Figure 1a), allowing it to cut off the infrared light to which the camera sensor is sensitive, unlike the human eye.

[0006] It is also possible to use a detector in which each pixel comprises four sub-pixels, each equipped with a red, green, blue, and infrared filter. Figure 1b illustrates an example of the sensitivities (in %) of such an SCR, SCG, SCB, and SCIR camera associated with the four aforementioned filters, as a function of the wavelength λ.

[0007] A color camera is therefore at least a "trispectral" camera. Its main advantage is the ability to map an entire sample in a single measurement. However, its low spectral resolution does not allow for precise thickness measurements based on the color detected by the camera.

[0008] The color measured by the camera is coded in a three-dimensional space (R, G, B), each color being the integral of the intensity of white light by the reflectivity of the thin film by the sensitivity of the camera in the color, over the wavelength range.

[0009] The relative intensity of the three measured colors depends on the camera's relative gain for each color (called white balance). However, to obtain a colorimetric measurement that can be used quantitatively, the camera would need to be calibrated on a surface with known reflectivity, and the white balance would need to be applied across the entire image field. In practice, this white balance is global and cannot be performed pixel by pixel. Consequently, some areas are more or less well balanced, and it is not possible to achieve perfect white balance across the entire image.

[0010] Because of this impossibility, colorimetry is not used to accurately quantify the thickness of thin layers at all points of them with a camera.

[0011] For example, document CN107310173 uses colorimetry to control the thickness of thin films during their production. The purpose of this document is to detect thick defects such as hoops, ribs, and grooves during film production. The color of an image is analyzed and compared to a predetermined color to determine if the thickness is deficient. However, the method does not require precise mapping of the layer thickness since it is only intended to detect significant defects within the layer.

[0012] Thus, known colorimetry methods do not allow for the accurate mapping of thin film thicknesses.

[0013] One of the aims of the present invention is to overcome the aforementioned drawbacks by providing a method that uses colorimetric information obtained with a camera to accurately map the thicknesses of thin films. SUMMARY

[0014] The invention relates to a method for determining a thickness map of a layer to be measured, having a refractive index and disposed on a substrate, with: • a camera comprising a detector and configured to image the layer on the detector, the detector comprising a plurality of pixels (Pi) indexed i, each pixel being subdivided into at least three sub-pixels comprising respectively at least three filters (FR, FG, FB), each sub-pixel detecting an intensity, a pixel being characterized by at least three coordinates corresponding to the intensities detected by the at least three sub-pixels, and • a source presenting an emission spectrum over a spectral band of interest, the method comprising the steps of: A acquiring a measurement image of the layer illuminated by the source with the camera, B obtaining a reference image of a reference reflector illuminated by the source, with the camera,the reference reflector having a predetermined reference reflectivity, C determine a normalized measurement image equal, for each pixel, to the ratio of the intensities of the measurement image and the reference image, each pixel of the normalized measurement image having at least three so-called normalized coordinates defining a normalized measurement vector, D have, for each pixel, a predetermined normalized theoretical vector comprising at least three so-called normalized theoretical coordinates, a function of a theoretical thickness of a theoretical layer, said normalized theoretical vector having been determined from a predetermined theoretical reflectivity of said theoretical layer and the reference reflectivity, said theoretical layer having a refractive index equal to the refractive index of the layer to be measured, E identify, for each pixel, the normalized theoretical vector closest to the normalized measurement vector,the thickness associated with the nearest normalized theoretical vector corresponding to the measured thickness of the layer for the imaged layer point on the pixel.

[0015] According to one embodiment, each of the at least three coordinates of the normalized theoretical vector is determined by, respectively: with: ^^ ^ ^ ^ / ^ , ^^ ^ ^^ / ^ , ^^^ ^ ^ / ^ : coordinates of the normalized vector ^^ ^ ^ / ^ ^ ^ ^, ^^^ ^ ^ ^ , ^^^ ^ ^ ^ and ^^^ ^ ^ ^ camera sensitivity for at least three sub-pixels respectively, ^ ^ ^ ^ ^^^ predetermined theoretical reflectivity of the theoretical layer of theoretical thickness e ^^ ^^^ ^ ^ ^ reflectivity of the reference reflector ^^ ^ ^ ^ emission spectrum of the source, λ min and λmax boundaries of the spectral band of interest.

[0016] According to one embodiment, the three filters correspond to three colored filters: red, green, and blue.

[0017] According to one embodiment, step D consists of loading a previously calculated normalized theoretical vector.

[0018] According to another embodiment, step D comprises a first substep consisting of loading refractive index values ​​onto the band of interest and a second substep consisting of determining said normalized theoretical vector.

[0019] According to one embodiment, step B consists of acquiring an image of the reference reflector illuminated by the source with the camera.

[0020] According to another embodiment, step B consists of loading a stored reference image.

[0021] According to one embodiment, the process further includes a control step F comprising the substeps of: F1 identifying, where appropriate, pixels exhibiting an abnormal layer thickness by comparison with thicknesses of at least two nearest neighbors of said pixels, F2 for each of the identified pixels, defining a range of possible thicknesses from said thicknesses of said nearest neighbors of said identified pixels, F3 determining, for said pixels identified in step F1, a corrected thickness from the identification of a normalized theoretical vector whose corresponding thickness is within said thickness range.

