Method for quality control of a composite structure comprising a thin c-SiC layer, and composite structure
A method using confocal microscopy and photoluminescence imaging with image recognition algorithms accurately classifies secondary defects in composite silicon carbide structures, addressing the challenge of distinguishing true defects from false ones, thus ensuring high-quality epitaxial layers are produced.
Patent Information
- Application Number
- FR2023012780
- Authority / Receiving Office
- FR · FR
- Patent Type
- Patents
- Current Assignee / Owner
- Filing Date
- 2023-11-21
- Publication Date
- 2025-11-21
- Estimated Expiration
- 2043-11-21
AI Technical Summary
Existing methods struggle to reliably inspect and classify secondary defects in composite structures comprising a thin layer of monocrystalline silicon carbide on a polycrystalline silicon carbide support substrate, which can lead to the transfer of non-specification epitaxial layers due to 'killer' defects, and distinguish these from 'false defects' induced by the underlying polycrystalline substrate.
A method combining confocal microscopy in visible light and photoluminescence imaging with image recognition algorithms to detect and classify secondary defects, applying specific similarity thresholds and additional photoluminescence analysis to ensure accurate defect identification and classification, thereby distinguishing true defects from false ones.
Enables reliable classification of secondary defects, ensuring the composite structures meet quality specifications by avoiding the transfer of non-specification epitaxial layers and reducing the risk of downgrading high-quality composite structures due to false defects.
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Abstract
Description
Title of the invention: Method for quality control of a composite structure comprising a thin layer of c-SiC, and composite structure. FIELD OF THE INVENTION
[0001] The present invention relates to the field of semiconductor materials. It relates in particular to a method for quality control of a composite structure comprising a thin layer of monocrystalline silicon carbide disposed on a support substrate of polycrystalline silicon carbide.
[0002] TECHNOLOGICAL BACKGROUND OF THE INVENTION
[0003] Silicon carbide is a material of particular interest for the manufacture of power devices, radio frequencies or devices operating at very high temperatures.
[0004] The quality of single-crystal silicon carbide (c-SiC) substrates has improved significantly over the past ten years, accompanied by increasingly precise knowledge and detection of the various types of crystalline defects that may be present in this material. The JEITA (Japan Electronics and Information Technology Industries Association) standard includes four documents relating to the zoology of defects on / in a layer produced by homoepitaxy on a 4H-SiC substrate (EDR 4712 / 100) and to non-destructive procedures for the optical inspection of these defects (EDR 4712 / 200, / 300, / 400). In particular, a procedure is described for evaluating and referencing defects by combining optical inspection and photoluminescence imaging.
[0005] Commercially available equipment, such as the SICA88 from Lasertec, allows the combination of Nomarski prism confocal microscopy techniques in visible light and by photoluminescence (as described by [1] T. Kimoto et al., “Fundamentals of Silicon Carbide Technology Growth Characterization Devices and Applications”, p. 126, or [2] D. Baierhofer et al, Materials Science in Semiconductor processing 140 (2022) 106414) and is commonly used to inspect c-SiC substrates before or after epitaxy.
[0006] By way of example, [3] Das et al (“Statistical analysis of killer and non-killer defects in SiC and the impact of device performance”, Material Science Forum, ISSN 1662-9752, Vol. 1004, pp 458-463 (2020) Trans Tech Publications Ltd) provides a statistical analysis of “killer” defects for devices fabricated on a homoepitaxial layer of c-SiC, and shows optical and photoluminescence images of typical c-SiC defects.
