Coating control method, system and equipment for photoresist

Abstract

The invention relates to a coating control method, system and equipment for photoresist, and relates to the technical field of semiconductors, and the method comprises the following steps: collecting an image of a glue dripping head, and carrying out glue dripping deviation analysis to obtain a first position deviation parameter; collecting a substrate clamping image, carrying out clamping deviation analysis to obtain a second position deviation parameter and an angle deviation parameter, and calculating to obtain a position deviation parameter in combination with the first position deviation parameter; performing spin-coating prediction according to the position deviation parameter and the angle deviation parameter to obtain edge glue amount distribution after spin-coating of the substrate; according to the edge glue amount distribution, edge glue removing parameters are generated, edge glue removing parameter distribution is obtained, compensation is carried out according to the position deviation parameters and the angle deviation parameters, compensated edge glue removing parameter distribution is obtained, edge glue removing control is carried out on the multiple edge areas of the substrate, and coating is completed. The technical problems that in the prior art, photoresist spin coating is not uniform, and the coating quality is low are solved.

Description

Technical Field

[0001] The present invention relates to the field of semiconductor technology, and in particular, to a coating control method, system, and device for photoresist. Background Art

[0002] Spin coating is a photoresist coating method widely used in semiconductor manufacturing at present. Its basic principle is to rotate the substrate at a high speed so that the photoresist spreads evenly under the action of centrifugal force and forms a thin film with a specific thickness. However, in the actual production process, the uniformity of photoresist coating is affected by various factors, resulting in the formation of photoresist accumulation at the edge of the substrate, and the accumulated glue amounts are different, resulting in uneven photoresist in different areas of the substrate, which affects subsequent development and etching production.

[0003] Therefore, there are technical problems in the prior art such as uneven spin coating of photoresist and low coating quality. Summary of the Invention

[0004] In view of the technical problems of uneven spin coating of photoresist and low coating quality in the prior art, the present invention provides a coating control method, system, and device for photoresist.

[0005] The technical solutions of the present invention to solve the above technical problems are as follows: In a first aspect, the present invention provides a coating control method for photoresist, including: collecting an image of a dispensing head, performing dispensing deviation analysis to obtain a first position deviation parameter; collecting an image of substrate clamping, performing clamping deviation analysis to obtain a second position deviation parameter and an angle deviation parameter, and combining the first position deviation parameter to calculate a position deviation parameter; performing spin coating prediction according to the position deviation parameter and the angle deviation parameter to obtain the edge glue amount distribution after spin coating of the substrate; generating edge glue removal parameters according to the edge glue amount distribution to obtain an edge glue removal parameter distribution, compensating according to the position deviation parameter and the angle deviation parameter to obtain a compensated edge glue removal parameter distribution, and performing edge glue removal control on multiple edge regions of the substrate to complete coating.

[0006] In a second aspect, the present invention provides a coating control system for photoresist, including: a dispensing deviation analysis module for collecting an image of a dispensing head, performing dispensing deviation analysis to obtain a first position deviation parameter; a clamping deviation analysis module for collecting an image of substrate clamping, performing clamping deviation analysis to obtain a second position deviation parameter and an angle deviation parameter, and combining the first position deviation parameter to calculate a position deviation parameter; An edge glue amount prediction module, configured to perform spin coating prediction based on the position deviation parameter and the angle deviation parameter, and obtain the edge glue amount distribution after spin coating of the substrate. An edge glue removal control module, configured to generate edge glue removal parameters based on the edge glue amount distribution to obtain an edge glue removal parameter distribution, perform compensation based on the position deviation parameter and the angle deviation parameter to obtain a compensated edge glue removal parameter distribution, and perform edge glue removal control on multiple edge regions of the substrate to complete coating.

[0007] In a third aspect, the present application provides an electronic device, which includes: a processor; a memory for storing executable instructions of the processor; wherein, the processor is configured to execute a coating control method for photoresist provided by the present application.

[0008] The beneficial effects of the present invention are as follows: By collecting images of the dispensing head for dispensing deviation analysis and collecting images of the substrate clamping for clamping deviation analysis, the present invention obtains the first position deviation parameter, the second position deviation parameter, and the angle deviation parameter. Compared with the traditional method that relies on the mechanical accuracy of the device and regular maintenance, this method can monitor and quantify the deviation of the dispensing head and the substrate clamping in real time, improving the consistency and controllability of photoresist coating. Through image acquisition and analysis of the dispensing head, the present invention can accurately identify the offset of the dispensing position, avoid the offset of the spin coating center caused by the clogging or position drift of the dispensing head, reduce the problems of uneven photoresist thickness and edge accumulation, and thus improve the uniformity of spin coating and the stability of subsequent processing. By detecting the offset amount and angle deviation of the substrate clamping, the present invention compensates for the uneven spin coating caused by improper clamping, optimizing the overall distribution of the photoresist. Based on the spin coating prediction model of the position deviation parameter and the angle deviation parameter, the present invention can predict in advance the edge glue amount distribution after spin coating of the substrate. Compared with the traditional method of empirical setting, this method can more accurately adjust the edge glue removal strategy in a data-driven manner, reducing etching defects caused by excessive or insufficient edge photoresist accumulation. At the same time, according to the predicted edge glue amount distribution, edge glue removal parameters are adaptively generated, and compensation calculations are performed in combination with the position and angle deviation parameters to achieve differential glue removal control of multiple edge regions, thereby ensuring the edge glue removal effect while avoiding insufficient photoresist caused by excessive glue removal and improving the integrity of edge patterns and etching accuracy. In summary, through multi-source image data analysis, intelligent prediction, and adaptive compensation, this method can effectively improve the quality of photoresist coating, reduce edge defects, and improve the stability and accuracy of photoresist coating and semiconductor manufacturing processes. Description of the Drawings

[0009] Figure 1 It is a schematic flow chart of a coating control method for photoresist provided by the present invention.

[0010] Figure 2Schematic structural diagram of a coating control system for photoresist provided by the present invention.

[0011] Figure 3 Schematic structural diagram of an electronic device provided by the present invention.

[0012] Reference numerals: dispensing deviation analysis module 11, clamping deviation analysis module 12, edge glue amount prediction module 13, edge glue removal control module 14, electronic device 200, memory 210, processor 220, first computer program 211. Specific embodiments

[0013] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative efforts fall within the protection scope of the present invention.

[0014] In the description of the present invention, the terms "first" and "second" are only used for descriptive purposes, and cannot be construed as indicating or implying relative importance or implicitly specifying the quantity of the indicated technical features. Thus, the features defined with "first" and "second" may explicitly or implicitly include one or more of the described features. In the description of the present invention, "a plurality" means two or more, unless otherwise specifically defined.