[0022] According to one embodiment, each pixel is subdivided into four sub-pixels, the first, second, and third sub-pixels comprising three red filters respectively, green and blue, the fourth sub-pixel comprising a filter in the infrared spectral band, the normalized measurement vector and the normalized theoretical vector being determined in a four-dimensional space.

[0023] According to one embodiment, the reference reflector is a mirror or a blank substrate identical to the substrate on which the layer is deposited.

[0024] According to another aspect, the invention relates to a system for determining a thickness map of a layer to be measured having a refractive index and disposed on a substrate, the system comprising: • a source having an emission spectrum over a spectral band of interest, the source being configured to illuminate said layer to be measured; • a camera comprising a detector and being configured to image said layer to be measured on the detector, the detector comprising a plurality of indexed pixels i, each pixel being subdivided into at least three sub-pixels, each sub-pixel detecting an intensity, a pixel being characterized by at least three coordinates corresponding to the intensities detected by the at least three sub-pixels, the camera being configured to acquire a measurement image of said layer;and • a processing unit configured to: - have a reference image of a reference reflector illuminated by the source with the camera, the reference reflector having a reference reflectivity, - determine a normalized measurement image equal, for each pixel, to the ratio of the intensities of the measurement image and the reference image, each pixel of the normalized measurement image having three so-called normalized coordinates defining a normalized measurement vector, - have, for each pixel, a predetermined normalized theoretical vector comprising at least three so-called normalized theoretical coordinates, a function of a theoretical thickness of a theoretical layer, said normalized theoretical vector having been determined from a predetermined theoretical reflectivity of said theoretical layer and the reference reflectivity, said theoretical layer having a refractive index equal to the refractive index of the layer to be measured;and - identify, for each pixel, the theoretical normalized vector closest to the normalized measurement vector, the thickness associated with said theoretical normalized vector corresponding to the thickness of the layer measured for the point of the layer imaged on the pixel. ;

[0025] According to one embodiment, the camera is further configured to acquire an image of the reference reflector illuminated by the source and to transmit said image of the reference reflector to the processing unit.

[0026] According to one embodiment, the three filters of the camera correspond to three colored filters: red, green, and blue.

[0027] According to one embodiment, each pixel is subdivided into four sub-pixels, the first, second and third sub-pixels comprising three red, green and blue filters respectively, the fourth sub-pixel comprising a filter in the infrared spectral band, the normalized measurement vector and the normalized theoretical vector being determined in a four-dimensional space.

[0028] According to one embodiment, the source and the camera are arranged symmetrically with respect to a normal to the substrate.

[0029] According to one embodiment, the source or camera is arranged on a normal to the substrate, and the system further includes a semi-reflective device (LS) configured to respectively send the light reflected by the layer to be measured to the camera or to send the light emitted by the source to the layer to be measured.

[0030] According to one embodiment, the source is a screen emitting white light.

[0031] The following description presents several embodiments of the device of the invention: these examples are not limiting to the scope of the invention. These embodiments illustrate both the essential features of the invention and additional features related to the embodiments considered. BRIEF DESCRIPTION OF THE DRAWINGS

[0032] The invention will be better understood and other advantages will become apparent upon reading the following description, which is given by way of non-limiting example, and through the figures, among which:

[0033] [Fig.1a] Figure 1a represents an example of a camera sensitivity spectrum comprising three sub-pixels, each with a filter on the blue, green, and red spectral bands;

[0034] [Fig.1b] Figure 1b represents an example of a camera sensitivity spectrum comprising four sub-pixels, each presenting a filter on the blue, green, red and infrared spectral band;

[0035] [Fig. 2] Figure 2 illustrates a method for determining a thickness map of a layer to be measured according to the invention;

[0036] [Fig.3] Figure 3 represents an example of theoretical reflectivity spectra of a silicon dioxide layer for different layer thicknesses;

[0037] [Fig. 4] Figure 4 represents an example of the evolution of the predetermined normalized theoretical vector (symbolized by a point) as a function of thickness;

[0038] [Fig. 5] Figure 5 illustrates an example of thickness determination with the method according to the invention when the measurement is noisy;

[0039] [Fig. 6a] Figure 6a represents an example of thickness mapping of a layer to be measured in silicon dioxide on a silicon wafer carried out with the method according to the invention;

[0040] [Fig. 6b] Figure 6b represents the thickness measured in Figure 6a according to a section along the y-axis;

[0041] [Fig. 7a] Figure 7a represents an example of a system for determining the thickness of a layer to be measured according to the invention;

[0042] [Fig. 7b] Figure 7b represents another example of a system for determining the thickness of a layer to be measured according to the invention; and

[0043] [Fig. 7c] Figure 7c shows another example of a system for determining the thickness of a layer to be measured according to the invention. DETAILED DESCRIPTION

[0044] The method 1000 for determining a thickness map of a layer to be measured CM according to the invention is shown schematically in figure 2. The layer to be measured CM has a refractive index n(λ) and is disposed on a substrate Sub.

[0045] The 1000 method is performed with a Cam camera comprising a Det detector, configured to image the CM layer on the detector. The detector includes a plurality of pixels Pi indexed i. Thus, an area of ​​the layer is associated with a point Pi on the detector, identified by its index i. The measured thickness map is therefore also indexed em(i).

[0046] Each pixel Pi of the camera is subdivided into at least three sub-pixels, each comprising at least three filters: FR, FG, FB. Each sub-pixel detects an intensity, a pixel thus being characterized by at least three coordinates corresponding to the intensities detected by the three sub-pixels.