[0007] Although rapidly developing, high-quality c-SiC substrates remain expensive and difficult to source in large sizes. Therefore, it is advantageous to use film transfer solutions to create composite structures comprising a thin film of monocrystalline SiC (derived from a high-quality c-SiC donor substrate) on a lower-cost support substrate, for example, polycrystalline SiC (p-SiC), which may also offer advantages in terms of electrical conductivity. A well-known thin-film transfer solution is the Smart Cut™ process, based on light ion implantation and direct bonding between a c-SiC donor substrate and a support substrate at a bonding interface.The implantation creates a fragile, buried plane along which a separation occurs, leading to the transfer of a thin c-SiC layer onto the supporting substrate to form the composite structure. This allows for the recovery and recycling of the remaining donor substrate, potentially enabling one or more further layer transfers. Epitaxy can then be performed on the thin layer of the composite structure, followed by the fabrication of the electronic devices.
[0008] For the implementation of a layer transfer process to be economically viable, it is important to be able to control the quality of the donor substrates in order to avoid transferring a layer that is out of specification, or that would result in an epitaxial layer that is out of specification, in terms of "killer" defects. It is therefore essential to detect and also to accurately classify the crystalline defects (known as primary defects) present on the donor substrates, in order to downgrade those donor substrates that will not allow the fabrication of a thin film of the required quality.The defect zoology of c-SiC is relatively well known, and numerous studies tend to define the type and size of defects, present in the epitaxial layer of c-SiC, that would be killer for components; criteria for classifying primary defects, with a view to a c-SiC thin film transfer, can therefore be established.
[0009] Furthermore, the detection and recognition of defects (so-called secondary defects) on and / or in the thin film (from a donor substrate) of a composite structure whose support substrate is polycrystalline SiC (p-SiC) is complex because the p-SiC grains are visible beneath said thin film. An effective thin film quality control process is therefore required, firstly, to avoid continuing the epitaxial steps if secondary defects in the thin film are likely to generate a non-specification, killer defect density in the homoepitaxial layer; secondly, it is important that the control process be able to distinguish thin film defects from potential "false defects" (or measurement noise) associated with underlying grains of the p-SiC substrate.
[0010] In view of the growing use of layer transfer techniques in the manufacturing chain of electronic devices on c-SiC, there is therefore a strong need to define control processes allowing reliable inspection of composite structures.
[0011] SUBJECT OF THE INVENTION
[0012] The present invention addresses the stated problem. The invention relates to a method for quality control of a composite structure comprising a thin film (from a monocrystalline silicon carbide donor substrate) disposed on a polycrystalline silicon carbide support substrate, said control method enabling a reliable classification of secondary defects present on and / or in the thin film.
[0013] BRIEF DESCRIPTION OF THE INVENTION
[0014] The invention relates to a method for quality control of a composite structure comprising a thin layer of monocrystalline silicon carbide deposited on a polycrystalline silicon carbide support substrate, the method comprising the following steps:
[0015] a) the inspection of a free surface of the thin film by a technique combining confocal microscopy in visible light and photoluminescence imaging, making it possible to detect defects, called secondary defects, present on and / or in the thin film, and to form, for each defect, an image in visible light and an image by photoluminescence,
[0016] b) the preliminary identification of secondary defects, by similarity, based on their image in visible light, using an image recognition algorithm trained on different types of defects likely to be present on and / or in a thin film, such as holes, bubbles, scratches, defects of crystalline origin; at the end of step b), each secondary defect being associated with an identified type of defect, with a certain level of similarity,
[0017] c) the final classification of at least some secondary defects by applying the following first conditions:
[0018] - if the level of similarity associated with a secondary defect is greater than a level At the top, the said secondary defect is definitively classified within the identified defect type.
[0019] - if the level of similarity associated with the secondary defect is between a level At the low and high levels, the photoluminescence image of said defect is analyzed by an image recognition algorithm trained on different labeled types of defects; if the secondary defect is associated with a labeled type of defect, said secondary defect is definitively classified in the identified type of defect.
[0020] - in other cases, the secondary defect is definitively classified as not being Not a flaw.
