Phase unwrapping method, device, equipment, medium and product

Abstract

The present invention discloses a phase unwrapping method, device, equipment, medium and product. The method includes: obtaining an interference fringe image to be processed and unwrapping operation prompt information corresponding to the wafer surface; the unwrapping operation prompt information is used to prompt the logical reasoning process of phase extraction and phase unwrapping, the intermediate reasoning results and the judgment method of abnormal phase jumps; the interference fringe image to be processed and the unwrapping operation prompt information are processed according to the unwrapping image generation model obtained in advance, and an unwrapping image corresponding to the interference fringe image to be processed is obtained, so as to determine the surface shape information corresponding to the wafer surface based on the unwrapping image. The technical solution of the embodiment of the present invention realizes the effect of effectively processing the abnormal phase jumps generated in the process of phase unwrapping of the phase image and generating an unwrapping image with higher accuracy.

Description

Technical Field

[0001] The present invention relates to the field of image processing technology, and in particular to a phase unwrapping method, device, equipment, medium and product. Background Art

[0002] To accurately recover the wafer surface topography, phase extraction and unwrapping are key steps in the interferometric measurement process. Due to the limitations of interference fringe denoising methods, residual noise can cause abnormal phase jumps during the phase extraction and unwrapping process, resulting in erroneous phase unwrapping results.

[0003] In the related technology, in the current interference fringe phase extraction and unwrapping method, there may be a problem of phase unwrapping discontinuity caused by improper handling of abnormal phase jumps, and the abnormal phase jumps cannot be effectively handled, which in turn causes the calculated wafer surface shape to deviate from the actual situation, affecting the accuracy of the wafer surface shape solution. Summary of the Invention

[0004] The present invention provides a phase unwrapping method, apparatus, device, medium and product to effectively process abnormal phase jumps generated during phase unwrapping of a phase image and generate an unwrapped image with higher precision.

[0005] According to one aspect of the present invention, a phase unwrapping method is provided, the method comprising:

[0006] Obtaining an interference fringe image to be processed corresponding to the wafer surface and unwrapping operation prompt information; wherein the unwrapping operation prompt information is used to prompt the logical reasoning process of phase extraction and phase unwrapping, intermediate reasoning results, and a method for distinguishing abnormal phase jumps;

[0007] Processing the interference fringe image to be processed and the unwrapping operation prompt information according to a pre-trained unwrapping image generation model, and obtaining an unwrapping image corresponding to the interference fringe image to be processed, so as to determine surface shape information corresponding to the wafer surface based on the unwrapping image;

[0008] Among them, the unwrapped image generation model includes at least a feature extraction sub-model, an unwrapping feature generation sub-model, a phase unwrapping feature generation sub-model and a feature conversion sub-model; the feature extraction sub-model is used to extract the image features of the interference fringe image to be processed; the unwrapping feature generation sub-model is used to generate an unwrapping operation prompt vector based on the unwrapping operation prompt information; the phase unwrapping feature generation sub-model is used to generate a phase unwrapping image feature corresponding to the interference fringe image to be processed based on the image features and the unwrapping operation prompt vector; the feature conversion sub-model is used to convert the phase unwrapping image feature into an unwrapped image corresponding to the interference fringe image to be processed.

[0009] According to another aspect of the present invention, a phase unwrapping device is provided, the device comprising:

[0010] An image acquisition module is used to acquire the interference fringe image to be processed corresponding to the wafer surface and unwrapping operation prompt information; wherein the unwrapping operation prompt information is used to prompt the logical reasoning process of phase extraction and phase unwrapping, intermediate reasoning results, and the judgment method of abnormal phase jump;

[0011] A phase unwrapping module is used to process the interference fringe image to be processed and the unwrapping operation prompt information according to a pre-trained unwrapping image generation model, and obtain an unwrapping image corresponding to the interference fringe image to be processed, so as to determine the surface shape information corresponding to the wafer surface based on the unwrapping image; wherein the unwrapping image generation model includes at least a feature extraction sub-model, an unwrapping feature generation sub-model, a phase unwrapping feature generation sub-model and a feature conversion sub-model; the feature extraction sub-model is used to extract the image features of the interference fringe image to be processed; the unwrapping feature generation sub-model is used to generate an unwrapping operation prompt vector according to the unwrapping operation prompt information; the phase unwrapping feature generation sub-model is used to generate a phase unwrapping image feature corresponding to the interference fringe image to be processed based on the image features and the unwrapping operation prompt vector; the feature conversion sub-model is used to convert the phase unwrapping image feature into an unwrapping image corresponding to the interference fringe image to be processed.

[0012] According to another aspect of the present invention, an electronic device is provided, comprising:

[0013] at least one processor; and

[0014] a memory communicatively connected to the at least one processor; wherein,

[0015] The memory stores a computer program executable by the at least one processor. The computer program is executed by the at least one processor to enable the at least one processor to perform the phase unwrapping method described in any embodiment of the present invention.

[0016] According to another aspect of the present invention, a computer-readable storage medium is provided, wherein the computer-readable storage medium stores computer instructions, and the computer instructions are used to enable a processor to implement the phase unwrapping method described in any embodiment of the present invention when executed.

[0017] According to another aspect of the present invention, a computer program product is provided. The computer program product includes a computer program. When the computer program is executed by a processor, the phase unwrapping method according to any embodiment of the present invention is implemented.

[0018] The technical solution of the embodiment of the present invention provides a data basis and operational guidance for subsequent phase unwrapping by obtaining a to-be-processed interference fringe image corresponding to the wafer surface and unwrapping operation prompt information. In addition, the unwrapping operation prompt information is used to at least prompt the logical reasoning process, intermediate reasoning results, and abnormal phase jump discrimination method of phase extraction and phase unwrapping, providing operational guidance for discriminating and processing abnormal phase jumps during the phase unwrapping process. Furthermore, the to-be-processed interference fringe image and the unwrapping operation prompt information are processed according to a pre-trained unwrapping image generation model, and an unwrapped image corresponding to the to-be-processed interference fringe image is obtained, so as to determine the surface shape information corresponding to the wafer surface based on the unwrapped image. This solves the problem in the related art that abnormal phase jumps cannot be effectively processed during phase extraction and phase unwrapping, thereby resulting in discontinuous phase unwrapping. This achieves the effect of effectively processing abnormal phase jumps generated during phase unwrapping of the phase image and generating a more accurate unwrapped image. By using the unwrapping operation prompt information as a model input object, operational guidance is provided for the model to perform abnormal phase jump discrimination and processing, ensuring the accuracy and continuity of the phase information in the unwrapped image.

[0019] It should be understood that the content described in this section is not intended to identify the key or important features of the embodiments of the present invention, nor is it intended to limit the scope of the present invention. Other features of the present invention will become readily understood through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0021] Figure 1 This is a flow chart of a phase unwrapping method provided according to the first embodiment of the present invention;

[0022] Figure 2 This is a flow chart of a phase unwrapping method provided according to the second embodiment of the present invention;

[0023] Figure 3 This is a flow chart of a phase unwrapping method provided according to the second embodiment of the present invention;

[0024] Figure 4 is a flow chart of a phase unwrapping method provided according to embodiment 3 of the present invention;

[0025] Figure 5 2 is a schematic structural diagram of a phase unwrapping device provided according to a fourth embodiment of the present invention;

[0026] Figure 6 It is a structural diagram of an electronic device for implementing the phase unwrapping method according to an embodiment of the present invention. DETAILED DESCRIPTION

[0027] In order to enable those skilled in the art to better understand the solutions of the present invention, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the embodiments described are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts should fall within the scope of protection of the present invention.