[0015] In the description of the present invention, the term "for example" is used to mean "serving as an example, illustration, or explanation". Any embodiment described as "for example" in the present invention is not necessarily construed as being more preferred or advantageous than other embodiments. In order for any person skilled in the art to implement and use the present invention, the following description is given. In the following description, details are set forth for the purpose of explanation. It should be understood that those of ordinary skill in the art can recognize that the present invention can be implemented without using these specific details. In other instances, well-known structures and processes are not elaborated in detail to avoid obscuring the description of the present invention with unnecessary details. Therefore, the present invention is not intended to be limited to the embodiments shown, but is consistent with the broadest scope that conforms to the principles and features disclosed in the present invention.

[0016] Embodiment 1, as Figure 1 shown, the embodiment of the present invention provides a coating control method for photoresist, and the method specifically includes the following steps: S10: Collect an image of the dispensing head, perform dispensing deviation analysis, and obtain a first position deviation parameter.

[0017] In the embodiments of the present application, during the process of spin-coating photoresist, the photoresist is first evenly dropped onto the center of the substrate by a dispensing head, and then spreads to the entire surface of the substrate through rotation. If the dispensing head is partially blocked or deposits dry photoresist residues, it will cause limited flow of the photoresist, resulting in a decrease in the amount of dispensed photoresist or a deviation in the dropping position. After the dispensing deviates from the center, the spreading of the photoresist during the spin-coating process is uneven, resulting in significant differences in the thickness of the photoresist in different regions, which in turn leads to differences in the accumulation of the photoresist in different edge regions of the substrate. For example, the amount of photoresist accumulation is large in some edges and small in some edges.

[0018] Therefore, first collect an image of the dispensing head to perform dispensing deviation analysis, obtain the first position deviation parameter, so as to obtain the dispensing deviation of the photoresist caused by partial blockage of the dispensing head, and further use it as the data basis for subsequent analysis of the edge photoresist accumulation.

[0019] Step S10 in the method provided by the embodiments of the present application includes: Collect an image of the dispensing head to obtain a dispensing image; According to the historical data of photoresist spin-coating, collect a set of sample dispensing images, label the dispensing position deviation after dispensing in each sample dispensing image, and obtain a set of sample first position deviation parameters. Among them, the position deviation parameter includes an X-axis deviation parameter and a Y-axis deviation parameter; Based on a convolutional neural network, construct a dispensing deviation recognizer; Use the set of sample dispensing images and the set of sample first position deviation parameters to perform supervised training and testing on the dispensing deviation recognizer until the accuracy meets the accuracy threshold to complete the construction; Input the dispensing image into the dispensing deviation recognizer to identify and obtain the first position deviation parameter.

[0020] In the embodiments of the present application, in order to accurately identify the deviation of the photoresist dropping position, first collect an image of the dispensing head. Specifically, the image of the dispensing head can be collected in the front and left directions of the dispensing head. For example, a high-resolution industrial camera is used for collection to obtain a dispensing image.

[0021] Furthermore, machine learning technology is used to identify the dispensing image, and then identify the possible deviation situation after the dispensing head performs dispensing. Among them, first collect sample data to train the dispensing deviation recognizer for dispensing deviation recognition analysis.

[0022] According to the historical data of photoresist spin coating, images of the dispensing head are collected during the previous dispensing process to obtain a set of sample dispensing images. Further, the deviation between the position of the dispensing head after dispensing in each sample dispensing image and the standard dispensing position is marked as the sample first position deviation parameter, and a set of sample first position deviation parameters is obtained. Each first position deviation parameter includes an X-axis deviation parameter and a Y-axis deviation parameter.

[0023] Exemplarily, a coordinate system can be established at the standard dispensing position. For example, taking the standard dispensing position as the origin, an X-axis and a Y-axis coordinate system are constructed, where the X-axis and the Y-axis are perpendicular. Then, the position coordinates of the dispensing head after dispensing in each sample dispensing image are obtained as the position deviation parameters. For example, the standard dispensing position is the origin with coordinate values (0, 0), and the actual dispensing position is (0.12 mm, 0.15 mm), which is used as the first position deviation parameter.

[0024] Further, a dispensing deviation recognizer is constructed using a convolutional neural network (CNN, Convolutional Neural Network), which includes an input layer, multiple convolutional layers, multiple pooling layers, a fully connected layer, and an output layer. The convolutional layer uses a 3*3 convolutional kernel for image feature extraction, and the fully connected layer uses the mean squared error loss function to calculate the loss for training optimization.

[0025] The set of sample dispensing images and the set of sample first position deviation parameters are divided, for example, in a ratio of 8:2, to obtain training data and test data. The training data is used to supervise the training of the dispensing deviation recognizer. The sample dispensing images are input, and the output first position deviation parameters are obtained. The error from the sample first position deviation parameters is calculated, and the loss is calculated through the mean squared error loss function. For example, the sum of the squared errors of the X-axis deviation parameter and the Y-axis deviation parameter is calculated as the loss. Then, the network parameters such as the weights of the fully connected layer are optimized according to the loss to reduce the loss until the accuracy meets the accuracy threshold. For example, if the accuracy is greater than or equal to 95%, the test is qualified and the construction training is completed.

[0026] The currently collected dispensing image is input into the trained dispensing deviation recognizer, and the corresponding first position deviation parameters are recognized and output. For example, the dispensing position deviation parameters are (0.12 mm, 0.15 mm).

[0027] Through the above steps, the embodiments of the present application can monitor in real time the deviation of the dispensing position caused by reasons such as dispensing head blockage during the dispensing process, use a convolutional neural network for recognition, achieve automated and high-precision dispensing deviation recognition, thereby avoiding uneven spin coating caused by problems such as dispensing head blockage, improving the consistency of photoresist coating, and ensuring the accuracy and stability of subsequent processing.

[0028] S20: Collect an image of the substrate clamping, perform clamping deviation analysis, obtain the second position deviation parameter and the angle deviation parameter, and calculate the position deviation parameter by combining the first position deviation parameter.

[0029] In the embodiment of the present application, in the photoresist coating process, before the substrate enters the spin coating device, it needs to be fixed by a vacuum chuck or a mechanical fixture. The clamping deviation of the substrate will directly affect the uniformity of the photoresist after spin coating, and further affect the subsequent exposure and etching accuracy. Although the accuracy of substrate clamping is currently ensured by regular maintenance and adjustment, due to inevitable errors, equipment wear and other problems, clamping offset may still occur, resulting in deviation of the dispensing position.

[0030] Therefore, the embodiment of the present application collects an image of the substrate clamping and performs deviation analysis caused by clamping deviation. Among them, clamping may also cause an angle between the substrate plane and the horizontal plane, resulting in deviation of the substrate angle. Furthermore, when spin coating, different regions of the substrate have different angles, resulting in less photoresist accumulation in the region with a downward angle and more photoresist accumulation in the region with an upward angle, affecting the coating uniformity.