[0047] For example, each pixel Pi is subdivided into three sub-pixels, each containing three associated filters FR, FG, and FB, corresponding to three color filters: red, green, and blue. In this case, a pixel is characterized by three coordinates corresponding to the intensities detected by the three filters.

[0048] In another example, each pixel is subdivided into four sub-pixels. The first, second, and third sub-pixels each contain three filters: red, green, and blue, respectively. The fourth sub-pixel has a FIR filter and SCIR sensitivity in the infrared spectral band. In this case, a pixel is characterized by four coordinates corresponding to the intensities detected by the four filters.

[0049] For a camera where each pixel comprises three subpixels, the spectral sensitivity of the first subpixel (e.g., red) is denoted SCR(λ), the spectral sensitivity of the second subpixel (e.g., green) is denoted SCG(λ), and the spectral sensitivity of the third subpixel (e.g., blue) is denoted SCIR(λ). These spectral sensitivities are defined over a spectral band of interest [λmin and λmax]. When the camera comprises four subpixels, the sensitivity of the fourth subpixel (e.g., an infrared subpixel) is denoted SCIR(λ).

[0050] Method 1000 is carried out with a source S having a known emission spectrum SE(λ) over the spectral band of interest. In one embodiment, the source S is a screen emitting white light. For example, the source S is a computer monitor.

[0051] In what follows, the 1000 process is described for a pixel Pi subdivided into three sub-pixels. It can be easily adapted depending on the number of sub-pixels.

[0052] Process 1000 includes the steps described below.

[0053] In step A, process 1000 comprises the acquisition of a measurement image Im of the CM layer illuminated by the source S, the image being acquired with the Cam camera. The acquisition is performed in such a way as to obtain, for each pixel Pi of the measurement image, a measurement vector ^!^ ^ with three coordinates ^!, ^!, ^! corresponding respectively to the three measurement intensities ^^!, ^^!, ^^! detected through the color filters FR, FG, FB. When the Cam camera includes four color filters, the measurement vector has four coordinates corresponding respectively to the four measurement intensities detected through the four filters. In one example, the measurement image Im includes the entire CM layer to be measured, or in another example, only a portion of the CM layer to be measured (for example, an area of ​​interest or a predetermined portion).

[0054] In step B of process 1000, a reference image Iref of a reference reflector Rref illuminated by the source S with the camera Cam is available. The reference reflector Rref has a predetermined (i.e., known) reference reflectivity RFref(λ). For example, the reference reflector Rref is a mirror or a blank substrate identical to the substrate Sub on which the CM layer is deposited. For example, the CM layer is deposited on a silicon substrate Sub, and the reference reflector Rref is a blank silicon substrate.

[0055] For each pixel Pi of the reference image Iref, a reference vector ^ ^^^ ^ ^ is obtained. The reference vector ^ ^^^ ^ ^ presents three coordinates^^", ^^", ^^" corresponding respectively to the three reference intensities of the reference image Iref detected through the colored filters of each pixel Pi.

[0056] In step C, process 1000 comprises determining a normalized measurement image In equal, for each pixel Pi, to the ratio of the intensities of the measurement image Im and the intensities of the reference image Iref. Each pixel of the normalized measurement image In has three coordinates ^^#, ^^#, ^^#, called normalized coordinates, defining a normalized measurement vector V % ^i^. The normalized measurement vector V % ^i^ is defined as the ratio of a measure vector ^ ! ^ ^ and the reference vector ^ ^^^ ^ ^, that is to say:

[0057] For example, when the Cam camera has three filters FR, FG, FB, the three filters being red, green, and blue colored filters, the normalized measurement vector V % ^ i ^ has three coordinates IRn, IGn, IBn defined respectively as follows, for each pixel Pi:

[0058] With, for a given pixel Pi indexed i: IRm(i) the intensity of the measurement image for the red sub-pixel of pixel Pi; IGm(i) the intensity of the measurement image for the green sub-pixel of pixel Pi; IBm(i) the intensity of the measurement image for the blue sub-pixel of pixel Pi; IR0(i) the intensity of the reference image for the red sub-pixel of pixel Pi; IG0(i) the intensity of the reference image for the green sub-pixel of pixel Pi; and IB0(i) the intensity of the reference image for the blue sub-pixel of pixel Pi.

[0059] We can mathematically express the intensities detected by each pixel of the camera, respectively for the layer to be measured CM and for the reference reflector Rref, with physical quantities characteristic of the different elements of the system, and therefore their ratios equal to IRn, IGn and IBn, in the following way:

[0060]

[0061] With: ^^^^^^, ^^^^^^ and ^^^^^^ the spectral sensitivity of the camera for the red, green and blue sub-pixels, respectively. ^^ ! ^^^ The reflectivity of the layer to be measured is CM; ^^ ^^^ ^^^ the reflectivity of the reference reflector ^ ^^^ ; ^^^^^ the emission spectrum of the source in arbitrary units α the actual spectral intensity of the source S, related to the measurement method

[0062] λ min and λ max lower and upper bounds of the spectral band of interest. Note that in the calculation above, the ratio of the measurement vector ^ ! ^ ^ and the reference vector ^ ^^^ ^ ^ allows us to disregard the real spectral intensity α of the source S, which simplifies for each pixel (i.e., for each sub-pixel of the pixel).