[0028] It should be noted that the terms "first", "second", etc. in the description and claims of the present invention and the above-mentioned drawings are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that the numbers used in this way can be interchanged where appropriate so that the embodiments of the present invention described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions. For example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0029] Example 1

[0030] Figure 1 This is a flow chart of a phase unwrapping method provided in the first embodiment of the present invention. This embodiment is applicable to the case where phase extraction and phase unwrapping are performed on the interference fringe image after denoising. The method can be performed by a phase unwrapping device, which can be implemented in the form of hardware and / or software and can be configured in a terminal and / or server. Figure 1 As shown, the method includes:

[0031] S110 , obtaining an interference fringe image to be processed corresponding to the wafer surface and unpacking operation prompt information.

[0032] A wafer refers to the silicon wafer used in the manufacture of semiconductor integrated circuits. Due to its circular shape, it is therefore called a wafer. Wafers are a key material in the manufacturing of high-end computing chips, and the accuracy of their surface shape significantly impacts the implementation of manufacturing technology. Therefore, accurate measurement of the wafer surface is an indispensable step in semiconductor manufacturing, not only crucial for improving wafer yield but also fundamental to ensuring the reliability of subsequent chip manufacturing processes. Generally, analyzing the surface structure of a wafer surface typically involves four main steps: image preprocessing, phase extraction, phase unwrapping, and wavefront fitting. In this embodiment, phase extraction and phase unwrapping are primarily performed on the interference fringe image obtained after image preprocessing to produce an unwrapped image. The wafer surface refers to the outermost layer of the wafer. The interference fringe image to be processed can be the interference fringe image to be subjected to phase extraction and phase unwrapping. The unwrapping operation prompt information can be understood as information that provides an explanatory description of the phase extraction and phase unwrapping operations. The unwrapping operation prompt information at least describes the logical reasoning process of phase extraction and phase unwrapping, the intermediate reasoning results, and the method for identifying abnormal phase jumps. The logical reasoning process for phase extraction and phase unwrapping can be used to characterize the computational steps required for phase extraction and phase unwrapping of the interference fringe image being processed. It can be understood that when analyzing the surface structure of a wafer surface, phase extraction typically involves processing the preprocessed interference fringe image to determine the phase difference resulting from the interference between the reference and measurement beams. Phase unwrapping, also known as phase unwrapping, sequentially unwraps the phase according to the phase unwrapping sequence to form a smooth and continuous phase unwrapping image. Furthermore, during the phase unwrapping process, phase jumps are processed to resolve phase overlap. Intermediate inference results can be used to characterize the intermediate results obtained during the computational process. The method for identifying abnormal phase jumps can be understood as the method for identifying phase jumps to be excluded. Abnormal phase jumps can be phase jumps that need to be excluded. Abnormal phase jumps generally refer to non-real phase jumps caused by various factors during signal processing, such as noise, interference, or equipment failure. Abnormal phase jumps are not related to the actual phase change of the signal itself but are caused by external factors. Phase jump refers to the phenomenon that the signal phase suddenly jumps from one value to another in a short period of time.

[0033] In this embodiment, the interference fringe image to be processed may be an interference fringe image obtained after image denoising. Furthermore, the interference fringe image to be processed corresponding to the wafer surface may be obtained by performing image denoising on the interference fringe image already obtained corresponding to the wafer surface.

[0034] Based on this, the above-mentioned technical solutions also include: obtaining the original interference fringe image corresponding to the wafer surface; denoising the original interference fringe image according to a preset image denoising method to obtain the interference fringe image to be processed corresponding to the wafer surface.

[0035] The original interference fringe image can be an interference fringe image obtained by measuring the wafer surface using an interferometer. Generally, the wafer is placed on the interferometer's measurement platform, ensuring that the wafer surface is perpendicular to the interferometer's measurement optical path. Furthermore, a beam splitter within the interferometer separates the light into reference light and measurement light. The reference light travels through a fixed path within the interferometer, while the measurement light is reflected off the wafer surface. The reference light and measurement light meet within the interferometer and interfere with each other, forming interference fringes. Furthermore, a camera can be used to capture the interference fringe image, and the captured interference fringe image can be used as the original interference fringe image corresponding to the wafer surface. The interferometer used can be a measuring instrument suitable for wafer surface measurement, optionally including a Fizeau interferometer or a Michelson interferometer. The camera can be any device capable of image capture, optionally including a charge-coupled device camera. Image denoising can be performed by correcting and enhancing the interference fringe image to reduce noise. Optionally, image denoising methods include frequency domain low-pass filtering, spin filtering, homomorphic filtering, partial differential equations, and wavelet transforms.

[0036] As an optional implementation of this embodiment, the wafer can be placed on the measuring platform of the Fizeau interferometer, and further, the light can be divided into reference light and measurement light by a beam splitter inside the interferometer. The reference light and the measurement light meet inside the interferometer and produce an interference phenomenon, forming interference fringes. Furthermore, a charge-coupled device camera can be used to capture the interference fringe image, and the captured interference fringe image can be used as the original interference fringe image corresponding to the wafer surface. Furthermore, the original interference fringe image can be denoised by a preset denoising method, and the image obtained after denoising is used as the interference fringe image to be processed.

[0037] It should be noted that to clearly and intuitively describe the phase extraction and unwrapping process of interference fringe images, the intermediate inference results and the identification method of abnormal phase jumps can be represented in the form of images. Furthermore, to reduce the amount of data required for subsequent data processing and improve data processing efficiency, feature extraction can be performed on the image, and unwrapping operation prompt information can be constructed based on the extracted image features.

[0038] Based on this, on the basis of the above-mentioned technical solutions, it also includes: obtaining unpacking process description information, wherein the unpacking process description information includes step description information of multiple unpacking operation steps, intermediate result images associated with each unpacking operation step, and phase jump discrimination images of abnormal phase jumps; performing feature extraction on the intermediate result image associated with at least one unpacking operation step to obtain intermediate result features associated with at least one unpacking operation step, and performing feature extraction on the phase jump discrimination image to obtain abnormal phase jump discrimination features corresponding to the abnormal phase jump; constructing unpacking operation prompt information based on the step description information of multiple unpacking operation steps, the intermediate result features associated with at least one unpacking operation step, and the abnormal phase jump discrimination features.

[0039] The unwrapping process description information can be understood as information that provides an explanatory description of the phase extraction and phase unwrapping process. The unwrapping operation steps can be the steps performed to perform phase extraction and phase unwrapping on the interference fringe image. The step description information can be text information that provides an explanatory description of the unwrapping operation steps. Exemplarily, the unwrapping process can include five unwrapping operation steps, and the step description information of each unwrapping operation step is as follows: step one, interference fringe image modeling; step two, substituting pixel intensity; step three, solving the equation system using the least squares method; step four, inverse tangent phase extraction; and step five, phase unwrapping. The intermediate result image can be a reference image that characterizes the execution status and reasoning results of the unwrapping operation steps. The phase jump discrimination image of the abnormal phase jump is used to characterize the discrimination method of the abnormal phase jump. In other words, the abnormal phase jump can be judged and identified based on the phase jump discrimination image.