[0031] Perform clamping deviation analysis through the image of the substrate clamping to obtain the second position deviation parameter and the angle deviation parameter caused by clamping.

[0032] Step S20 in the method provided by the embodiment of the present application includes: Collect an image of the substrate clamping to obtain a substrate clamping image; According to the substrate clamping historical data, collect a set of sample substrate clamping images, label the substrate position deviation and angle deviation in each sample substrate clamping image, and obtain a set of sample second position deviation parameters and a set of sample angle deviation parameters. Among them, the angle deviation parameter includes the angle formed by the deviation of the substrate from the horizontal plane and the deviation direction; Based on the convolutional neural network, construct a clamping deviation recognizer; Use the set of sample substrate clamping images, the set of sample second position deviation parameters, and the set of sample angle deviation parameters to perform supervised training and testing on the clamping deviation recognizer until the accuracy rate meets the accuracy threshold to complete the construction; Input the substrate clamping image into the clamping deviation recognizer to recognize and output the second position deviation parameter and the angle deviation parameter.

[0033] In the embodiments of the present application, during the photoresist coating process, the clamping deviation of the substrate may cause uneven thickness of the spin-coated photoresist. Therefore, it is necessary to detect and compensate for the clamping error through substrate clamping image analysis. For this purpose, first, an image of the substrate clamping is collected, and a clamping deviation recognizer is trained through a convolutional neural network (CNN) to identify the second position deviation parameter (i.e., the translational offset of the substrate in the X and Y directions) and the angle deviation parameter (i.e., the tilt angle and deviation direction of the substrate relative to the horizontal plane).

[0034] In the embodiments of the present application, an image of the substrate clamping is collected. For example, an industrial camera is used to collect images directly above, directly in front of, and to the left of the substrate clamping to obtain the substrate clamping image, and then the parameters of the clamping deviation of the substrate in different directions are analyzed.

[0035] Further, according to the data of the substrate clamping within the historical time, the images taken during the previous substrate clamping are collected as sample substrate clamping images to obtain a set of sample substrate clamping images. And the position deviation and angle deviation of the substrate in each sample substrate clamping image are marked. For example, a coordinate system is constructed with the standard position as the origin, the coordinate system includes the X-axis and the Y-axis, and the coordinate values of the center of the substrate after actual clamping within the coordinate system are obtained, such as (-0.08 mm, -0.10 mm), as the sample second position deviation parameter, and then the sample second position deviation parameter set is obtained through marking. And the angle formed by the deviation of the plane where the substrate is located from the horizontal plane and the deviation direction in each sample substrate clamping image are marked. The formed angle is, for example, 5°, and the deviation direction is, for example, the direction in which the plane where the substrate is located forms an angle with the horizontal plane due to the warping of the substrate, such as the positive direction of the X-axis, or 45° deviating from the negative direction of the Y-axis in the positive direction of the X-axis. In this way, the sample angle and the sample deviation direction are marked as the sample angle deviation parameter, and the sample angle deviation parameter set is obtained.

[0036] Further, based on the convolutional neural network, a clamping deviation recognizer is constructed, which includes an input layer, multiple convolutional layers, multiple pooling layers, a fully connected layer, and an output layer. The convolutional layer uses a 3*3 convolutional kernel for image feature extraction, and the fully connected layer uses the mean square error loss function to calculate the loss for training optimization.

[0037] Further, the clamped deviation identifier is supervised and trained and tested by using the sample substrate clamping image set, the sample second position deviation parameter set, and the sample angle deviation parameter set. Specifically, each type of data in the sample substrate clamping image set, the sample second position deviation parameter set, and the sample angle deviation parameter set is divided, for example, divided into training data and test data according to a ratio of 8:2. Then, supervised training is performed using the training data. The sample substrate clamping image is input, and the output second position deviation parameter and angle deviation parameter are obtained. Then, the difference amplitude from the true sample second position deviation parameter and sample angle deviation parameter is calculated, and the loss is calculated based on the loss function. Then, according to the loss, the network parameters such as weights of the fully connected layer are optimized using the adam optimizer to reduce the loss and improve the accuracy. After iterative training and testing, if the accuracy meets the accuracy threshold, for example, is greater than or equal to 96%, the training is completed, and the clamped deviation identifier is constructed and obtained.

[0038] Based on the trained clamped deviation identifier, the currently acquired substrate clamping image is input, and through image convolution feature extraction and recognition, the output second position deviation parameter and angle deviation parameter are obtained as the analysis results of the position deviation and angle deviation caused by substrate clamping.

[0039] In the embodiments of the present application, by using deep learning, automatic and high-accuracy substrate position offset and angle offset can be achieved. Furthermore, combined with the dispensing position offset analyzed by the dispensing head, the possible position offset of the photoresist in actual spin coating is analyzed, thereby providing a data basis for photoresist spin coating and edge degumming control and improving the quality of photoresist coating.

[0040] In the embodiments of the present application, after the first position deviation parameter caused by dispensing head blockage and the second position deviation parameter caused by clamping deviation are identified, the two types of position deviation parameters are fused to obtain the final dispensing position deviation impact under the influence of multi-dimensional deviations.

[0041] Step S20 in the method provided in the embodiments of the present application further includes: Extracting the first X-axis deviation parameter, the first Y-axis deviation parameter, the second X-axis deviation parameter, and the second Y-axis deviation parameter within the first position deviation parameter and the second position deviation parameter; According to the first X-axis deviation parameter, the first Y-axis deviation parameter, the second X-axis deviation parameter, and the second Y-axis deviation parameter, the combined X-axis deviation parameter and the combined Y-axis deviation parameter are calculated, and the position deviation parameter is integrated and obtained.

[0042] In the embodiments of the present application, during the photoresist spin coating process, the offset between the dispensing position and the substrate clamping position will affect the final uniformity of the photoresist. Therefore, it is necessary to comprehensively analyze the dispensing offset and the substrate clamping offset and calculate the final position deviation parameter.

[0043] In the embodiments of the present application, the X-axis offset parameter and the Y-axis offset parameter within the first position deviation parameter and the second position deviation parameter are extracted to obtain a first X-axis deviation parameter, a first Y-axis deviation parameter, a second X-axis deviation parameter, and a second Y-axis deviation parameter.

[0044] Exemplarily, the first X-axis deviation parameter is 0.12 mm, and the first Y-axis deviation parameter is 0.15 mm, that is, the actual dispensing position deviates 0.12 mm and 0.15 mm in the positive X-axis direction and the positive Y-axis direction from the standard position. The second X-axis deviation parameter is -0.08 mm, and the second Y-axis deviation parameter is -0.10 mm. That is, the actual dispensing position deviates 0.08 mm and 0.10 mm in the negative X-axis direction and the negative Y-axis direction from the standard position.