[0063] In another example, the Cam camera has four filters: FR, FG, FB, and FIR, the four filters being red, green, blue, and infrared, with the normalized vector V % ^i^ has four coordinates IRn, IGn, IBn, IIRn, the red, green and blue coordinates being defined as above and the infrared coordinate being defined as follows: With, for a given pixel Pi: ^^^^^^^ , the spectral sensitivity of the camera for the red, green and blue sub-pixel, respectively; IIRm(i) the intensity of the measurement image for the infrared sub-pixel of pixel Pi; and IIR0(i) the intensity of the reference image for the infrared sub-pixel of pixel Pi.

[0064] In one embodiment, step B consists of acquiring an image of the reference reflector Rref illuminated by the source with the camera Cam. Thus, an image Iref is acquired before or after the acquisition of the image Im, by positioning the reference reflector in place of the layer to be measured.

[0065] In another embodiment, step B consists of loading a stored reference image. For example, a series of measurements, i.e., image acquisitions Im, can be performed for layers to be measured CM on a substrate Sub of the same material. Thus, a stored reference image Iref corresponding to the reference reflector Rref is used for a series of measurements. For example, a reference image Iref acquired with a silicon reference reflector Rref is acquired and stored. Subsequently, when the layer to be measured CM is placed on a silicon substrate Sub, step C is performed with the stored reference image Iref of the silicon reference reflector Rref, thereby reducing the time required to perform a series of measurements.

[0066] In step D, process 1000 includes the arrangement, for each pixel Pi, of a normalized theoretical vector V , . - / %^i^ predetermined comprising at least three coordinates called normalized theoretical coordinates IRth, IGth, IBth, a function of a theoretical thickness e of a theoretical layer CT. The theoretical layer CT has a refractive index equal to the refractive index n(λ) of the layer to be measured CM. The normalized theoretical vector ^ ^ ^ ^ / ^ ^ ^ was determined from a theoretical reflectivity R . ,- (λ) predetermined of the theoretical layer CT and of the reference reflectivity RFref(λ).

[0067] Figure 3 illustrates examples of theoretical reflectivity R . - (λ) predetermined as a function of the wavelength λ (in nm) for a silicon substrate alone (curve a), and for a theoretical CT layer of silicon dioxide deposited on a silicon substrate, for layer thicknesses of 50 nm (curve b), 500 nm (curve c) and 1000 nm (curve d). It is noted that reflectivity varies significantly with thickness in the visible spectral range. These calculations are based on classical reflectivity models, such as the transfer matrix method.

[0068] Figure 4 illustrates the evolution of the normalized theoretical vector V , . - / % ^i^ calculated for a theoretical CT layer of silicon dioxide on a silicon substrate, as a function of the thickness e. In this figure, the thickness e of the theoretical CT layer varies between 0 (point V, with a normalized vector V , . - 0 / " % coordinates (1,1,1)) and 1400 nm (point W), and the refractive index n(λ) is known. The theoretical reflectivity R is calculated . ,- (λ) from known formulas for a plurality of thicknesses, and the reflectivity of the reference reflector is known. The normalized theoretical vector V is deduced , . - / %^i^ . In the example in Figure 4, the normalized theoretical vector V , . - / % ^i^ is represented by a point in a three-dimensional space since it is obtained from three coordinates ^^ ^ ^ ^ / ^ , ^^ ^ ^ ^ / ^ , ^^ ^ ^ ^ / ^ associated with a given thickness (when three filters are used).

[0069] In another embodiment, the normalized theoretical vector V , . - / % ^i^ is calculated with four coordinates (when four filters are determined) and is represented in four dimensions. Representing it in three or four dimensions helps to limit the probability of crossover points.

[0070] In step E, process 1000 includes the identification, for each pixel Pi, of the normalized theoretical vector closest to the normalized measurement vector The thickness associated with the normalized theoretical vector ^ ^ ^ ^ ! / ^ ^ ^ The closest corresponds to the measured thickness em(i) of the CM layer for the point (or area) of the CM layer imaged on pixel Pi. In particular, the normalized measurement vector ^ ^ ^ ^ is compared to the normalized theoretical vector ^ ^ ^ ^ / ^ ^ ^ (for example, illustrated in Figure 4). The normalized theoretical vector ^ ^ ^ ^ / ^ Since the thickness is a function of a theoretical thickness e of a theoretical layer CT, by comparing the two vectors, it is possible to determine the thickness corresponding to the normalized theoretical vector. closest to the normalized measurement vector ^ ^ ^ ^. Thus, by this method, a thickness is obtained and it is therefore possible to perform a thickness map with a single image of the layer to be measured CM.

[0071] Advantageously, the invention eliminates the need for white balance by using normalization with a reference reflector Rref. The reference reflector Rref normalizes the image of the layer to be measured CM, thus overcoming the colorimetric non-uniformity of the source and the camera Cam. This normalization allows for the acquisition of precise thickness values.

[0072] Furthermore, this thickness measurement method is as accurate as other methods such as spectral reflectivity, X-ray reflectivity, or ellipsometry, but much faster and less expensive. Indeed, the invention allows for mapping the thickness of a thin layer across the entire field of view of the Cam camera in a single shot. and therefore in a very short period of time, with a resolution depending on the field of view, the lens and the pixel density of the Cam camera used.