[0040] As an optional implementation of this embodiment, unwrapping process description information can be obtained based on a priori indications of the interference fringe phase unwrapping process. The obtained unwrapping process description information can include step description information for multiple unwrapping operation steps, an intermediate result image associated with at least one unwrapping operation step, and a phase jump discrimination image for abnormal phase jumps. Furthermore, for the intermediate result image associated with at least one unwrapping operation step, feature extraction is performed on the intermediate result image according to a preset feature extraction algorithm, and the extracted image features are used as the intermediate result image associated with the unwrapping operation step. Furthermore, intermediate result features associated with the at least one unwrapping operation step can be obtained. Furthermore, feature extraction is performed on the phase jump discrimination image according to a preset feature extraction algorithm, and the extracted image features are used as abnormal phase jump discrimination features corresponding to the abnormal phase jump. Furthermore, unwrapping operation prompt information can be constructed based on the step description information for multiple unwrapping operation steps, the intermediate result features associated with at least one unwrapping operation step, and the abnormal phase jump discrimination features.

[0041] S120. Process the interference fringe image to be processed and the unwrapping operation prompt information according to the pre-trained unwrapping image generation model, and obtain an unwrapping image corresponding to the interference fringe image to be processed, so as to determine the surface shape information corresponding to the wafer surface based on the unwrapping image.

[0042] The unwrapped image generation model can be understood as a deep learning model that generates an unwrapped image that meets phase unwrapping requirements based on an input object. The unwrapped image generation model can be a deep learning model that takes as input the interference fringe image to be processed and unwrapping operation prompt information, and then, based on the unwrapping operation prompt information, performs phase extraction and phase unwrapping on the interference fringe image to generate an unwrapped image. The unwrapping operation prompt information is input into the unwrapping generation model to guide the unwrapping generation model to perform specific tasks and generate corresponding outputs. The unwrapped image generation model can be a deep learning model that includes at least one sub-model. The unwrapped image generation model includes at least a feature extraction sub-model, an unwrapping feature generation sub-model, a phase unwrapping feature generation sub-model, and a feature conversion sub-model. The unwrapped image can be an image containing original, continuous phase information. It can be understood that a phase map is a visual representation that displays phase information distributed in space or time. When the phase change exceeds a certain threshold (e.g., 2π), the phase information becomes "wrapped" or "folded," resulting in the absolute value of the phase value no longer accurately representing the original information. The goal of unwrapping an image is to eliminate this wrapping effect and restore the true phase information. Surface shape information can be used to characterize the surface condition of the wafer. Optionally, this surface shape information can include surface flatness, smoothness, cleanliness, and topographical characteristics.

[0043] The feature extraction submodel is used to extract image features from the interference fringe image to be processed. The feature extraction submodel can be a neural network model that takes the interference fringe image to be processed as input and extracts features from the input. The feature extraction submodel can be a neural network model with any model structure. Optionally, the feature extraction submodel can include an encoder and a linear layer. The encoder can be used to extract features from the interference fringe image to be processed. The linear layer can be used to align the extracted features.

[0044] The unwrapping feature generation sub-model is used to generate an unwrapping operation hint vector based on the unwrapping operation hint information. The unwrapping feature generation sub-model can be a deep learning model that takes the unwrapping operation hint information as input and encodes the input to generate an operation hint vector. The unwrapping operation hint vector can be understood as a feature vector that represents the phase extraction operation, the phase unwrapping operation, and the discrimination of abnormal phase jumps. The unwrapping operation hint vector can serve as operational guidance for the phase unwrapping feature generation sub-model to perform phase extraction, phase unwrapping, and the discrimination and elimination of abnormal phase jumps. The unwrapping feature generation sub-model can be a neural network model with any model structure. Optionally, the unwrapping feature generation sub-model can include an unwrapping feature generation module and a first feature alignment module. The unwrapping feature generation module can be used to generate an operation hint feature vector based on the unwrapping operation hint information. The first feature alignment module can be used to align the generated operation hint feature vectors. The first feature alignment module can include a linear layer. It should be noted that the unwrapping feature generation model can be a neural network model capable of processing information that fuses visual and language information. In other words, the unwrapped feature generation module is able to process and understand information from different modalities, i.e., images and text, and achieve cross-modal interaction and reasoning.

[0045] Among them, the phase unwrapping feature generation submodel is used to generate phase unwrapping image features corresponding to the interference fringe image to be processed based on image features and unwrapping operation hint vectors. The phase unwrapping feature generation submodel can be a deep learning model that takes image features and unwrapping operation hint vectors as input objects, and performs phase extraction, phase unwrapping, and abnormal phase jump discrimination and elimination on image features based on the unwrapping operation hint vector to generate phase unwrapping image features. The phase unwrapping feature generation submodel may include a phase unwrapping feature generation module and a second feature alignment module. The phase unwrapping feature generation module can be used to perform phase extraction, phase unwrapping, and abnormal phase jump discrimination and elimination on image features based on the unwrapping operation hint vector to generate phase unwrapping image features. The second feature alignment module can be used to align the generated operation hint feature vectors. The second feature alignment module may include a linear layer.

[0046] The feature conversion submodel can be used to convert the phase-unwrapped image features into an unwrapped image corresponding to the interference fringe image to be processed. The feature conversion submodel can be a neural network model that uses the phase-unwrapped features as input and performs feature conversion on the input object. The feature conversion submodel can be a neural network model with any model structure. Optionally, the feature conversion submodel can include a decoder.

[0047] In practical applications, limitations of the image denoising method used in the image preprocessing stage can lead to abnormal phase jumps caused by errors in the phase extraction solution. Consequently, the phase unwrapping map obtained in the phase unwrapping stage exhibits obvious discontinuities or errors. These phase discontinuities or errors have a serious impact on the final results of the subsequent wavefront fitting, thus affecting the accuracy of the circular surface shape solution.

[0048] In response to the above situation, in this embodiment, unwrapping operation prompt information and a denoised interference fringe image to be processed can be obtained. Furthermore, an unwrapping image generation model can be pre-trained to perform phase extraction, phase unwrapping, and identification and elimination of abnormal phase jumps on an input image to generate an unwrapped image. The interference fringe image to be processed and the unwrapping operation prompt information are then input into the unwrapping image generation model. The unwrapping operation prompt information serves as a guide for the unwrapping operation process, and phase extraction, phase unwrapping, and identification and elimination of abnormal phase jumps are performed on the image to be processed based on the unwrapping operation prompt information. Consequently, an unwrapped image corresponding to the interference fringe image to be processed can be output.

[0049] In this embodiment, after obtaining the interference fringe image to be processed and the unwrapping operation prompt information, the interference fringe image to be processed and the unwrapping operation prompt information can be input into the unwrapping image generation model. Furthermore, the interference fringe image to be processed can be input into the feature extraction sub-model to perform feature extraction on the interference fringe image to be processed based on the feature extraction sub-model to obtain image features. Furthermore, the unwrapping operation prompt information can be input into the unwrapping feature generation sub-model to encode the unwrapping operation prompt information based on the unwrapping generation sub-model to obtain an unwrapping operation prompt vector. Furthermore, the image features and the unwrapping operation prompt vector can be input into the phase unwrapping feature generation sub-model to process the image features based on the unwrapping operation prompt vector based on the phase unwrapping feature generation sub-model and output an unwrapped image corresponding to the interference fringe image to be processed. Furthermore, wavefront fitting can be performed on the unwrapped image to obtain surface shape information corresponding to the wafer surface.