[0045] Further, according to the first X-axis deviation parameter, the first Y-axis deviation parameter, the second X-axis deviation parameter, and the second Y-axis deviation parameter, a combined X-axis deviation parameter and a combined Y-axis deviation parameter are calculated. For example, the sum of the first X-axis deviation parameter and the second X-axis deviation parameter is calculated as the combined X-axis deviation parameter, and the sum of the first Y-axis deviation parameter and the second Y-axis deviation parameter is calculated as the combined Y-axis deviation parameter. For example, the combined X-axis deviation parameter is 0.12 mm + (-0.08 mm) = 0.04 mm, and the combined Y-axis deviation parameter is 0.15 mm + (-0.10 mm) = 0.05 mm.

[0046] In the embodiments of the present application, by fusing two types of deviation parameters, the comprehensive offset amount of the dispensing position relative to the standard position during the photoresist spin coating process can be accurately calculated, ensuring more accurate subsequent edge degluing and spin coating compensation.

[0047] S30: According to the position deviation parameter and the angle deviation parameter, spin coating prediction is performed to obtain the edge glue amount distribution after the substrate is spin coated.

[0048] In the embodiments of the present application, the position offset of the dispensing on the substrate (i.e., the overall offset in the X-axis and Y-axis directions) and the angle offset of the substrate (i.e., the tilt angle and direction of the substrate) will directly affect the distribution of the photoresist after spin coating. For example, due to the centrifugal force during the spin coating process, the amount of photoresist accumulation at the substrate edge in the direction where the dispensing deviates is larger, and the amount of photoresist accumulation at the substrate edge of the upwardly tilted part of the substrate is larger during spin coating. Therefore, in order to perform edge degluing treatment for different photoresist accumulation amounts in different edge regions and ensure the uniformity of photoresist coating, spin coating prediction is performed according to the analyzed position deviation parameter and angle deviation parameter to predict the edge glue amounts of different edge regions after the substrate is spin coated, forming an edge glue amount distribution as a reference for edge degluing treatment.

[0049] Step S30 in the method provided by the embodiment of the present application includes: Collect a set of sample position deviation parameters and a set of sample angle deviation parameters according to the spin coating record data within the historical time, and collect the edge glue amounts of multiple edge regions of the substrate after spin coating under different sample position deviation parameters and sample angle deviation parameters, and label to obtain a set of sample edge glue amount distributions; Use a multi-layer feedforward neural network to construct a spin coating predictor; Use the set of sample position deviation parameters, the set of sample angle deviation parameters, and the set of sample edge glue amount distributions to perform supervised training and testing on the spin coating predictor until the accuracy rate meets the accuracy threshold to complete the construction; Input the position deviation parameter and the angle deviation parameter into the spin coating predictor to predict and output the edge glue amount distribution.

[0050] In the embodiment of the present application, machine learning is used to perform spin coating prediction according to the position deviation parameter and the angle deviation parameter to predict the edge photoresist accumulation amounts of multiple edge regions of the substrate after spin coating, and obtain the edge glue amount distribution.

[0051] Among them, first, according to the data log of the substrate spin coating within the historical time, collect the set of sample position deviation parameters and the set of sample angle deviation parameters monitored and recorded previously, and form a set of sample position deviation parameters and a set of sample angle deviation parameters, which can be obtained through the steps in the above content.

[0052] Furthermore, collect the edge glue amounts of multiple edge regions of the substrate after photoresist spin coating under different sample position deviation parameters and sample angle deviation parameters. When the spin coating speed is the same, according to the positions of multiple edge regions, label as the sample edge glue amount distribution to obtain a set of sample edge glue amount distributions.

[0053] Exemplarily, the edge regions of the substrate can be divided to obtain multiple edge regions. For example, according to the four directions of the upper, lower, left, and right of the substrate, divide into four edge regions. For example, if the photoresist thicknesses at the edge positions of the four edge regions obtained by testing are 1.85 μm, 2.10 μm, 2.30 μm, and 1.70 μm respectively, then construct a sample edge glue amount distribution of (1.85 μm, 2.10 μm, 2.30 μm, 1.70 μm). Optionally, more edge regions can also be divided, such as edge regions in 8 directions, and test the photoresist thicknesses at the edges of the substrate in eight directions as the edge glue amounts to form the edge glue amount distribution.

[0054] Furthermore, a multi-layer feedforward neural network is adopted to construct a spin coating predictor, which includes an input layer, a hidden layer, and an output layer. The dimension of the input layer is 4, used to input data of X-axis offset, Y-axis offset, tilt angle, and deviation direction. The dimension of the output layer is, for example, 4, used to output the photoresist accumulation thickness of four edge regions. The hidden layer includes two layers, which include 128 and 64 nodes respectively, and the ReLU activation function is adopted. The spin coating predictor uses the mean squared error loss function to calculate the loss.

[0055] Furthermore, a set of sample position deviation parameters, a set of sample angle deviation parameters, and a set of sample edge glue amount distributions are used to supervise the training and testing of the spin coating predictor. First, the set of sample position deviation parameters, the set of sample angle deviation parameters, and the set of sample edge glue amount distributions are divided. For example, they are divided into training data and testing data according to a ratio of 8:2. The training data is used to supervise the training of the spin coating predictor, and the testing data is used for testing. Among them, during the training process, the sample position deviation parameters and sample angle deviation parameters are input to obtain the output edge glue amount distribution, and the error from the true sample edge glue amount distribution is calculated. The mean squared error loss function is used to calculate the loss. For example, the sum of the squares of the edge glue amount errors of the four edge regions is calculated as the loss. The network parameters, such as the weights and biases of the nodes in the hidden layer, are adjusted and optimized according to the loss to reduce the loss and improve the accuracy until the testing accuracy meets the accuracy threshold, for example, greater than or equal to 95%, then the construction training of the spin coating predictor is completed.

[0056] The currently identified position deviation parameters and angle deviation parameters are input into the trained spin coating predictor to predict and output the edge glue amounts of multiple edge regions, such as the photoresist thickness of the four edge regions, as the edge glue amount distribution.

[0057] Based on machine learning, this application embodiment performs spin coating prediction according to the position deviation parameters and angle deviation parameters, obtains the edge glue amount distribution affected by the drop glue deviation and substrate angle deviation, and uses it as reference data for edge degluing process control, thereby improving the uniformity and coating quality of the edge degluing process of the photoresist coating.

[0058] S40: According to the edge glue amount distribution, generate edge degluing parameters to obtain an edge degluing parameter distribution, compensate according to the position deviation parameters and angle deviation parameters to obtain a compensated edge degluing parameter distribution, and perform edge degluing control on multiple edge regions of the substrate to complete the coating.