[0073] In one embodiment, the coordinates of the normalized theoretical vector correspond, for each sub-pixel of a given pixel, to the calculation of the ratio of the intensity detected by the camera when the layer to be measured is replaced by the theoretical CT layer, and the intensity detected by the camera when the layer to be measured is replaced by the reference reflector.

[0074] Typically the coordinates of the normalized theoretical vector ^ ^ ^ ^ / ^ ^ ^ determined in step D are determined by, respectively:

[0075] With: ^^^ ^ ^ ^ , ^^^ ^ ^ ^ and ^^^ ^ ^ ^ the spectral sensitivity of the camera for the red, green, and blue sub-pixels, respectively; ^ ^ ^ ^ ^^^ the predetermined theoretical reflectivity of the theoretical layer; ^^ ^^^ ^^^ the reflectivity of the reference reflector; ^^ ^ ^ ^the emission spectrum of the source.

[0076] We note that the calculation of the normalized theoretical vector ^ ^ ^ ^ / ^ ^ ^ corresponds to the calculation of the normalized measurement vector V % ^ i ^ which was simplified by the actual spectral intensity α of the source S, by replacing the reflectivity of the layer to be measured CM with the theoretical reflectivity ^ ^ ^ ^ ^^^ for a given thickness (for example, illustrated in Figure 3). The normalized theoretical vector ^ ^ ^ ^ / ^ ^ ^ is therefore comparable to the normalized measurement vector V % ^ i ^ However, it is necessary to know: the reference reflectivity RFref(λ) of the reference reflector ^ ^^^ ^^^, the emission spectrum of the source (in arbitrary units), and the spectral sensitivities of the sub-pixels (in arbitrary units).

[0077] In one embodiment, step D consists of loading a normalized theoretical vector ^ ^ ^ ^ / ^ ^ ^ previously calculated. In particular, the normalized theoretical vector V , . - / % ^i^ predetermined is determined for a given refractive index n(λ) (and therefore a given material). Thus, the normalized theoretical vector V , . - / % ^i^ is calculated for a given material and can be used for a series of thickness maps em(i) of layers to be measured CM in that same material. For example, a first normalized theoretical vector V1 . , - / % ^i^ is calculated for a first material M1 having a first refractive index n1(λ) and a second normalized theoretical vector V2 . , - / % ^i^ is calculated for a second material M2 having a second refractive index n2(λ). The first normalized theoretical vector V1 . ,- / % ^i^ is loaded to perform a series of thickness mapping determinations of layers to be measured CM in the first material M1. Then, the second normalized theoretical vector V2 . , - / % ^i^ is loaded to perform a series of determinations of the thickness mapping em(i) of layers to be measured CM in the second material M2.

[0078] In another embodiment, step D comprises a first substep of loading refractive index values ​​onto the band of interest and a second substep of determining the normalized theoretical vector ^ ^ ^ ^ / ^ For example, the first substep involves identifying the material of the layer to be measured CM and loading the corresponding refractive index value n(λ) for that material. Then, the second substep involves determining the normalized theoretical vector. ^ ^ ^ / ^^ ^ based on the charged refractive index value.

[0079] As mentioned above, for each pixel, the thickness is determined by comparing the normalized theoretical vector ^ ^ ^ ^ / ^ ^ ^ (illustrated in Figure 4) and the normalized measurement vector ^ ^ ^ ^. To determine the closest vector, typically we determine a Euclidean distance between the two vectors.

[0080] As illustrated in Figure 4, the curve representing the evolution of the normalized theoretical vector as a function of thickness comprises several "turns." The thickness corresponding to each pixel Pi therefore corresponds to the point on the turn closest to the point associated with the normalized measurement vector. If the measurement is noisy, and this noise is on the order of magnitude of the difference between two turns, the determined thickness may be erroneous due to the proximity of the incorrect turn.

[0081] Figure 5 illustrates an example of thickness determination when the measurement is noisy. Two turns, Sp1 and Sp2, are shown. When a measurement is noisy, the measured thicknesses may give an erroneous value. The mean plane between the two turns, Sp1 and Sp2, is denoted Pm. For example, point 50 (corresponding to the normalized measurement vector Vn(i) of a pixel Pi) is closer to turn Sp2 than to turn Sp1, thus suggesting that the thickness associated with point 50 is the thickness e2(50). However, the nearest neighbors PPV1 and PPV2 of point 50 both have thicknesses e(PPV1). e(PPV2) placed on the Sp1 loop. Thus, the thickness associated with point 50 is not the thickness e2(50) but is actually the thickness e1(50).

[0082] In order to correct these errors, in one embodiment, process 1000 includes a control step F.

[0083] Step F includes a substep F1 for identifying, where applicable, "abnormal" pixels exhibiting an abnormal layer thickness by comparison with the thicknesses of at least two nearest neighbors of the pixels. For example, the thickness determined by the method according to the invention is compared to thicknesses determined for nearest neighbors of the pixel, and when the difference between the determined thickness and the thicknesses of the nearest neighbors exceeds a threshold, the determined thickness is identified as abnormal.

[0084] Next, step F includes a substep F2 of defining a range of possible thicknesses [emin; emax], for each of the identified "abnormal" pixels, from the thicknesses of at least two nearest neighbors of the identified pixels.

[0085] Thus, the range allows the thickness to be limited to a so-called normal value, that is to say without sudden variation.