[0021] According to other advantageous and non-limiting features of the invention, taken alone or in any technically feasible combination: • the secondary defects to which the first conditions apply are those associated, at the end of step b), with holes or defects of crystalline origin; • the high level, called first high level, and / or the low level, called first low level, used in step c) for a secondary defect associated with a hole in step b), are different from the high level, called second high level, and / or the low level, called second low level, used in step c) for a secondary defect associated with a defect of crystalline origin in step b); • Step c) of the final classification is also based on the application of the following second conditions: - if a secondary defect is identified as a bubble-type defect in step b) with a similarity level greater than a third level, said secondary defect is definitively classified as a bubble, - if a secondary defect is identified as a bubble-type defect in step b) with a similarity level less than or equal to the third level, said secondary defect is definitively classified as not being a defect; • Step c) of the final classification is also based on the application of the following third conditions: - if a secondary defect is identified as a scratch-type defect in step b) with a similarity level greater than a fourth level, said secondary defect is definitively classified as a scratch, - if a secondary defect is identified as a scratch-type defect in step b) with a similarity level less than or equal to the fourth level, said secondary defect is definitively classified as not being a defect; • each secondary defect detected in step a) has a size greater than Ipm, 5qm, or even 1Oum; • step c) leads to a grading of the composite structure and a physical sorting allowing to separate downgraded composite structures and composite structures meeting the specifications; • A composite structure is downgraded when it has a density of crystalline defects greater than 0.25 defects / cm2 and / or an overall density of bubbles and holes greater than 0.2 defects / cm2.
[0022] The invention also relates to a composite structure formed of a thin layer of monocrystalline silicon carbide disposed on a support substrate of polycrystalline silicon carbide, said composite structure having a density of defects of crystalline origin less than or equal to 0.25 defects / cm2 and an overall density of bubbles and holes less than or equal to 0.2 defects / cm2. BRIEF DESCRIPTION OF THE FIGURES
[0023] Other features and advantages of the invention will become apparent from the detailed description of the invention which follows with reference to the accompanying figures in which:
[0024] [Fig.1] The [Fig.1] presents a donor substrate, a composite structure without and with epitaxial layer;
[0025] [Fig.2a]
[0026] [Fig.2b] Fig.2a and Fig.2b illustrate different types of defects with their final classification by application of the specific conditions of the control process according to the present invention. DETAILED DESCRIPTION OF THE INVENTION
[0027] In the following description, we will conventionally call "primary defects" the defects present on and / or in a donor substrate 1, "secondary defects" the defects present on and / or in the thin layer 10 of a composite structure 100, and "tertiary defects" the defects present on and / or in the raw epitaxial layer 150 on the thin layer of a composite structure 100.
[0028] In a principal plane (x,y), the donor substrate 1 and the composite structure 100, with or without an epitaxial layer 150, are preferably in the form of circular wafers with diameters of 100 mm, 150 mm, 200 mm, or even larger. They could, however, be in any other form allowing for their subsequent processing for the manufacture of components. The thickness of the substrate and structures extends along the z-axis in [Fig. 1]. It is the front faces 1a, 10a, 150a of the substrate 1 and the structure 100 that are inspected and are likely to contain the aforementioned defects.
[0029] Before starting the manufacture of a composite structure 100 which includes a thin layer of monocrystalline silicon carbide (c-SiC) transferred onto a support substrate of polycrystalline silicon carbide (p-SiC), it is important to carry out a quality control of the donor substrate 1 (in c-SiC, for example of type 4H-SiC) from which the thin layer will be taken.
[0030] An inspection of the front face of the donor substrate 1 by a photoluminescence imaging technique as described in references [1] and [2] cited in the introduction, is preferably carried out to detect and classify defects, referred to as primary defects hereafter.
[0031] Primary defects are made visible in photoluminescence imaging due to local variations in the intensity of a photoluminescence (PL) signal emitted by the front face following excitation by an incident beam. These local variations in the intensity of the PL signal reflect modified properties of the material (stress, roughness, flatness, etc.), corresponding to the primary defect. The latter can appear on the generated PL image as white or black spot(s), depending on its characteristics.