[0059] In the embodiment of the present application, in the photoresist spin coating process, due to the above-mentioned dispensing deviation and substrate alignment deviation, the photoresist thickness is often uneven in different edge regions of the substrate, which affects the stability of subsequent processes. Therefore, based on the edge glue amount distribution, appropriate edge glue removal parameters are calculated, and compensated by combining the position deviation parameter and the angle deviation parameter to generate a compensated edge glue removal parameter distribution, and finally accurate edge glue removal control is realized to ensure the uniformity of photoresist coating.

[0060] Step S40 in the method provided by the embodiment of the present application includes: According to the edge glue removal historical data of spin coating, a sample edge glue amount set is collected, corresponding sample edge glue removal parameters are configured, and a sample edge glue removal parameter set is obtained by annotation; Construct a mapping relationship between the sample edge glue amount set and the sample edge glue removal parameter set to obtain an edge glue removal decision table; Input multiple edge glue amounts in the edge glue amount distribution into the edge glue removal decision table, and map to obtain edge glue removal parameters for multiple edge regions, and generate an edge glue removal parameter distribution.

[0061] In the embodiment of the present application, from the edge glue removal historical data of spin coating in the historical spin coating process record, the photoresist thickness at the edge of the substrate during the previous photoresist coating is collected as the sample edge glue amount, and a sample edge glue amount set is obtained. Then, according to different sample edge glue amounts, corresponding sample edge glue removal parameters are configured, such as the thickness of the removed photoresist or the solvent spraying amount. The solvent can melt the photoresist to achieve the purpose of glue removal. The larger the solvent spraying amount, the greater the thickness of the removed photoresist.

[0062] Exemplarily, the sample edge glue amount is 2.30 μm, and the configured sample edge glue removal parameter is -0.30 μm, that is, the glue removal amount required to achieve the standard photoresist thickness (such as 2.0 μm), and the corresponding solvent spraying amount is, for example, 3.5 μL. In this way, a sample edge glue removal parameter set is obtained by configuration and annotation. The configuration of the sample edge glue removal parameter can be configured by those skilled in the field of photoresist coating to ensure that the edge glue amount meets the coating requirements after edge glue removal. In this way, a sample edge glue removal parameter set is configured and annotated.

[0063] Furthermore, construct a mapping relationship between the sample edge glue amount set and the sample edge glue removal parameter set to obtain an edge glue removal decision table. In the edge glue removal decision table, each sample edge glue amount corresponds to a sample edge glue removal parameter.

[0064] Part of the data in the edge glue removal decision table is shown in Table 1.

[0065] Edge glue amount (μm) Glue removal parameter (μm) 2.50 -0.50 2.30 -0.30 2.10 -0.10 1.90 0 Table 1 Further, the edge glue amounts of multiple edge regions within the currently predicted edge glue amount distribution are input into an edge glue removal decision table for mapping to obtain corresponding multiple edge glue removal parameters. Combining the multiple edge regions, an edge glue removal parameter distribution is generated. For example, the edge glue removal parameter distribution corresponding to the edge glue amount distribution of (1.85 μm, 2.10 μm, 2.30 μm, 1.70 μm) is (0μm, -0.10μm, -0.30μm, 0μm). After inputting the edge glue amount into the edge glue removal decision table, if there is the same sample edge glue amount, the corresponding sample edge glue removal parameter is mapped; if there is no same sample edge glue amount, the sample edge glue removal parameter corresponding to the closest sample edge glue amount is mapped to obtain the edge glue removal parameter distribution.

[0066] Through the above steps, the embodiments of the present application map to obtain the edge glue removal parameter distribution, which is used as the basic data for subsequent edge glue removal control to improve the uniformity of photoresist coating and the accuracy and stability of subsequent lithography processes.

[0067] In the embodiments of the present application, during the edge glue removal process, if the glue removal amount in the edge glue removal parameters is too large, it may cause excessive removal of the photoresist, resulting in too small a photoresist thickness and affecting subsequent lithography processing. In the foregoing content of the embodiments of the present application, machine learning and deep learning are used to identify glue dropping deviation and predict the spin coating of the photoresist, and there may be some errors, resulting in the larger the edge glue removal parameter. In order to avoid excessive edge glue removal, it is necessary to compensate and adjust the edge glue removal parameter distribution to avoid excessive glue removal amount.

[0068] Step S40 in the method provided by the embodiments of the present application further includes: Retrieving in the spin coating database according to the position deviation parameter and the angle deviation parameter to obtain the position deviation occurrence rate and the angle deviation occurrence rate, and calculating to obtain the deviation occurrence rate; Obtaining the average deviation occurrence rate of different position deviation parameters and angle deviation parameters in the spin coating database; Judging whether the deviation occurrence rate is greater than or equal to the average deviation occurrence rate; If so, no compensation is made to the edge glue removal parameter distribution; if not, calculate the difference amplitude between the deviation occurrence rate and the average deviation occurrence rate as the compensation adjustment parameter; Obtaining a preset compensation coefficient; Using the compensation adjustment parameter to adjust and calculate the preset compensation coefficient to obtain a compensation coefficient; Using the compensation coefficient to perform compensation adjustment calculation on the edge glue removal parameter distribution to obtain a compensated edge glue removal parameter distribution; Perform edge degumming control on multiple edge regions of the substrate according to multiple compensation edge degumming parameters within the described compensation edge degumming parameter distribution, and complete coating.

[0069] In an embodiment of the present application, according to the historical data of photoresist spin coating within a historical time, the method described above is used to process and obtain a set of sample position deviation parameters and a set of sample angle deviation parameters, or the set of sample position deviation parameters and the set of sample angle deviation parameters are actually measured to construct a spin coating database, which stores the sample position deviation parameters and sample angle deviation parameters generated during previous photoresist coating.

[0070] Further, according to the current position deviation parameters and angle deviation parameters, retrieve in the spin coating database to retrieve the occurrence rates of the same position deviation parameters and angle deviation parameters. For example, the respective occurrence rates are 2% and 3%, which are used as the position deviation occurrence rate and the angle deviation occurrence rate. The larger the position deviation occurrence rate and the angle deviation occurrence rate, the greater the probability that the current position deviation parameters and angle deviation parameters appear in photoresist spin coating, the more representative the current position deviation parameters and angle deviation parameters are, and the higher the accuracy of the analysis and processing.

[0071] Further, according to the position deviation occurrence rate and the angle deviation occurrence rate, calculate the deviation occurrence rate. For example, calculate the mean of the two as the deviation occurrence rate, such as 2.5%.

[0072] Further, obtain the average deviation occurrence rate of different position deviation parameters and angle deviation parameters in the spin coating database. For example, retrieve the deviation occurrence rates of different sets of position deviation parameters and angle deviation parameters in the spin coating database, and then calculate the mean as the average deviation occurrence rate, such as 1%.