[0050] The technical solution of the embodiment of the present invention provides a data basis and operational guidance for subsequent phase unwrapping by obtaining a to-be-processed interference fringe image corresponding to the wafer surface and unwrapping operation prompt information. In addition, the unwrapping operation prompt information is used to at least prompt the logical reasoning process, intermediate reasoning results, and abnormal phase jump discrimination method of phase extraction and phase unwrapping, providing operational guidance for discriminating and processing abnormal phase jumps during the phase unwrapping process. Furthermore, the to-be-processed interference fringe image and the unwrapping operation prompt information are processed according to a pre-trained unwrapping image generation model, and an unwrapped image corresponding to the to-be-processed interference fringe image is obtained, so as to determine the surface shape information corresponding to the wafer surface based on the unwrapped image. This solves the problem in the related art that abnormal phase jumps cannot be effectively processed during phase extraction and phase unwrapping, thereby resulting in discontinuous phase unwrapping. This achieves the effect of effectively processing abnormal phase jumps generated during phase unwrapping of the phase image and generating a more accurate unwrapped image. By using the unwrapping operation prompt information as a model input object, operational guidance is provided for the model to perform abnormal phase jump discrimination and processing, ensuring the accuracy and continuity of the phase information in the unwrapped image.

[0051] Example 2

[0052] Figure 2 This is a flow chart of a phase unwrapping method provided in Example 2 of the present invention. Based on the previous example, the method for obtaining an unwrapped image is further refined. Optionally, a pre-trained unwrapped image generation model is used to process the interference fringe image to be processed and unwrapping operation prompt information, and obtain an unwrapped image corresponding to the interference fringe image to be processed. This method includes: inputting the interference fringe image to be processed into a feature extraction submodel for feature extraction to obtain image features corresponding to the interference fringe image to be processed; inputting the unwrapping operation prompt information into an unwrapping feature generation submodel for information encoding to generate an unwrapping operation prompt vector; inputting the image features and the unwrapping operation prompt vector into a phase unwrapping feature generation submodel to generate phase unwrapping image features corresponding to the interference fringe image to be processed; and inputting the phase unwrapping image features into a feature conversion submodel for feature conversion to obtain an unwrapped image corresponding to the interference fringe image to be processed. For specific implementation methods, please refer to the technical solution of this example. Technical terms that are identical or similar to those in the previous example are not repeated here.

[0053] like Figure 2 As shown, the method includes:

[0054] S210 , obtaining an interference fringe image to be processed corresponding to the wafer surface and unpacking operation prompt information.

[0055] S220. Input the interference fringe image to be processed into the feature extraction sub-model for feature extraction to obtain image features corresponding to the interference fringe image to be processed, and input the unpacking operation prompt information into the unpacking feature generation sub-model for information encoding to generate an unpacking operation prompt vector.

[0056] Image features can be understood as features that represent the underlying meaning of an image and are easily understood and processed by the model. Image features can be represented in any form, optionally in vector form. The unwrapping operation hint vector can be understood as a feature vector that represents the phase extraction operation, the phase unwrapping operation, and the discrimination of abnormal phase jumps. This feature vector facilitates model understanding and processing. The unwrapping operation hint vector can be a text vector that represents the unwrapping operation process.

[0057] As an optional implementation scheme of this embodiment, the feature extraction sub-model includes an encoder and a linear layer; the interference fringe image to be processed is input into the feature extraction sub-model for feature extraction to obtain image features corresponding to the interference fringe image to be processed, including: performing feature extraction on the interference fringe image to be processed based on the encoder to obtain features of the image to be processed; performing feature alignment on the features of the image to be processed based on the linear layer to obtain image features corresponding to the interference fringe image to be processed.

[0058] In this embodiment, the unwrapping feature generation sub-model may include an unwrapping feature generation module and a first feature alignment module. When the unwrapping operation prompt information is input into the unwrapping feature generation sub-model, the unwrapping operation prompt information may be processed based on the unwrapping feature generation model and the first feature alignment module in sequence to obtain an unwrapping operation prompt vector.

[0059] Optionally, the unpacking feature generation sub-model includes an unpacking feature generation module and a first feature alignment module; the unpacking operation prompt information is input into the unpacking feature generation sub-model for information encoding to generate an unpacking operation prompt vector, including: encoding the unpacking operation prompt information based on the unpacking feature generation module to obtain a prompt vector to be processed; aligning the prompt vector to be processed based on the first feature alignment module to obtain an unpacking operation prompt vector.

[0060] The prompt vector to be processed may be a feature vector obtained by encoding the unpacking operation prompt information.

[0061] In this embodiment, when the unwrapping prompt information is input into the unwrapping feature generation sub-model, the unwrapping prompt information can be encoded based on the unwrapping feature generation module to obtain a pending prompt vector. Furthermore, the pending prompt vector can be feature aligned based on the first feature alignment module to obtain an unwrapping operation prompt vector.

[0062] S230 , inputting the image features and the unwrapping operation hint vector into a phase unwrapping feature generation sub-model to generate phase unwrapping image features corresponding to the interference fringe image to be processed.

[0063] In this embodiment, after obtaining the image features and the unwrapping operation hint vector, the image features and the unwrapping operation hint vector can be input into the phase unwrapping feature generation sub-model to process the image features according to the unwrapping operation hint vector based on the phase unwrapping feature sub-model and generate phase unwrapped image features.

[0064] Optionally, the phase unwrapping feature generation submodel includes a phase unwrapping feature generation module and a second feature alignment module; the image features and the unwrapping operation prompt vector are input into the phase unwrapping feature generation submodel to generate phase unwrapping image features corresponding to the interference fringe image to be processed, including: processing the image features and the unwrapping operation prompt vector based on the phase unwrapping feature generation module to generate the unwrapped image features to be processed; performing feature alignment on the unwrapped image features to be processed based on the second feature alignment module to obtain the phase unwrapped image features corresponding to the interference fringe image to be processed.

[0065] As an optional implementation of this embodiment, when the image features and the unwrapping operation hint vector are input into the phase unwrapping feature generation sub-model, the phase unwrapping feature generation module can process the image features and the unwrapping operation hint vector to generate unwrapped image features to be processed. Furthermore, the second feature alignment module can perform feature alignment on the unwrapped image features to be processed, and the features obtained after feature alignment are used as the phase unwrapped image features corresponding to the interference fringe image to be processed.

[0066] S240, inputting the phase unwrapping image features into the feature conversion sub-model for feature conversion to obtain an unwrapped image corresponding to the interference fringe image to be processed, and determining the surface shape information corresponding to the wafer surface based on the unwrapped image.

[0067] Among them, the feature conversion sub-model may include a decoder.

[0068] In this embodiment, after obtaining the phase-unwrapped image features, they can be input into the feature conversion sub-model. Furthermore, the decoder in the feature conversion sub-model can be used to perform feature conversion on the phase-unwrapped image features, generating an unwrapped image corresponding to the interference fringe image to be processed. Furthermore, the surface shape information corresponding to the wafer surface can be determined based on the unwrapped image.

[0069] For example, Figure 3 FIG. 1 is a flow chart of a phase unwrapping method according to an embodiment of the present invention. Figure 3 As shown, the interference fringe image to be processed and the unwrapping operation prompt information are input into the unwrapping image generation model. Furthermore, feature extraction can be performed on the interference fringe image to be processed based on the encoder in the feature extraction sub-model to obtain features of the image to be processed; feature alignment can be performed on the features of the image to be processed based on the linear layer in the feature extraction sub-model to obtain image features corresponding to the interference fringe image to be processed; and the unwrapping prompt information can be encoded based on the unwrapping feature generation module in the unwrapping feature generation sub-model to obtain a hint vector to be processed. Furthermore, feature alignment can be performed on the hint vector to be processed based on the first feature alignment module in the unwrapping feature generation sub-model to obtain an unwrapping operation hint vector. Furthermore, the image features and the unwrapping operation hint vector can be input into the phase unwrapping feature generation sub-model. The phase unwrapping feature generation module in the phase unwrapping feature generation sub-model processes the image features and the unwrapping operation hint vector to generate features of the unwrapped image to be processed. Furthermore, feature alignment can be performed on the unwrapped image features to be processed based on the second feature alignment module in the phase unwrapping feature generation sub-model, and the features obtained after the feature alignment are used as the phase unwrapping image features corresponding to the interference fringe image to be processed. Furthermore, the phase unwrapped image features are input into the feature conversion sub-model. Furthermore, feature conversion can be performed on the phase unwrapped image features based on the decoder in the feature conversion sub-model to obtain an unwrapped image corresponding to the interference fringe image to be processed.