[0086] Furthermore, step F includes a substep F3 for determining, for the pixels identified in step F1, a corrected thickness based on the identification of a normalized theoretical vector ^ ^ ^ ^ / ^ ^ ^ whose corresponding thickness is within the range of possible thicknesses [emin ; emax]. Thus, the thickness is determined in the turn within the range of possible thicknesses [emin ; emax].

[0087] Advantageously, this method allows for the detection and correction of erroneous thickness values. Indeed, the thickness of a layer is generally continuous. Abrupt variations (for example, of several tens of nm) between two consecutive pixels are unlikely.

[0088] Figure 6a illustrates an example of thickness maps em(i) of a silicon dioxide CM layer obtained using method 1000 according to the invention. A point on the layer is located along two axes X and Y (expressed in mm), and the layer thickness determined by the method according to the invention is expressed in nm. The image pixels have been transformed to their corresponding positions on the layer. Figure 6b illustrates an example of a thickness measurement of a silicon dioxide CM layer taken in cross-section along the Y-axis (line 60 of Figure 6a). A thickness of 590 nm is noted at point O in Figure 6b. The thickness of the CM layer was measured independently with a spectroscopic ellipsometer at the center O of the CM layer, and a value very close to 590 nm was measured.

[0089] Furthermore, the method according to the invention makes it possible to measure the em(i) thickness of the CM layer regardless of its surface roughness, whereas it is not possible to measure the thickness of a thin film using known techniques based on reflectivity (e.g., spectral reflectivity, X-ray reflectivity, or ellipsometry) if the surface is rough. This is because these reflectivity measurements are based on measuring interference fringes, i.e., oscillations, the amplitude of which decreases sharply with roughness. Conversely, roughness has no impact on colorimetry. This robustness of the method according to the invention stems from the fact that the spectral ranges of the colors and / or infrared are very broad.

[0090] In another aspect, the invention relates to a Sys system for determining a thickness map em(i) of a layer to be measured CM having a refractive index n(λ) and deposited on a substrate Sub, enabling the implementation of the method 1000 described above. Examples of Sys systems are illustrated in Figures 7a, 7b, and 7c.

[0091] The Sys system includes a source S exhibiting an emission spectrum SE(λ) over a spectral band of interest, with the source S configured to illuminate the layer to be measured CM. In one example, the source S is a screen emitting white light. For example, the source is a computer monitor.

[0092] The Sys system also includes a Cam camera comprising a Det detector and configured to image the CM layer on the Det detector, the detector comprising a plurality of Pi pixels indexed i, each pixel being subdivided into at least three sub-pixels comprising respectively at least three filters FR, FG, FB, each sub-pixel detecting an intensity, a pixel being characterized by at least three coordinates corresponding to the intensities detected by the at least three sub-pixels.

[0093] The Cam camera is configured to acquire a measurement image Im of the CM layer. In one embodiment, the Cam camera comprises three colors: blue, green, and red. Specifically, each pixel of the Cam camera's detector Det is subdivided into three sub-pixels, each containing three filters: FR, FG, and FB, corresponding to three colored filters: red, green, and blue. In another example, each pixel is subdivided into four sub-pixels: the first, second, and third sub-pixels each contain three filters: red, green, and blue, respectively, while the fourth sub-pixel contains an FIR filter in the infrared spectral band. In this case, a pixel is characterized by four coordinates corresponding to the intensities detected by the four filters. In one example, the Cam camera is a tri-spectral color camera equipped with a lens. The lens is focused on the surface of the CM layer to be measured, which is illuminated by the source S.

[0094] In the embodiment illustrated in Figure 7a, the source S and the camera Cam are arranged symmetrically with respect to the normal N to the substrate Sub. In this In this configuration, the source S illuminates the layer to be measured CM and the camera Cam directly images the layer to be measured CM.

[0095] In the example in Figures 7b, the source S is arranged on a normal to the substrate, and the system further includes a semi-reflective device LS configured to send the light reflected by the layer to be measured CM to the camera Cam.

[0096] In the example in Figure 7c, the Cam camera is positioned on a normal to the substrate, and the system further includes a semi-reflective device LS configured to send the light emitted by the source to the layer to be measured CM.

[0097] For example, the semi-reflective device LS can be a beam splitter or a beam splitter cube. In the configuration shown in Figure 7c, the camera lens Cam is focused on the surface of the layer to be measured CM, which is illuminated by the source S via the semi-reflective device LS. Alternatively (Figure 7b), the source S illuminates the layer to be measured CM, which is imaged by the camera Cam via the semi-reflective device LS.

[0098] In addition, the Sys system includes a PU processing unit configured to implement process 1000. The PU processing unit is connected to the Cam camera.

[0099] The processing unit PU is configured to have a reference image Iref of a reference reflector Rref illuminated by the source with the camera Cam, the reference reflector Rref having a reference reflectivity RFref(λ).

[0100] Furthermore, the processing unit PU is configured to determine a normalized measurement image In equal, for each pixel, to the ratio of the intensities of the measurement image and the reference image, each pixel of the normalized measurement image having three so-called normalized coordinates defining a normalized measurement vector ^ ^ ^ ^.

[0101] Furthermore, the processing unit PU is configured to have, for each pixel Pi, a normalized theoretical vector ^ ^ ^ ^ / ^ ^ ^ predetermined including at least three coordinates called normalized theoretical coordinates, a function of a theoretical thickness e of a theoretical layer CT, the normalized theoretical vector having been determined from a theoretical reflectivity predetermined theoretical layer CT and reference reflectivity, the theoretical layer CT having a refractive index equal to the refractive index n(λ) of the layer to be measured CM.