[0073] Further, determine whether the deviation occurrence rate is greater than or equal to the average deviation occurrence rate. If so, it means that the deviation occurrence rate of the current position deviation parameters and angle deviation parameters is relatively large, belonging to common glue dripping deviation and substrate angle deviation situations, with a relatively large amount of corresponding sample data, and the accuracy of spin coating prediction is relatively high. There is no need to compensate for the edge degumming parameter distribution, and edge degumming treatment can be directly performed.

[0074] If not, it means that the deviation occurrence rate of the current position deviation parameters and angle deviation parameters is relatively small, belonging to rare glue dripping deviation and substrate angle deviation situations, with a relatively small amount of corresponding sample data, and the accuracy of spin coating prediction is relatively low. It is necessary to compensate for the edge degumming parameter distribution to avoid excessive degumming amount during edge degumming treatment, resulting in too little photoresist remaining after edge degumming and affecting photolithography processing.

[0075] Exemplarily, calculate the difference amplitude between the deviation occurrence rate and the average deviation occurrence rate. For example, calculate the absolute difference between the deviation occurrence rate and the average deviation occurrence rate, and calculate the ratio of the absolute difference to the average deviation occurrence rate as the difference amplitude. For example, if the deviation occurrence rate is 0.8% and the average deviation occurrence rate is 1.0%, then the difference amplitude is 0.2% / 1.0% = 0.2, which is used as the compensation adjustment parameter.

[0076] Furthermore, obtain a preset compensation coefficient, specifically a coefficient preset for compensating and adjusting to reduce the edge desizing parameters. For example, it is 20%, that is, compensate and reduce the desizing amount within the edge desizing parameters by 20%, or reduce the desizing solvent spraying amount by 20%. For example, if the edge desizing parameter is -0.030μm, then the edge desizing parameter after compensating and adjusting according to the preset compensation coefficient is -0.030μm * (1 - 20%) = -0.024μm.

[0077] Use this compensation adjustment parameter to perform adjustment calculations on this preset compensation coefficient to obtain the compensation coefficient. For example, multiply the sum of the compensation adjustment parameter and 1 by this preset compensation coefficient to complete the adjustment calculation and obtain the compensation coefficient. In this way, the fewer the position deviation parameters and angle deviation parameters, the larger the corresponding compensation adjustment parameter, the larger the adjusted compensation coefficient, and the greater the amplitude of reducing the desizing amount, so as to avoid excessive desizing resulting in too small a photoresist thickness. For example, if the compensation adjustment parameter is 0.2, perform adjustment calculations on the preset compensation coefficient, and the adjusted compensation coefficient is (1 + 0.2) * 20% = 24%.

[0078] Furthermore, use the adjusted compensation coefficient to perform compensation adjustment calculations on multiple edge desizing parameters within the edge desizing parameter distribution. For example, use 1 - 24% = 76% to multiply each edge desizing parameter to complete the compensation adjustment and obtain the compensated edge desizing parameter distribution. For example, if the edge desizing parameter is -0.030μm, then the compensated edge desizing parameter after compensation is -0.030μm * 76% = -0.0228μm.

[0079] Furthermore, according to the multiple compensated edge desizing parameters within the compensated edge desizing parameter distribution after compensation adjustment, perform edge desizing control on multiple edge regions of the substrate respectively. For example, remove photoresist with different thicknesses to complete coating. Exemplarily, after spin coating is completed, use multiple compensated edge desizing parameters to perform edge desizing treatment on different edge regions of the substrate, complete photoresist spin coating and edge desizing treatment, and complete photoresist coating.

[0080] A coating control method for photoresist provided by an embodiment of the present invention has at least the following technical effects: By collecting images of the dispensing head for dispensing deviation analysis and collecting images of substrate clamping for clamping deviation analysis, the first position deviation parameter, the second position deviation parameter, and the angle deviation parameter are obtained. Compared with the traditional method that relies on the mechanical accuracy of the equipment and regular maintenance, this method can monitor and quantify the deviations of the dispensing head and substrate clamping in real time, improving the consistency and controllability of photoresist coating. Through image acquisition and analysis of the dispensing head, the offset of the dispensing position can be accurately identified, avoiding the offset of the spin coating center caused by clogging or position drift of the dispensing head, reducing the problems of uneven photoresist thickness and edge accumulation, and thus improving the uniformity of spin coating and the stability of subsequent processing. By detecting the offset amount and angle deviation of substrate clamping, the uneven spin coating caused by improper clamping is compensated, and the overall distribution of photoresist is optimized. Based on the spin coating prediction model of the position deviation parameter and the angle deviation parameter, the edge glue amount distribution after substrate spin coating can be predicted in advance. Compared with the traditional method of empirical setting, this method can adjust the edge desizing strategy more accurately in a data-driven manner, reducing etching defects caused by excessive or insufficient edge photoresist accumulation. At the same time, according to the predicted edge glue amount distribution, edge desizing parameters are adaptively generated, and compensation calculations are performed in combination with the position and angle deviation parameters to achieve differential desizing control of multiple edge regions, thereby ensuring the edge desizing effect while avoiding insufficient photoresist caused by excessive desizing and improving the integrity and etching accuracy of edge patterns. In summary, through multi-source image data analysis, intelligent prediction, and adaptive compensation, this method can effectively improve the quality of photoresist coating, reduce edge defects, and improve the stability and accuracy of photoresist coating and semiconductor manufacturing processes.

[0081] Embodiment 2, as Figure 2 shown, with the same inventive concept as a coating control method for photoresist in Embodiment 1, an embodiment of the present invention further provides a coating control system for photoresist. The explanation of a coating control method for photoresist in Embodiment 1 also applies to a coating control system for photoresist. The system includes: A dispensing deviation analysis module 11, configured to collect images of the dispensing head, perform dispensing deviation analysis, and obtain a first position deviation parameter; A clamping deviation analysis module 12, configured to collect images of substrate clamping, perform clamping deviation analysis, obtain a second position deviation parameter and an angle deviation parameter, and calculate a position deviation parameter in combination with the first position deviation parameter; An edge glue amount prediction module 13, configured to perform spin coating prediction according to the position deviation parameter and the angle deviation parameter, and obtain the edge glue amount distribution after substrate spin coating; The edge glue removal control module 14 is used to generate edge glue removal parameters according to the edge glue amount distribution, obtain the edge glue removal parameter distribution, compensate according to the position deviation parameter and the angle deviation parameter, obtain the compensated edge glue removal parameter distribution, and control the edge glue removal of multiple edge regions of the substrate to complete coating.