[0070] The technical solution of the embodiment of the present invention is as follows: the interference fringe image to be processed is input into a feature extraction sub-model for feature extraction to obtain image features corresponding to the interference fringe image to be processed, and the unwrapping operation prompt information is input into an unwrapping feature generation sub-model for information encoding to generate an unwrapping operation prompt vector; further, the image features and the unwrapping operation prompt vector are input into a phase unwrapping feature generation sub-model to generate phase unwrapping image features corresponding to the interference fringe image to be processed; the phase unwrapping image features are input into a feature conversion sub-model for feature conversion to obtain an unwrapped image corresponding to the interference fringe image to be processed, thereby achieving the effect of effectively processing the abnormal phase jumps generated in the process of phase unwrapping of the phase image and generating a more accurate unwrapped image. By adopting the unwrapping operation prompt information as the model input object, operational guidance is provided for the model to perform abnormal phase jump discrimination and processing, thereby ensuring the accuracy and continuity of the phase information in the wrapped image.

[0071] Example 3

[0072] Figure 4 This is a flowchart of a phase unwrapping method provided in Example 3 of the present invention. Based on the previous example, before applying the unwrapped image generation model, a pre-built deep learning model can be trained. The unwrapped image is then generated based on the trained unwrapped image generation model. For detailed implementation details, please refer to the technical solution of this example. Technical terms that are identical or similar to those in the previous examples are not repeated here.

[0073] like Figure 4 As shown, the method includes:

[0074] S310, obtaining a plurality of training samples; wherein the training samples include a sample interference fringe image corresponding to a sample wafer surface, unwrapping operation prompt information, and a real unwrapping image corresponding to the sample interference fringe image.

[0075] It should be noted that before applying the unwrapped image generation model provided in this embodiment, a pre-built deep learning model can be trained in a supervised or unsupervised manner. Before training the deep learning model, multiple training samples can be constructed to train the model based on the multiple training samples. To improve the generation accuracy of the unwrapped image generation model, as many and rich training samples as possible can be constructed.

[0076] Among them, the sample interference fringe image can be an interference fringe image that has been denoised and is to be subjected to phase extraction and phase unwrapping processing. The sample interference fringe image can be an image obtained by photographing the interference fringes formed on the surface of the sample wafer through a camera device, or an image reconstructed by an image reconstruction model, or an image pre-stored from a storage space, etc. The unwrapping operation prompt information can be understood as information that explanatoryally describes the phase extraction and phase unwrapping operations. The unwrapping operation prompt information is at least used to describe the logical reasoning process of phase extraction and phase unwrapping, the intermediate reasoning results, and the method for distinguishing abnormal phase jumps. The true unwrapped image can be an image that performs phase unwrapping on the phase information in the sample interference fringes to restore the original, continuous phase information.

[0077] In this embodiment, in order to construct a large number of training samples in a rich and diverse manner, multiple sample interference fringe images corresponding to the sample wafer surfaces can be obtained. Furthermore, each sample interference fringe image can be processed separately to obtain a true unwrapped image corresponding to each sample interference fringe image. Furthermore, unwrapping operation prompt information can be obtained, and training samples can be constructed based on the sample interference fringe images corresponding to the sample wafer surfaces, the true unwrapped images corresponding to the sample interference fringe images, and the unwrapping operation prompt information, thereby obtaining multiple training samples. Furthermore, a pre-constructed deep learning model can be trained based on the multiple training samples to obtain a trained deep learning model, and the trained deep learning model can be used as an unwrapped image generation model.

[0078] S320: For multiple training samples, input the sample interference fringe images and unwrapping operation prompt information in the training samples into a pre-built deep learning model, and output the actual unwrapping image.

[0079] The deep learning model includes a feature extraction sub-model, an unwrapping feature generation sub-model, a phase unwrapping feature generation sub-model, and a feature conversion sub-model. The actual unwrapped image is the unwrapped image output after the sample interference fringe image and unwrapping operation prompt information are input into the deep learning model.

[0080] In this embodiment, for multiple training samples, the sample interference fringe images and unwrapping operation prompt information in the training samples can be input into a pre-built deep learning model. Furthermore, the sample interference fringe images can be input into a feature extraction sub-model to extract features from the sample interference fringe images based on the feature extraction sub-model, thereby obtaining sample image features. Furthermore, the unwrapping operation prompt information can be input into an unwrapping feature generation sub-model to encode the unwrapping operation prompt information based on the unwrapping generation sub-model to obtain an unwrapping operation prompt vector. Furthermore, the sample image features and the unwrapping operation prompt vector can be input into a phase unwrapping feature generation sub-model to process the sample image features based on the unwrapping operation prompt vector based on the phase unwrapping feature generation sub-model, thereby outputting an actual unwrapped image corresponding to the sample interference fringe image.

[0081] S330 , performing loss processing on the actual unwrapped image and the true unwrapped image according to the target loss function to obtain a target loss value.

[0082] Among them, the target loss function is determined based on the error loss function and the cross entropy loss function.

[0083] Exemplarily, the joint loss function can be determined based on the following formula:

[0084] L uni =αL1+(1-α)L cct

[0085] Among them, L uni represents the target loss function; α represents the weight factor; L1 represents the error loss function; L cct represents the cross entropy loss function.

[0086] In this embodiment, in order to effectively train the unwrapped image generation model to generate noise-free phase unwrapped images, loss processing can be performed on the actual unwrapped image and the true unwrapped image based on the target loss function obtained by weighted fusion of the two loss functions.

[0087] Optionally, loss processing is performed on the actual unwrapped image and the true unwrapped image according to a target loss function to obtain a target loss value, including: performing loss processing on the actual unwrapped image and the true unwrapped image according to an error loss function to obtain a first loss value; performing loss processing on the actual unwrapped image and the true unwrapped image according to a cross entropy loss function to obtain a second loss value; and performing weighted summation of the first loss value and the second loss value to obtain a target loss value.

[0088] The error loss function may be a function that represents the degree of difference between the image content of the actual unwrapped image and the true unwrapped image. The error loss function may be an L1 loss function. The cross entropy loss function may be a function that represents the degree of difference between the feature distributions of the actual unwrapped image and the true unwrapped image.

[0089] As an optional implementation of this embodiment, the actual unwrapped image and the true unwrapped image can be subjected to loss processing according to the error loss function to obtain a first loss value. Also, the actual unwrapped image and the true unwrapped image can be subjected to loss processing according to the cross entropy loss function to obtain a second loss value. Furthermore, a first weight corresponding to the first loss value can be determined, a second weight corresponding to the second loss value can be determined, and the product between the first loss value and the first weight can be determined to obtain a first loss value to be superimposed, and the product between the second loss value and the second weight can be determined to obtain a second loss value to be superimposed. Furthermore, the first loss value to be superimposed and the second loss value to be superimposed can be added to obtain a target loss value.

[0090] S340. Modify the model parameters in the deep learning model based on the target loss value, take the convergence of the target loss function in the deep learning as the training goal, and obtain the unwrapped image generation model.