[0102] In one embodiment, each pixel is subdivided into four sub-pixels, the first, second, and third sub-pixels comprising three red, green, and blue filters respectively, the fourth sub-pixel comprising a filter in the infrared spectral band, the normalized measurement vector ^ ^ ^ ^ and the normalized theoretical vector being determined in a four-dimensional space.

[0103] Furthermore, the PU processing unit is configured to identify, for each pixel, the normalized theoretical vector closest to the normalized measurement vector ^ ^ ^ ^, the thickness associated with the normalized theoretical vector ^ ^ ^ ^ ! / ^^ ^ the closest corresponding to the thickness of the layer measured CM for the point of the layer imaged on the pixel.

[0104] Because calculating the Euclidean distance is very simple and each pixel is independent of the others, it is possible to parallelize the calculation of finding the theoretical vector closest to the measured vector of each pixel, using a GPU (Graphics Processing Unit). This greatly reduces the computation time.

[0105] The processing unit (PU) can be implemented using hardware, software, and / or a combination thereof. For example, hardware devices can be implemented using processing circuits such as, but not limited to, a processor, a central processing unit (CPU), a controller, an arithmetic logic unit (ALU), a digital signal processor, a microcomputer, a field-programmable gate array (FPGA), a system-on-a-chip (SoC), a programmable logic unit, a microprocessor, or any other device capable of responding to and executing instructions in a defined manner. Software can include a computer program, program code, instructions, or a combination thereof, to provide instructions or configure a hardware device, independently or collectively, to operate as desired.The computer program and / or program code may include program instructions or computer-readable instructions, software components, software modules, data files, data structures, and / or the like, capable of being implemented by one or more hardware devices. When a hardware device is a computing device (e.g., a CPU, controller, ALU, digital signal processor, microcomputer, microprocessor, etc.), the computing device may be configured to execute program code by performing arithmetic, logical, and input / output operations, according to the program code. Each unit may also include one or more storage devices.The storage device(s) may be tangible or non-transient computer-readable storage media, such as random access memory (RAM), read-only memory (ROM), a permanent mass storage device (such as a disk drive), a semiconductor (e.g., NAND flash), and / or any other similar data storage mechanism capable of storing and recording data. The storage device(s) may be configured to store computer programs, program code, instructions, or a combination thereof, for one or more operating systems and / or to implement the example embodiments described herein. The computer programs, program code, instructions, or a combination thereof, may also be loaded from a separate, readable storage medium. by computer into the storage device(s) and / or one or more computer processing devices using a drive mechanism. Such separate computer-readable storage media may include a USB (Universal Serial Bus) flash drive, a USB flash drive, a Blu-ray / DVD / CD-ROM drive, a memory card, and / or other similar computer-readable storage media.

[0106] In one embodiment, the Cam camera is further configured to acquire an image of the reference reflector Rref illuminated by the source and to transmit the image of the reference reflector Iref to the PU processing unit. The PU processing unit can then use the image of the reference reflector Rref to determine the thickness map em(i) of the layer to be measured CM.

[0107] Although the invention has been illustrated and described in detail using a preferred embodiment, the invention is not limited to the disclosed examples. Other variations can be deduced by a person skilled in the art without departing from the scope of protection of the claimed invention.

Claims

CLAIMS 1. A method (1000) for determining a thickness map (em(i)) of a measurement layer (CM) having a refractive index (n(λ)) and disposed on a substrate (Sub), with: • a camera (Cam) comprising a detector (Det) and configured to image the layer on the detector, the detector comprising a plurality of pixels (Pi) indexed i, each pixel being subdivided into at least three sub-pixels comprising respectively at least three filters (FR, FG, FB), each sub-pixel detecting an intensity, a pixel being characterized by at least three coordinates corresponding to the intensities detected by the at least three sub-pixels, and • a source (S) having an emission spectrum (SE(λ)) over a spectral band of interest, the method comprising the steps of: Acquiring a measurement image (Im) of the layer (CM) illuminated by the source with the camera,B. to have a reference image (Iref) of a reference reflector (Rref) illuminated by the source, with the camera, the reference reflector having a predetermined reference reflectivity (RFref(λ)), C. to determine a normalized measurement image (Inorm) equal, for each pixel, to the ratio of the intensities of the measurement image and the reference image, each pixel of the normalized measurement image having at least three so-called normalized coordinates defining a normalized measurement vector ^^, ^ ^ ^^, D have, for each pixel, a normalized theoretical vector (^ ^ ^ ^ / ^ ^ ^^ predetermined comprising at least three coordinates called normalized theoretical coordinates, a function of a theoretical thickness (e) of a theoretical layer (CT), said normalized theoretical vector having been determined from a theoretical reflectivity (^ ^ ^^ (λ)) predetermined of said theoretical layer and of the reference reflectivity, said theoretical layer having a refractive index equal to the refractive index (n(λ)) of the layer to be measured, E identify, for each pixel, the normalized theoretical vector (^ ^ ^ ^ ! / ^ ^ ^^ closest to the normalized measurement vector ^^ ^ ^ ^^, the associated thickness (em(i)) of the nearest normalized theoretical vector corresponding to the measured thickness of the layer for the imaged layer point on the pixel.