[0082] Further, a coating control system for photoresist is also used for: Collect the image of the dispensing head to obtain the dispensing image; According to the photoresist spin coating historical data, collect a set of sample dispensing images, label the dispensing position deviation in each sample dispensing image after dispensing, and obtain a set of sample first position deviation parameters, where the position deviation parameter includes the X-axis deviation parameter and the Y-axis deviation parameter; Based on the convolutional neural network, construct a dispensing deviation recognizer; Use the set of sample dispensing images and the set of sample first position deviation parameters to perform supervised training and testing on the dispensing deviation recognizer until the accuracy rate meets the accuracy threshold to complete the construction; Input the dispensing image into the dispensing deviation recognizer to identify and obtain the first position deviation parameter.

[0083] Further, a coating control system for photoresist is also used for: Collect the image of the substrate clamping to obtain the substrate clamping image; According to the substrate clamping historical data, collect a set of sample substrate clamping images, label the substrate position deviation and the angle deviation in each sample substrate clamping image, and obtain a set of sample second position deviation parameters and a set of sample angle deviation parameters, where the angle deviation parameter includes the included angle formed by the deviation of the substrate from the horizontal plane and the deviation direction; Based on the convolutional neural network, construct a clamping deviation recognizer; Use the set of sample substrate clamping images, the set of sample second position deviation parameters and the set of sample angle deviation parameters to perform supervised training and testing on the clamping deviation recognizer until the accuracy rate meets the accuracy threshold to complete the construction; Input the substrate clamping image into the clamping deviation recognizer to identify and output the second position deviation parameter and the angle deviation parameter.

[0084] Further, a coating control system for photoresist is also used for: Extract the first X-axis deviation parameter, the first Y-axis deviation parameter, the second X-axis deviation parameter and the second Y-axis deviation parameter from the first position deviation parameter and the second position deviation parameter; Based on the first X-axis deviation parameter, the first Y-axis deviation parameter, the second X-axis deviation parameter, and the second Y-axis deviation parameter, a combined X-axis deviation parameter and a combined Y-axis deviation parameter are calculated, and a position deviation parameter is integrated and obtained.

[0085] Further, a coating control system for photoresist is also used for: According to the spin coating record data within the historical time, a set of sample position deviation parameters and a set of sample angle deviation parameters are collected, and the edge glue amounts of multiple edge regions of the substrate after spin coating under different sample position deviation parameters and sample angle deviation parameters are collected, and a set of sample edge glue amount distributions is obtained by marking; Using a multi-layer feedforward neural network, a spin coating predictor is constructed; Using the set of sample position deviation parameters, the set of sample angle deviation parameters, and the set of sample edge glue amount distributions, the spin coating predictor is supervised and trained and tested until the accuracy rate meets the accuracy threshold, and the construction is completed; Input the position deviation parameter and the angle deviation parameter into the spin coating predictor, and predict and output the edge glue amount distribution.

[0086] Further, a coating control system for photoresist is also used for: According to the edge glue removal historical data of spin coating, a set of sample edge glue amounts is collected, corresponding sample edge glue removal parameters are configured, and a set of sample edge glue removal parameters is obtained by marking; Construct a mapping relationship between the set of sample edge glue amounts and the set of sample edge glue removal parameters to obtain an edge glue removal decision table; Input the multiple edge glue amounts in the edge glue amount distribution into the edge glue removal decision table, map to obtain the edge glue removal parameters of multiple edge regions, and generate an edge glue removal parameter distribution.

[0087] Further, a coating control system for photoresist is also used for: According to the position deviation parameter and the angle deviation parameter, retrieve in the spin coating database to obtain the position deviation occurrence rate and the angle deviation occurrence rate, and calculate and obtain the deviation occurrence rate; Obtain the average deviation occurrence rate of different position deviation parameters and angle deviation parameters in the spin coating database; Judge whether the deviation occurrence rate is greater than or equal to the average deviation occurrence rate; If so, no compensation is made to the edge glue removal parameter distribution. If not, calculate the difference amplitude between the deviation occurrence rate and the average deviation occurrence rate as the compensation adjustment parameter; Obtain a preset compensation coefficient; Using the compensation adjustment parameter, adjust and calculate the preset compensation coefficient to obtain a compensation coefficient; Using the compensation coefficient, perform compensation adjustment calculations on the edge debonding parameter distribution to obtain a compensated edge debonding parameter distribution; According to multiple compensated edge debonding parameters within the compensated edge debonding parameter distribution, perform edge debonding control on multiple edge regions of the substrate to complete coating.

[0088] Example 3, please refer to Figure 3 , Figure 3 which is a schematic diagram of an embodiment of an electronic device provided by an embodiment of the present invention. As Figure 3 shown, an electronic device 200 provided by an embodiment of the present invention includes a memory 210, a processor 220, and a first computer program 211 stored on the memory 210 and executable on the processor 220. When the processor 220 executes the first computer program 211, it implements a coating control method for photoresist.

[0089] It should be noted that in the above embodiments, the descriptions of the respective embodiments have their own emphases. For parts not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0090] Those skilled in the art should understand that the embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present invention can take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk memory, CD-ROM, optical memory, etc.) containing computer-usable program code.

[0091] The present invention is described with reference to the flowcharts and / or block diagrams of methods, apparatuses (systems), and computer program products according to embodiments of the present invention. It should be understood that each flow and / or block in the flowcharts and / or block diagrams, and the combination of flows and / or blocks in the flowcharts and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to the processors of general-purpose computers, special-purpose computers, embedded computers, or other programmable data processing devices to generate a machine, such that the instructions executed by the processors of the computer or other programmable data processing devices generate means for implementing the functions specified in one Figure 1 one flow or multiple flows and / or blocks Figure 1 one block or multiple blocks.

[0092] These computer program instructions can also be stored in a computer-readable memory that can direct a computer or other programmable data processing device to work in a specific manner, such that the instructions stored in the computer-readable memory generate a manufactured article including instruction means that implement the functions in the flow Figure 1One or more flows and / or boxes Figure 1 The functions specified in one or more boxes

[0093] These computer program instructions can also be loaded onto a computer or other programmable data processing device, so that a series of operation steps are executed on the computer or other programmable device to generate a computer-implemented process. Thus, the instructions executed on the computer or other programmable device provide steps for implementing the functions specified in Figure 1 One or more flows and / or boxes Figure 1 One or more boxes

[0094] Although the preferred embodiments of the present invention have been described, those skilled in the art can make additional changes and modifications to these embodiments once they learn the basic creative concept.

[0095] Obviously, those skilled in the art can make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention fall within the scope of the present invention and its equivalent technologies, the present invention also intends to include these modifications and variations.

Claims

1. A method for controlling the coating of a photoresist, characterized in that: The method comprises: Collect an image of the glue dispensing head, perform glue dispensing deviation analysis, and obtain a first position deviation parameter; Collecting an image of the substrate clamping, performing clamping deviation analysis, obtaining a second position deviation parameter and an angle deviation parameter, and combining the first position deviation parameter to calculate and obtain the position deviation parameter; According to the position deviation parameter and the angle deviation parameter, spin coating prediction is performed to obtain the edge glue amount distribution of the substrate after spin coating; According to the edge glue amount distribution, edge degumming parameters are generated to obtain edge degumming parameter distribution, compensation is performed according to the position deviation parameters and angle deviation parameters to obtain compensated edge degumming parameter distribution, edge degumming control is performed on multiple edge areas of the substrate to complete coating.