[0032] This inspection step can be performed on known equipment, for example, the SICA88 (Lasertec), the Photoluminescence Scanner (Intego), or the MiPlato SiC (EtaMax). These devices typically combine an optical microscopy image with a photoluminescence image of the same defect to assign dimensional (size, area) and intensity (contrast) criteria based on the measured optical and photoluminescence signals. They also assign a typology criterion (predefined defect classes) to each primary defect, based on a learning algorithm. The predefined classes typically correspond to micro-hole (micro-pipe) defects, stacking faults (SF), pits, bumps, scratches, and particles.
[0033] After inspection and potential downgrading of certain donor substrates 1 whose density and / or defect type would be incompatible with the transfer of a viable thin film, the composite structure 100 can be fabricated. Any thin-film transfer technique can be implemented, in particular the Smart Cut process. This process, based on light ion implantation and direct assembly by molecular adhesion, will not be described in detail here as it is well known. To obtain the composite structure 100, it is implemented with a c-SiC donor substrate 1 and a p-SiC support substrate 20.
[0034] Typically, the thickness of the thin film 10 is between 20 nm and 1000 nm, and its free surface has a roughness less than or equal to 0.5 nm RMS, or even 0.1 nm RMS (measured by atomic force microscopy, on scans, for example, from 1000 sq m² to 30 x 30 sq m²). The support substrate 20 can have a thickness between approximately 50 sq m and several hundred micrometers, for example, between 200 sq m and 700 sq m. Optionally, the composite structure 100 can include an intermediate layer, interposed between the thin film 10 and the support substrate 20. This intermediate layer can be made of a dielectric material, semiconductor or metallic (such as, for example, silicon dioxide, silicon, silicon carbide, tungsten, titanium, etc.). A bonding interface is present between the thin film 10 and the support substrate 20, or adjacent to the intermediate layer.
[0035] The present invention relates to a method for quality control of a composite structure 100 comprising a thin film 10 of c-SiC disposed on a support substrate 20 of p-SiC ([Fig. 1]).
[0036] The control method includes a step a) of inspecting a free surface 10a of the thin film 10 by a technique combining confocal microscopy in visible light (differential interference contrast - DIC) and photoluminescence imaging, to detect defects, called secondary defects, present on and / or in the thin film 10. The defects detected typically have a size (or equivalent diameter) greater than or equal to Ipm, greater than or equal to 5qm, or even greater than or equal to 1Oum.
[0037] Differential interference contrast (also called Nomarski) is a lighting technique that makes it possible to discern minute variations in the topography of a surface by exploiting the interference of light waves.
[0038] Photoluminescence imaging is based on local variations in the intensity of a photoluminescence (PL) signal emitted by the free surface 10a, following excitation by an incident electromagnetic beam. In a SICA88 instrument, which will be preferred in the remainder of this description, the incident excitation beam has a wavelength of 313 nm and the emitted photoluminescence signal is collected in a wavelength range from 700 nm to 1000 nm. The local variations in the intensity of the PL signal reflect modified properties of the material (stress, roughness, flatness, etc.), corresponding to the secondary defect. The latter can appear on the generated PL image as white or black spot(s), depending on its characteristics.
[0039] In step a), a visible light image - hereafter referred to as a DIC image - and a photoluminescence image - hereafter referred to as a PL image - are formed for each secondary defect detected.
[0040] Secondary defects can be induced by primary defects or by particles or other surface contaminants which have not been totally eliminated before the assembly of the donor substrate 1 and the support substrate 20. They can therefore be defects of crystalline origin, bonding defects such as holes (local absence of thin film 10) or bubbles (defect at the bonding interface 40, at which the thin film 10 is not bonded and forms a blister), or surface defects such as scratches or film chips (“flakes”) present on the front face 10a of the thin film.
[0041] The control process then includes a step b) corresponding to the preliminary identification of secondary defects, by similarity, on the basis of their DIC image.