2. A method according to claim 1, wherein each of the at least three coordinates of the normalized theoretical vector (^ ^ ^ ^ / ^ ^ ^^, is determined by, respectively: with: ^^ ^ ^ ^ ^ / ^ , ^^^^ / ^ , ^^^ ^ ^ / ^ : coordinates of the normalized vector ^ ^ ^ ^ / ^ ^ ^^, ^^^ ^ ^ ^, ^^^ ^ ^ ^ and ^^^ ^ ^ ^ camera sensitivity for at least three sub-pixels respectively, ^ ^ ^ ^ ^^^ predetermined theoretical reflectivity of the theoretical layer of theoretical thickness e ^^ ^^^ ^ ^ ^ reflectivity of the reference reflector ^^ ^ ^ ^ emission spectrum of the source, λmin And λmaxbounds of the spectral band of interest.

3. A method according to claim 1 or 2, wherein the three filters correspond to three colored filters: red, green, and blue.

4. A method according to any one of the preceding claims, wherein step D consists of loading a previously calculated normalized theoretical vector.

5. A method according to any one of claims 1 to 3, wherein step D comprises a first substep of loading refractive index values ​​onto the band of interest and a second substep of determining said normalized theoretical vector.

6. A method according to any one of the preceding claims, wherein step B consists of acquiring an image of the reference reflector illuminated by the source with the camera.

7. A method according to any one of claims 1 to 5, wherein step B consists of loading a stored reference image.

8. A method according to any one of the preceding claims, further comprising a control step F including the substeps of: F1 identifying, where appropriate, pixels exhibiting an abnormal layer thickness by comparison with the thicknesses of at least two nearest neighbors of said pixels; F2, for each of the identified pixels, defining a range of possible thicknesses ([emin; emax]) from said thicknesses of said nearest neighbors of said identified pixels; F3 determining, for said pixels identified in step F1, a corrected thickness from the identification of a normalized theoretical vector whose corresponding thickness is within said thickness range. 9.A method according to any one of the preceding claims, wherein each pixel is subdivided into four sub-pixels, the first, second and third sub-pixels comprising three red, green and blue filters respectively, the fourth sub-pixel comprising a filter in the infrared spectral band, the normalized measurement vector (^. ^^ ^^ and the normalized theoretical vector being determined in a four-dimensional space.

10. A method according to any one of the preceding claims, wherein the reference reflector (Rref) is a mirror or a blank substrate identical to the substrate on which the layer is deposited. 11.System (Sys) for determining a thickness map (em(i)) of a layer to be measured (CM) having a refractive index (n(λ)) and disposed on a substrate (Sub), the system (Sys) comprising: • a source (S) having an emission spectrum (SE(λ)) over a spectral band of interest, the source being configured to illuminate said layer to be measured; • a camera (Cam) comprising a detector (Det) and being configured to image said layer to be measured on the detector, the detector comprising a plurality of pixels (Pi) indexed i, each pixel being subdivided into at least three sub-pixels, each sub-pixel detecting an intensity, a pixel being characterized by at least three coordinates corresponding to the intensities detected by the at least three sub-pixels, the camera being configured to acquire a measurement image (Im) of said layer (CM); and • a processing unit (PU) configured to:. - to have a reference image (Iref) of a reference reflector (Rref) illuminated by the source with the camera, the reference reflector having a reference reflectivity (RFref(λ)), - to determine a normalized measurement image equal, for each pixel, to the ratio of the intensities of the measurement image and the reference image, each pixel of the normalized measurement image having three so-called normalized coordinates defining a normalized measurement vector (^ ^ ^ ^) - to have, for each pixel, a normalized theoretical vector predetermined comprising at least three coordinates called normalized theoretical coordinates, a function of a theoretical thickness (e) of a theoretical layer (CT), said normalized theoretical vector having been determined from a theoretical reflectivity (^ ^ ^^ (λ)) predetermined of said theoretical layer and of the reference reflectivity, said theoretical layer having a refractive index equal to the refractive index (n(λ)) of the layer to be measured; and - identify, for each pixel, the normalized theoretical vector closest to the normalized measurement vector, the associated thickness (em(i)) of said nearest normalized theoretical vector corresponds to the thickness of the measured layer for the imaged layer point on the pixel.

12. System according to claim 11, wherein the camera is further configured to acquire an image of the reference reflector illuminated by the source and to transmit said image of the reference reflector to the processing unit.

13. System according to claims 11 or 12, wherein the three filters of the camera correspond to three colored filters: red, green, and blue. 14.A system according to claim 11 or 12, wherein each pixel is subdivided into four sub-pixels, the first, second, and third sub-pixels comprising three red, green, and blue filters, respectively, and the fourth sub-pixel comprising a filter in the infrared spectral band, the normalized measurement vector and the normalized theoretical vector being determined in a four-dimensional space. A system according to any one of claims 11 to 14, wherein the source and the camera are arranged symmetrically with respect to a normal to the substrate. A system according to any one of claims 11 to 14, wherein the source or the camera is arranged on a normal to the substrate, and wherein the system further comprises a semi-reflective (SR) device configured to emit the reflected light, respectively. through the layer to be measured towards the camera or to send the light emitted by the source towards the layer to be measured.

17. System according to any one of claims 11 to 16 wherein the source is a screen emitting white light.

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