[0091] Optionally, the unwrapping feature generation submodel includes an unwrapping feature generation module and a first feature alignment module; the phase unwrapping feature generation submodel includes a phase unwrapping feature generation module and a second feature alignment module; and the model parameters in the deep learning model are corrected based on the target loss value, including: correcting the model parameters of the feature extraction submodel, the first feature alignment module, the second feature alignment module and the feature conversion submodel based on the target loss value.

[0092] In this embodiment, during model training, the model parameters of the unwrapped feature generation module and the phase unwrapped feature generation module in the deep learning model can be frozen, and the model parameters of the feature extraction submodel, the first feature alignment module, the second feature alignment module, and the feature conversion submodel can be modified based on the target loss value. Subsequently, the training error of the target loss function in the deep learning model, i.e., the loss parameter, can be used as a condition for detecting whether the current target loss function has reached convergence. For example, the training error is less than a preset error, the error trend is stable, or the current number of model iterations is equal to a preset number. If convergence conditions are met, for example, the training error of the target loss function is less than a preset error or the error trend is stable, it indicates that deep learning model training is complete, and iterative training can be terminated. If convergence conditions are not met, additional training samples can be obtained to train the deep learning model until the training error of the target loss function is within a preset range. When the training error of the loss function reaches convergence, the trained deep learning model can be used as the unwrapped image generation model.

[0093] S350: Obtain the interference fringe image to be processed corresponding to the wafer surface and unpacking operation prompt information.

[0094] S360. Process the interference fringe image to be processed and the unwrapping operation prompt information according to the pre-trained unwrapping image generation model, and obtain an unwrapping image corresponding to the interference fringe image to be processed, so as to determine the surface shape information corresponding to the wafer surface based on the unwrapping image.

[0095] The technical solution of the embodiment of the present invention is to obtain multiple training samples; wherein the training samples include sample interference fringe images, unwrapping operation description information and real unwrapped images corresponding to the sample interference fringe images; further, for multiple training samples, the sample interference fringe images and unwrapping operation description information in the training samples are input into a pre-constructed deep learning model, and the actual unwrapped images are output; further, the actual unwrapped images and the real unwrapped images are subjected to loss processing according to the target loss function to obtain the target loss value; further, the model parameters in the deep learning model are corrected based on the target loss value, and the convergence of the target loss function in the deep learning is used as the training target to obtain the unwrapped image generation model, thereby achieving the effect of training an unwrapped image generation model based on the constructed training samples, which can perform phase unwrapping processing on the phase image and effectively process the generated abnormal phase jumps to generate the unwrapped image, thereby effectively improving the intelligence, accuracy and robustness of the model.

[0096] Example 4

[0097] Figure 5Schematic diagram of the structure of a phase unwrapping device provided by the fourth embodiment of the present invention. Figure 5 As shown, the device includes: an image acquisition module 410 and a phase unwrapping module 420.

[0098] Among them, the image acquisition module 410 is used to obtain the interference fringe image to be processed and the unwrapping operation prompt information corresponding to the wafer surface; wherein the unwrapping operation prompt information is at least used to describe the logical reasoning process of phase extraction and phase unwrapping, the intermediate reasoning results and the judgment method of abnormal phase jump; the phase unwrapping module 420 is used to process the interference fringe image to be processed and the unwrapping operation prompt information according to the unwrapping image generation model obtained by pre-training, and obtain the unwrapping image corresponding to the interference fringe image to be processed, so as to determine the surface shape information corresponding to the wafer surface based on the unwrapping image; wherein, the unwrapping The image generation model includes at least a feature extraction submodel, an unwrapping feature generation submodel, a phase unwrapping feature generation submodel and a feature conversion submodel; the feature extraction submodel is used to extract the image features of the interference fringe image to be processed; the unwrapping feature generation submodel is used to generate an unwrapping operation prompt vector based on the unwrapping operation prompt information; the phase unwrapping feature generation submodel is used to generate phase unwrapping image features corresponding to the interference fringe image to be processed based on the image features and the unwrapping operation prompt vector; the feature conversion submodel is used to convert the phase unwrapping image features into an unwrapping image corresponding to the interference fringe image to be processed.

[0099] The technical solution of the embodiment of the present invention provides a data basis and operational guidance for subsequent phase unwrapping by obtaining a to-be-processed interference fringe image corresponding to the wafer surface and unwrapping operation prompt information. In addition, the unwrapping operation prompt information is used to at least prompt the logical reasoning process, intermediate reasoning results, and abnormal phase jump discrimination method of phase extraction and phase unwrapping, providing operational guidance for discriminating and processing abnormal phase jumps during the phase unwrapping process. Furthermore, the to-be-processed interference fringe image and the unwrapping operation prompt information are processed according to a pre-trained unwrapping image generation model, and an unwrapped image corresponding to the to-be-processed interference fringe image is obtained, so as to determine the surface shape information corresponding to the wafer surface based on the unwrapped image. This solves the problem in the related art that abnormal phase jumps cannot be effectively processed during phase extraction and phase unwrapping, thereby resulting in discontinuous phase unwrapping. This achieves the effect of effectively processing abnormal phase jumps generated during phase unwrapping of the phase image and generating a more accurate unwrapped image. By using the unwrapping operation prompt information as a model input object, operational guidance is provided for the model to perform abnormal phase jump discrimination and processing, ensuring the accuracy and continuity of the phase information in the unwrapped image.

[0100] Optionally, the device further includes: an original image acquisition module and an image denoising module.

[0101] The original image acquisition module is used to acquire the original interference fringe image corresponding to the wafer surface;

[0102] The image denoising module is used to denoise the original interference fringe image according to a preset image denoising method to obtain a to-be-processed interference fringe image corresponding to the wafer surface.

[0103] Optionally, the device further includes: a process description information acquisition module, a feature extraction module and an operation description information determination module.

[0104] a process description information acquisition module, configured to acquire unpacking process description information, wherein the unpacking process description information includes step description information of a plurality of unpacking operation steps, an intermediate result image associated with each of the unpacking operation steps, and a phase jump discrimination image of an abnormal phase jump;

[0105] a feature extraction module, configured to perform feature extraction on an intermediate result image associated with at least one of the unwrapping operation steps to obtain an intermediate result feature associated with at least one of the unwrapping operation steps, and to perform feature extraction on the phase jump discrimination image to obtain an abnormal phase jump discrimination feature corresponding to the abnormal phase jump;

[0106] An operation prompt information determination module is used to construct unpacking operation prompt information based on step description information of multiple unpacking operation steps, intermediate result features associated with at least one of the unpacking operation steps, and the abnormal phase jump discrimination feature.

[0107] Optionally, the phase unwrapping module 420 includes: a feature extraction unit, a feature generation unit and a feature conversion unit.

[0108] a feature extraction unit, configured to input the interference fringe image to be processed into the feature extraction sub-model for feature extraction to obtain image features corresponding to the interference fringe image to be processed, and input the unwrapping operation prompt information into the unwrapping feature generation sub-model for information encoding to generate an unwrapping operation prompt vector;

[0109] a feature generation unit, configured to input the image feature and the unwrapping operation hint vector into the phase unwrapping feature generation sub-model to generate a phase unwrapping image feature corresponding to the interference fringe image to be processed;

[0110] A feature conversion unit is used to input the phase unwrapping image features into the feature conversion sub-model to perform feature conversion to obtain an unwrapped image corresponding to the interference fringe image to be processed.

[0111] Optionally, the unwrapping feature generation sub-model includes an unwrapping feature generation module and a first feature alignment module; the feature extraction unit includes: an information encoding sub-unit and an information alignment sub-unit;

[0112] an information encoding subunit, configured to encode the unpacking operation prompt information based on the unpacking feature generation module to obtain a prompt vector to be processed;

[0113] An information alignment subunit is configured to align the prompt vectors to be processed based on the first feature alignment module to obtain an unpacking operation prompt vector

[0114] Optionally, the phase unwrapping generation sub-model includes a phase unwrapping feature generation module and a second feature alignment module; the feature generation unit includes: a feature generation sub-unit and a feature alignment sub-unit.