[0042] The equipment mentioned above (and in particular the SICA88) is capable of identifying secondary defects based on images captured by visible light microscopy, using an image recognition algorithm. The algorithm is fed and trained with verified images of defects likely to be present on and / or in a thin film 10, such as holes, bubbles, scratches, chips, and defects of crystalline origin: it can thus learn to recognize each of these defects and calculate a level of similarity between the DIC image of the detected defect and its database, taking into account criteria of size, shape, color, contrast, etc.
[0043] At the end of step b), each secondary defect is associated with an identified type of defect (for example, hole, bubble, scratch, chip or defect of crystalline origin), with a certain level of similarity (between 0 and 1).
[0044] Due to the presence of a polycrystalline support substrate 20 under the thin layer 10, the preliminary identification carried out automatically by the equipment is not sufficiently reliable to decide on the quality of the composite structure 100. Indeed, this identification can be impacted by the underlying grains and the level of similarity attributed to the secondary defect does not by itself allow to determine whether said defect is indeed of the identified type or whether it is a "false defect", for example related to the p-SiC grains.
[0045] The control process therefore includes a step c) of final classification of secondary defects by application of particular conditions, set out below.
[0046] Some initial conditions apply in particular to secondary defects identified in step b) as being holes or defects of crystalline origin: i. if the level of similarity associated with a secondary defect is greater than a high level, said secondary defect is definitively classified in the identified type of defect; ii. If the similarity level associated with the secondary defect is between a low and a high level, the PL image of said defect is analyzed by an image recognition algorithm trained on different labeled types of defects. Two cases may then arise: - if the secondary fault is associated with a labeled type of fault, said secondary fault is definitively classified in the identified type of fault; - If the secondary fault is not recognized as a labeled type of fault, said secondary fault is permanently classified as not being a defect (i.e., considered as a "false defect"); iii. Finally, if the level of similarity associated with the secondary defect is lower than the low level, said secondary defect is also definitively classified as not being a defect.
[0047] A "false defect" is a defect detected and associated with a given type (with a certain level of similarity) in step b), but which does not actually correspond to said type: it may either correspond to another type of defect in the thin film, to a measurement artifact or to the image of a grain of the underlying support substrate 20.
[0048] The PL image recognition algorithm is fed and trained with verified PL images of defects likely to be present on and / or in a thin film 10: it can thus learn to recognize each of these defects and classify them into predefined categories (i.e., associate them with labeled defect types), taking into account criteria such as size, shape, contrast, etc. A labeled defect type therefore corresponds to a real defect, whose signature on the PL image is expressed by specific criteria (as mentioned above).
[0049] In condition (ii), when the PL image associated with the analyzed secondary defect corresponds to one of the labeled types of defects, this confirms that we are dealing with a "true defect" and the secondary defect is thus classified in the type identified in step b); on the other hand, when the PL image does not correspond to any of the labeled types of defects and / or the PL image does not show any particular contrast (no PL signal), the secondary defect is considered a "false defect", although it has been associated with an identified type of defect in step b).
[0050] Note that the high level (called first high level) and / or the low level (called first low level) used in step c) to judge the level of similarity of the DIC image, for a secondary defect associated with a hole, may be different from the high level (called second high level) and / or the low level (called second low level) used for a secondary defect associated with a defect of crystalline origin.
[0051] Second conditions may apply to secondary defects identified in step b) as being bubbles: i. If the similarity level associated with a secondary defect identified as a bubble is greater than a third level, said secondary defect is definitively classified as a bubble, ii. if the level of similarity is less than or equal to the third level, the said secondary defect is definitively classified as not being a defect.
[0052] Third conditions may apply to secondary defects identified in step b) as being scratches: i. If the level of similarity associated with a secondary defect identified as a scratch is greater than a fourth level, said secondary defect is definitively classified as a scratch, ii. if the level of similarity is less than or equal to the fourth level, the said secondary defect is definitively classified as not being a defect.