2. The method for controlling the coating of photoresist according to claim 1, characterized in that: Collect images of the glue dispensing head, perform glue dispensing deviation analysis, and obtain the first position deviation parameters, including: Collect the image of the glue dispensing head to obtain the glue dispensing image; According to the photoresist spin coating history data, a sample glue drop image set is collected, and the glue drop position deviation after glue drop in each sample glue drop image is marked to obtain a sample first position deviation parameter set, wherein the position deviation parameter includes an X-axis deviation parameter and a Y-axis deviation parameter; Based on the convolutional neural network, a glue drop deviation detector is constructed; Using the sample glue drop image set and the sample first position deviation parameter set, supervised training and testing are performed on the glue drop deviation identifier until the accuracy meets the accuracy threshold and the construction is completed; The glue drop image is input into the glue drop deviation identifier to identify and obtain a first position deviation parameter.

3. The coating control method for photoresist according to claim 1, characterized in that: Collect the image of the substrate clamping, perform clamping deviation analysis, and obtain the second position deviation parameter and angle deviation parameter, including: Collecting an image of the substrate clamping to obtain a substrate clamping image; According to the substrate clamping history data, a set of sample substrate clamping images is collected, and the substrate position deviation and angle deviation in each sample substrate clamping image are marked to obtain a sample second position deviation parameter set and a sample angle deviation parameter set, wherein the angle deviation parameter includes an angle formed by the deviation of the substrate from the horizontal plane and a deviation direction; Based on convolutional neural network, a clamping deviation detector is constructed; Using the sample substrate clamping image set, the sample second position deviation parameter set and the sample angle deviation parameter set, supervised training and testing of the clamping deviation identifier are performed until the accuracy meets the accuracy threshold, and the construction is completed; The substrate clamping image is input into the clamping deviation identifier, and a second position deviation parameter and an angle deviation parameter are identified and output.

4. The coating control method for photoresist according to claim 1, characterized in that: Combined with the first position deviation parameter, the position deviation parameter is calculated, including: Extracting a first X-axis deviation parameter, a first Y-axis deviation parameter, a second X-axis deviation parameter, and a second Y-axis deviation parameter from the first position deviation parameter and the second position deviation parameter; A combined X-axis deviation parameter and a combined Y-axis deviation parameter are calculated according to the first X-axis deviation parameter, the first Y-axis deviation parameter, the second X-axis deviation parameter, and the second Y-axis deviation parameter, and the position deviation parameter is obtained by integration.

5. The method for controlling the coating of photoresist according to claim 1, characterized in that: According to the position deviation parameter and the angle deviation parameter, spin coating prediction is performed to obtain the edge glue amount distribution of the substrate after spin coating, including: According to the spin coating record data in the historical time, the sample position deviation parameter set and the sample angle deviation parameter set are collected, and the edge glue amount of multiple edge areas of the substrate after spin coating under different sample position deviation parameters and sample angle deviation parameters are collected, and the sample edge glue amount distribution set is obtained by marking; A multi-layer feed-forward neural network was used to construct a spin coating predictor; Using the sample position deviation parameter set, the sample angle deviation parameter set, and the sample edge glue amount distribution set, supervised training and testing are performed on the spin coating predictor until the accuracy meets the accuracy threshold and the construction is completed; The position deviation parameter and the angle deviation parameter are input into the spin coating predictor to predict and output the edge glue amount distribution.

6. The method for controlling the coating of photoresist according to claim 1, characterized in that: According to the edge glue amount distribution, edge glue removal parameters are generated to obtain edge glue removal parameter distribution, including: According to the historical data of edge degumming of spin coating, a set of sample edge degumming amounts is collected, corresponding sample edge degumming parameters are configured, and a set of sample edge degumming parameters is obtained by annotation; Constructing a mapping relationship between the sample edge glue amount set and the sample edge glue removal parameter set to obtain an edge glue removal decision table; Multiple edge glue amounts in the edge glue amount distribution are input into the edge glue removal decision table, and edge glue removal parameters of multiple edge areas are obtained by mapping to generate an edge glue removal parameter distribution.

7. The method for controlling the coating of photoresist according to claim 1, characterized in that: Compensation is performed according to the position deviation parameter and the angle deviation parameter to obtain a compensation edge debonding parameter distribution, and edge debonding control is performed on a plurality of basic edge areas to complete coating, including: According to the position deviation parameter and the angle deviation parameter, searching in the spin coating database, obtaining the position deviation occurrence rate and the angle deviation occurrence rate, and calculating the deviation occurrence rate; Obtaining average deviation occurrence rates of different position deviation parameters and angle deviation parameters in the spin coating database; Determining whether the deviation occurrence rate is greater than or equal to the average deviation occurrence rate; If yes, then the edge stripping parameter distribution is not compensated, if no, then the difference between the deviation occurrence rate and the average deviation occurrence rate is calculated as a compensation adjustment parameter; Get the preset compensation coefficient; Using the compensation adjustment parameter, adjusting and calculating the preset compensation coefficient to obtain the compensation coefficient; Using the compensation coefficient, performing compensation adjustment calculation on the edge stripping parameter distribution to obtain a compensated edge stripping parameter distribution; According to the plurality of compensation edge stripping parameters within the compensation edge stripping parameter distribution, edge stripping control is performed on a plurality of edge regions of the substrate to complete coating.

8. A coating control system for photoresist, characterized in that: The system is used to execute the method according to any one of claims 1 to 7, and the system comprises: A glue dispensing deviation analysis module is used to collect images of the glue dispensing head, perform glue dispensing deviation analysis, and obtain a first position deviation parameter; A clamping deviation analysis module is used to collect images of substrate clamping, perform clamping deviation analysis, obtain a second position deviation parameter and an angle deviation parameter, and calculate the position deviation parameter in combination with the first position deviation parameter; An edge glue amount prediction module is used to perform spin coating prediction according to the position deviation parameter and the angle deviation parameter to obtain edge glue amount distribution of the substrate after spin coating; The edge degumming control module is used to generate edge degumming parameters according to the edge glue amount distribution, obtain the edge degumming parameter distribution, compensate according to the position deviation parameter and the angle deviation parameter, obtain the compensated edge degumming parameter distribution, perform edge degumming control on multiple edge areas of the substrate, and complete coating.

9. An electronic device, characterized in that: The electronic device comprises: Memory for storing computer software programs; A processor is used to read and execute the computer software program, thereby implementing a coating control method for photoresist as described in any one of claims 1-7.

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