[0115] a feature generation subunit, configured to process the image features and the unwrapping operation hint vector based on the phase unwrapping feature generation module to generate unwrapped image features to be processed;

[0116] The feature alignment subunit is configured to perform feature alignment on the features of the unwrapped image to be processed based on the second feature alignment module to obtain phase unwrapped image features corresponding to the interference fringe image to be processed.

[0117] Optionally, the device further includes: a sample acquisition module, an actual unwrapped image determination module, a loss determination module and a model parameter correction module.

[0118] A sample acquisition module is used to acquire a plurality of training samples; wherein the training samples include a sample interference fringe image of a sample wafer surface, unwrapping operation description information, and a real unwrapping image corresponding to the sample interference fringe image;

[0119] an actual unwrapped image determination module, configured to input, for a plurality of training samples, sample interference fringe images in the training samples and the unwrapping operation prompt information into a pre-built deep learning model, and output an actual unwrapped image;

[0120] a loss determination module, configured to perform loss processing on the actual unwrapped image and the true unwrapped image according to a target loss function to obtain a target loss value; wherein the target loss function is determined based on an error loss function and a cross entropy loss function;

[0121] A model parameter correction module is used to correct the model parameters in the deep learning model based on the target loss value, take the convergence of the target loss function in the deep learning as the training goal, and obtain an unwrapped image generation model.

[0122] Optionally, the deep learning model includes a feature extraction submodel, an unwrapped feature generation submodel, a phase unwrapped feature generation submodel and a feature conversion submodel; the unwrapped feature generation submodel includes an unwrapped feature generation module and a first feature alignment module; the phase unwrapped feature generation submodel includes a phase unwrapped feature generation module and a second feature alignment module;

[0123] The model parameter correction module is specifically used to correct the model parameters of the feature extraction sub-model, the first feature alignment module, the second feature alignment module and the feature conversion sub-model based on the target loss value.

[0124] The phase unwrapping device provided in the embodiment of the present invention can execute the phase unwrapping method provided in any embodiment of the present invention, and has the corresponding functional modules and beneficial effects of the execution method.

[0125] Example 5

[0126] Figure 6 A schematic diagram of the structure of an electronic device 10 that can be used to implement an embodiment of the present invention is shown. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital processing, cellular phones, smart phones, wearable devices (such as helmets, glasses, watches, etc.) and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the present invention described and / or claimed herein.

[0127] like Figure 6As shown, the electronic device 10 includes at least one processor 11 and a memory, such as a read-only memory (ROM) 12, a random access memory (RAM) 13, etc., which is communicatively connected to the at least one processor 11. The memory stores a computer program that can be executed by the at least one processor. The processor 11 can perform various appropriate actions and processes according to the computer program stored in the read-only memory (ROM) 12 or the computer program loaded from the storage unit 18 into the random access memory (RAM) 13. Various programs and data required for the operation of the electronic device 10 can also be stored in the RAM 13. The processor 11, ROM 12, and RAM 13 are connected to each other via a bus 14. An input / output (I / O) interface 15 is also connected to the bus 14.

[0128] Multiple components in the electronic device 10 are connected to the I / O interface 15, including an input unit 16, such as a keyboard, a mouse, etc.; an output unit 17, such as various types of displays, speakers, etc.; a storage unit 18, such as a magnetic disk, an optical disk, etc.; and a communication unit 19, such as a network card, a modem, a wireless communication transceiver, etc. The communication unit 19 allows the electronic device 10 to exchange information / data with other devices via a computer network such as the Internet and / or various telecommunication networks.

[0129] The processor 11 may be any general-purpose and / or specialized processing component with processing and computing capabilities. Examples of the processor 11 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various specialized artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, a digital signal processor (DSP), and any suitable processor, controller, microcontroller, etc. The processor 11 executes the various methods and processes described above, such as the phase unwrapping method.

[0130] In some embodiments, the phase unwrapping method may be implemented as a computer program tangibly embodied in a computer-readable storage medium, such as storage unit 18. In some embodiments, part or all of the computer program may be loaded and / or installed on electronic device 10 via ROM 12 and / or communication unit 19. When the computer program is loaded into RAM 13 and executed by processor 11, one or more steps of the phase unwrapping method described above may be performed. Alternatively, in other embodiments, processor 11 may be configured to perform the phase unwrapping method in any other suitable manner (e.g., by means of firmware).

[0131] Various embodiments of the systems and techniques described herein can be implemented in digital electronic circuit systems, integrated circuit systems, field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), system-on-chip systems (SOCs), programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments can include being implemented in one or more computer programs that are executable and / or interpreted on a programmable system that includes at least one programmable processor, which can be a special purpose or general purpose programmable processor that can receive data and instructions from a storage system, at least one input device, and at least one output device, and transmit data and instructions to the storage system, the at least one input device, and the at least one output device.

[0132] Computer programs for implementing the methods of the present invention may be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, such that when the computer program is executed by the processor, the functions / operations specified in the flowcharts and / or block diagrams are implemented. The computer program may be executed entirely on the machine, partially on the machine, as a stand-alone software package, partially on the machine and partially on a remote machine, or entirely on a remote machine or server.

[0133] In the context of the present invention, computer-readable storage media can be tangible media that can contain or store a computer program for use with an instruction execution system, device or equipment or used in combination with an instruction execution system, device or equipment. Computer-readable storage media can include but are not limited to electronic, magnetic, optical, electromagnetic, infrared or semiconductor systems, devices or equipment, or any suitable combination of the foregoing. Alternatively, computer-readable storage media can be machine-readable signal media. More specific examples of machine-readable storage media can include electrical connections based on one or more lines, portable computer disks, hard disks, random access memories (RAM), read-only memories (ROM), erasable programmable read-only memories (EPROM or flash memory), optical fibers, portable compact disk read-only memories (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination of the foregoing.

[0134] To provide interaction with a user, the systems and techniques described herein can be implemented on an electronic device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and pointing device (e.g., a mouse or trackball) through which the user can provide input to the electronic device. Other types of devices can also be used to provide interaction with the user; for example, the feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including acoustic input, voice input, or tactile input).

[0135] The systems and techniques described herein can be implemented in a computing system that includes back-end components (e.g., as a data server), or a computing system that includes middleware components (e.g., an application server), or a computing system that includes front-end components (e.g., a user computer with a graphical user interface or web browser through which a user can interact with implementations of the systems and techniques described herein), or a computing system that includes any combination of such back-end components, middleware components, or front-end components. The components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: a local area network (LAN), a wide area network (WAN), a target blockchain network, and the Internet.

[0136] A computing system may include clients and servers. The clients and servers are typically remote from each other and typically interact via a communication network. This client-server relationship arises through computer programs running on the respective computers, creating a client-server relationship. The server may be a cloud server, also known as a cloud computing server or cloud host. This server is a hosting product within the cloud computing service ecosystem that addresses the management difficulties and limited scalability of traditional physical hosting and VPS services.

[0137] It should be understood that the various forms of the processes shown above can be used to reorder, add, or delete steps. For example, the steps described in the present invention can be performed in parallel, sequentially, or in a different order, as long as the desired results of the technical solution of the present invention can be achieved. This is not limited herein.

[0138] The above specific embodiments do not limit the scope of protection of the present invention. Those skilled in the art will appreciate that various modifications, combinations, sub-combinations, and substitutions may be made based on design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present invention are intended to be included within the scope of protection of the present invention.