[0053] This final classification step c) allows for the reliable categorization of critical defects present on and / or in the thin layer 10, and avoids the consideration of "false defects", particularly those related to the detection of the underlying grains of the support substrate 20.
[0054] By way of example, the inspection method is applied to a composite structure 100 comprising a 600 nm thick c-SiC thin film deposited on a 150 mm diameter p-SiC support substrate. The equipment used is the SICA88. In the examples in [Fig. 2a] and [Fig. 2b], the first low and first high similarity levels, used in step c) to assess the similarity level associated with secondary defects identified as holes, are defined at 0.3 and 0.85, respectively. The second low and second high similarity levels, used in step c) to assess the similarity level associated with secondary defects identified as crystalline defects, are defined at 0.3 and 0.85, respectively. The third level, used in step c) to assess the similarity level associated with secondary defects identified as bubbles, is defined at 0.85.Finally, the fourth level used in step c) to judge the level of similarity associated with secondary defects identified as scratches is set at 0.97.
[0055] Figures 2a and 2b illustrate hole-type defects, crystalline defects, bubbles and scratches detected on and / or in the thin film 10:
[0056] [1] DIC Image: Similarity level higher than the first high level => Defect classified as a hole;
[0057] [2] DIC Image: Similarity level between the first high level and the first low level => Defect classified as a hole because a labeled defect was detected on the PL image;
[0058] [3] DIC Image: Similarity level higher than the second high level => Defect classified as a defect of crystalline origin;
[0059] [4] DIC Image: Similarity level between the second high level and the second low level => Defect classified as a crystalline origin defect because a labeled defect was detected on the PL image;
[0060] [5] DIC Image: Similarity level lower than the second low level => Defect secondary classified as not being a defect of crystalline origin;
[0061] [6] DIC Image: Similarity level higher than the third level => Defect classified in a bubble;
[0062] [7] DIC Image: Similarity level greater than the fourth level => Classified defect striped.
[0063] Following step c) of the inspection process, a grade can be assigned to the inspected composite structures 100. In particular, a superior grade can be assigned to structures 100 with an overall density of bubbles and holes less than or equal to 0.2 defects / cm². Preferably, such a grade will be assigned to composite structures 100 with an overall density of bubbles, holes, and scratches less than or equal to 0.2 defects / cm². Even more preferably, the superior grade can be assigned to structures 100 with an overall density of defects, excluding crystalline defects, less than or equal to 0.2 defects / cm².
[0064] The acceptable crystalline defect density is defined according to the quality level of the donor substrate 1 from which the thin film 10 is derived. Advantageously, a crystalline defect density less than or equal to 0.25 / cm2 for a structure 100 gives it a superior quality grade.
[0065] Conversely, a lower grade, leading to the downgrading of a composite structure 100 at the end of step c), may be defined for an overall defect density greater than the thresholds previously stated.
[0066] The present invention therefore also relates to a composite structure 100 formed of a thin layer 10 of monocrystalline silicon carbide deposited on a support substrate 20 of polycrystalline silicon carbide. Said composite structure, inspected in the final stage using the inspection method according to the invention, exhibits: - a crystalline defect density less than or equal to 0.25 defects / cm2, less than or equal to 0.2 defects / cm2, or even less than or equal to 0.18 defects / cm2, or even less than or equal to 0.15 defects / cm2, and - an overall density of bubbles and holes (and possibly scratches) less than or equal to 0.2 defects / cm2, less than or equal to 0.18 defects / cm2, less than or equal to 0.15 defects / cm2, or even less than or equal to 0.12 defects / cm2, or even less than or equal to 0.1 defects / cm2.
[0067] After grading, a physical sorting of the composite structures 100 allows the downgraded structures 100 to be separated from the structures 100 that meet the specifications.
[0068] In a subsequent manufacturing step, epitaxial growth of SiC can be performed on the thin layer 10 of the specified composite structures 100, in order to form the epitaxial layer 150 on and in which the devices will be fabricated ([Fig. 1]). The epitaxial growth step can be carried out according to known prior art techniques.