Claims

1. A phase unwrapping method, characterized in that: include: Obtaining an interference fringe image to be processed corresponding to the wafer surface and unwrapping operation prompt information; wherein the unwrapping operation prompt information is used to prompt the logical reasoning process of phase extraction and phase unwrapping, intermediate reasoning results, and a method for distinguishing abnormal phase jumps; Processing the interference fringe image to be processed and the unwrapping operation prompt information according to a pre-trained unwrapping image generation model, and obtaining an unwrapping image corresponding to the interference fringe image to be processed, so as to determine surface shape information corresponding to the wafer surface based on the unwrapping image; Among them, the unwrapped image generation model includes at least a feature extraction sub-model, an unwrapping feature generation sub-model, a phase unwrapping feature generation sub-model and a feature conversion sub-model; the feature extraction sub-model is used to extract the image features of the interference fringe image to be processed; the unwrapping feature generation sub-model is used to generate an unwrapping operation prompt vector based on the unwrapping operation prompt information; the phase unwrapping feature generation sub-model is used to generate a phase unwrapping image feature corresponding to the interference fringe image to be processed based on the image features and the unwrapping operation prompt vector; the feature conversion sub-model is used to convert the phase unwrapping image feature into an unwrapped image corresponding to the interference fringe image to be processed.

2. The phase unwrapping method according to claim 1, characterized in that: Also includes: Acquire an original interference fringe image corresponding to the wafer surface; The original interference fringe image is denoised according to a preset image denoising method to obtain a to-be-processed interference fringe image corresponding to the wafer surface.

3. The phase unwrapping method according to claim 1, wherein: Also includes: Acquiring unpacking process description information, wherein the unpacking process description information includes step description information of multiple unpacking operation steps, an intermediate result image associated with each of the unpacking operation steps, and a phase jump discrimination image of an abnormal phase jump; performing feature extraction on at least one intermediate result image associated with the unwrapping operation step to obtain an intermediate result feature associated with the at least one unwrapping operation step, and performing feature extraction on the phase jump discrimination image to obtain an abnormal phase jump discrimination feature corresponding to the abnormal phase jump; The unpacking operation prompt information is constructed based on the step description information of the plurality of unpacking operation steps, the intermediate result feature associated with at least one of the unpacking operation steps, and the abnormal phase jump discrimination feature.

4. The phase unwrapping method according to claim 1, wherein: The method of processing the interference fringe image to be processed and the unwrapping operation prompt information according to the pre-trained unwrapping image generation model and obtaining an unwrapping image corresponding to the interference fringe image to be processed includes: Inputting the interference fringe image to be processed into the feature extraction sub-model to perform feature extraction to obtain image features corresponding to the interference fringe image to be processed, and inputting the unwrapping operation prompt information into the unwrapping feature generation sub-model to perform information encoding to generate an unwrapping operation prompt vector; Inputting the image features and the unwrapping operation hint vector into the phase unwrapping feature generation sub-model to generate phase unwrapping image features corresponding to the interference fringe image to be processed; The phase unwrapping image features are input into the feature conversion sub-model for feature conversion to obtain an unwrapped image corresponding to the interference fringe image to be processed.

5. The phase unwrapping method according to claim 4, characterized in that: The unwrapping feature generation sub-model includes an unwrapping feature generation module and a first feature alignment module; inputting the unwrapping operation prompt information into the unwrapping feature generation sub-model for information encoding to generate an unwrapping operation prompt vector includes: Encoding the unpacking operation prompt information based on the unpacking feature generation module to obtain a prompt vector to be processed; The prompt vectors to be processed are aligned based on the first feature alignment module to obtain an unpacking operation prompt vector.

6. The phase unwrapping method according to claim 4, characterized in that: The phase unwrapping generation sub-model includes a phase unwrapping feature generation module and a second feature alignment module; Inputting the image features and the unwrapping operation hint vector into the phase unwrapping feature generation sub-model to generate phase unwrapping image features corresponding to the interference fringe image to be processed includes: Processing the image features and the unwrapping operation hint vector based on the phase unwrapping feature generation module to generate unwrapped image features to be processed; Based on the second feature alignment module, feature alignment is performed on the features of the unwrapped image to be processed to obtain phase unwrapped image features corresponding to the interference fringe image to be processed.

7. The phase unwrapping method according to claim 1, wherein: Also includes: Acquire multiple training samples; wherein the training samples include a sample interference fringe image of a sample wafer surface, unwrapping operation description information, and a real unwrapping image corresponding to the sample interference fringe image; For a plurality of training samples, the sample interference fringe images in the training samples and the unwrapping operation description information are input into a pre-built deep learning model, and the actual unwrapped images are output; Performing loss processing on the actual unwrapped image and the true unwrapped image according to a target loss function to obtain a target loss value; wherein the target loss function is determined based on an error loss function and a cross entropy loss function; The model parameters in the deep learning model are modified based on the target loss value, and the convergence of the target loss function in the deep learning is used as a training goal to obtain an unwrapped image generation model.

8. The phase unwrapping method according to claim 7, characterized in that: The deep learning model includes a feature extraction submodel, an unwrapped feature generation submodel, a phase unwrapped feature generation submodel and a feature conversion submodel; the unwrapped feature generation submodel includes an unwrapped feature generation module and a first feature alignment module; the phase unwrapped feature generation submodel includes a phase unwrapped feature generation module and a second feature alignment module; The modifying the model parameters in the deep learning model based on the target loss value includes: Model parameters of the feature extraction sub-model, the first feature alignment module, the second feature alignment module, and the feature conversion sub-model are modified based on the target loss value.

9. A phase unwrapping device, characterized in that: include: An image acquisition module is used to acquire the interference fringe image to be processed corresponding to the wafer surface and unwrapping operation prompt information; wherein the unwrapping operation prompt information is used to prompt the logical reasoning process of phase extraction and phase unwrapping, intermediate reasoning results, and the judgment method of abnormal phase jump; A phase unwrapping module is used to process the interference fringe image to be processed and the unwrapping operation prompt information according to a pre-trained unwrapping image generation model, and obtain an unwrapping image corresponding to the interference fringe image to be processed, so as to determine the surface shape information corresponding to the wafer surface based on the unwrapping image; wherein the unwrapping image generation model includes at least a feature extraction sub-model, an unwrapping feature generation sub-model, a phase unwrapping feature generation sub-model and a feature conversion sub-model; the feature extraction sub-model is used to extract the image features of the interference fringe image to be processed; the unwrapping feature generation sub-model is used to generate an unwrapping operation prompt vector according to the unwrapping operation prompt information; the phase unwrapping feature generation sub-model is used to generate a phase unwrapping image feature corresponding to the interference fringe image to be processed based on the image features and the unwrapping operation prompt vector; the feature conversion sub-model is used to convert the phase unwrapping image feature into an unwrapping image corresponding to the interference fringe image to be processed.

10. An electronic device, characterized in that: The electronic device comprises: at least one processor; and a memory communicatively connected to the at least one processor; wherein, The memory stores a computer program executable by the at least one processor, and the computer program is executed by the at least one processor to enable the at least one processor to perform the phase unwrapping method according to any one of claims 1 to 8.

11. A computer-readable storage medium, characterized in that The computer-readable storage medium stores computer instructions, and the computer instructions are used to enable a processor to implement the phase unwrapping method according to any one of claims 1 to 8 when executed.

12. A computer program product, characterized in that The computer program product comprises a computer program which, when executed by a processor, implements the phase unwrapping method according to claims 1-8.

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