[0069] The quality control process for composite structures 100, prior to the epitaxial stage, ensures that the density of critical tertiary defects, related to quality of the thin layer 10, will be controlled, or even lower than a given specification, because this process allows a reliable classification of defects present on and / or in the thin layer 10, without over-quality by downgrading expensive composite structures 100, on the basis of "false defects" induced in particular by an underlying polycrystalline substrate.
[0070] Of course, the invention is not limited to the embodiments and examples described, and alternative embodiments can be made without departing from the scope of the invention as defined by the claims.
Claims
Demands
1. A method for quality control of a composite structure (100) comprising a thin film (10) of monocrystalline silicon carbide deposited on a support substrate (20) of polycrystalline silicon carbide, the method comprising the following steps: a) inspection of a free surface (10a) of the thin film (10) by a technique combining confocal microscopy in visible light and photoluminescence imaging, enabling the detection of defects, referred to as secondary defects, present on and / or in the thin film (10), and the formation, for each defect, of a visible light image and a photoluminescence image; b) preliminary identification of the secondary defects, by similarity, based on their visible light image, using an image recognition algorithm trained on different types of defects likely to be present on and / or in a thin film (10), such as holes, bubbles, scratches,crystalline defects; at the end of step b), each secondary defect being associated with an identified type of defect, with a certain level of similarity, c) the final classification of at least some secondary defects by applying the following initial conditions: - if the level of similarity associated with a secondary defect is greater than a high level, said secondary defect is definitively classified in the identified type of defect, - if the level of similarity associated with the secondary defect is between a low level and a high level, the photoluminescence image of said defect is analyzed by an image recognition algorithm trained on different labeled types of defects; if the secondary defect is associated with a labeled type of defect, said secondary defect is definitively classified in the identified type of defect, - in other cases, the secondary defect is definitively classified as not being a defect.
2. A method for quality control of a composite structure (100) according to claim 1, wherein the secondary defects to which the first conditions apply are those associated, at the end of step b), with holes or defects of crystalline origin.
3. A method for quality control of a composite structure (100) according to claim 2, wherein the high level, referred to as the first high level, and / or the low level, referred to as the first low level, used in step c) for a secondary defect associated with a hole in step b), are different from the high level, referred to as the second high level, and / or the low level, referred to as the second low level, used in step c) for a secondary defect associated with a defect of crystalline origin in step K'
4. D). A method for quality control of a composite structure (100) according to any one of claims 1 to 3, wherein the final classification step c) is also based on the application of the following second conditions: - if a secondary defect is identified as a bubble-type defect in step b) with a similarity level greater than a third level, said secondary defect is definitively classified as a bubble, - if a secondary defect is identified as a bubble-type defect in step b) with a similarity level less than or equal to the third level, said secondary defect is definitively classified as not being a defect.
5. A method for quality control of a composite structure (100) according to any one of claims 1 to 4, wherein the final classification step c) is also based on the application of the following third conditions: - if a secondary defect is identified as a scratch-type defect in step b) with a similarity level greater than a fourth level, said secondary defect is definitively classified as a scratch, - if a secondary defect is identified as a scratch-type defect in step b) with a similarity level less than or equal to the fourth level, said secondary defect is definitively classified as not being a defect.
6. A method for quality control of a composite structure (100) according to any one of claims 1 to 5, wherein each secondary defect detected in step a) has a size greater than Ipm, 5qm, or even 1Oum.
7. A method for quality control of a composite structure (100) according to any one of claims 1 to 6, wherein step c) leads to a grading of the composite structure (100) and a physical sorting allowing to separate downgraded composite structures and composite structures meeting the specifications.
8. A method for quality control of a composite structure (100) according to claim 7, wherein a composite structure (100) is downgraded when it has a crystalline defect density greater than 0.25 defects / cm2 and / or an overall bubble and hole density greater than 0.2 defects / cm2.