A preparation method and system of photoresist for a high-resolution display panel

Abstract

The present invention provides a method and system for preparing a photoresist for a high-resolution display panel, which relates to the field of photoresist preparation. The method includes: randomly generating the preparation parameters of the photoresist within the preparation parameter range, performing integrated prediction of photoresist preparation to obtain multiple photoresist bubble distributions; randomly performing etching simulation within multiple photoresist bubble distributions to obtain multiple etching blur rates; calculating the lithography fitness based on multiple photoresist bubble distributions and multiple etching blur rates, and performing iterative optimization of the photoresist preparation parameters to obtain the optimal photoresist preparation parameters for preparation. The aim is to solve the technical problem that the traditional photoresist preparation method cannot effectively control the bubble interference, resulting in the influence on the accuracy and details of the lithography pattern. Through integrated prediction and algorithm optimization, the accuracy and reliability of the photoresist preparation parameter setting can be significantly improved, thereby effectively controlling the generation and distribution of bubbles and improving the accuracy, clarity and details of the pattern.

Description

Technical Field

[0001] The present invention relates to the field of photoresist preparation, and particularly to a method and system for preparing a photoresist for a high-resolution display panel. Background Art

[0002] Bubbles not only affect the uniformity of the photoresist during the preparation process, but also have a serious impact on the formation of the photolithographic pattern. Especially in high-resolution lithography, the presence of bubbles can lead to blurred pattern edges, loss of details, and even serious defects.

[0003] Bubbles in the photoresist are usually caused by factors such as uneven stirring, solvent evaporation, or external vibration during the preparation process. In traditional photoresist preparation processes, the generation of bubbles is often inevitable, and it is difficult to precisely control the distribution of bubbles. Although some methods such as ultrasonic stirring are applied to reduce the generation of bubbles, due to the randomness and complexity of bubbles, existing technologies are difficult to achieve accurate prediction and precise control in actual production. Summary of the Invention

[0004] The purpose of the present invention is to provide a method and system for preparing a photoresist for a high-resolution display panel, so as to solve the technical problem that the traditional photoresist preparation method cannot effectively control the bubble interference, resulting in affecting the accuracy and details of the photolithographic pattern, including:

[0005] In a first aspect, the present invention provides a method for preparing a photoresist for a high-resolution display panel, including: obtaining a range of preparation parameters during the photoresist preparation process, where the preparation parameters include at least one of photosensitive dose, resin amount, solvent amount, and stirring parameters; randomly generating preparation parameters of the photoresist within the range of the preparation parameters, performing integrated prediction of photoresist preparation to obtain multiple photoresist bubble distributions; randomly performing etching simulations within the multiple photoresist bubble distributions to obtain multiple etching blur rates; calculating a photolithography fitness based on the multiple photoresist bubble distributions and multiple etching blur rates, performing iterative optimization of the photoresist preparation parameters to obtain optimal photoresist preparation parameters, and performing photoresist preparation.

[0006] Preferably, the method for preparing a photoresist for a high-resolution display panel further includes: obtaining the ranges of photosensitive dose, resin amount, solvent amount, and stirring parameters during the photoresist preparation process; combining the ranges of at least one of the photosensitive dose, resin amount, solvent amount, and stirring parameters to obtain a range of preparation parameters.

[0007] Preferably, the method for preparing a photoresist for a high-resolution display panel further includes: randomly generating preparation parameters of the photoresist within the preparation parameter range to obtain the preparation parameters; inputting the preparation parameters into a pre-trained photoresist preparation predictor, and predicting and outputting to obtain a plurality of photoresist bubble distributions, wherein the photoresist preparation predictor includes a plurality of pre-trained photoresist preparation prediction branches.

[0008] Preferably, the method for preparing a photoresist for a high-resolution display panel further includes: according to the photoresist preparation data within the historical time, collecting a sample preparation parameter set, and collecting the bubble position distributions of the bubbles detected in the photoresists actually prepared with different sample preparation parameters, and labeling them as a sample photoresist bubble distribution set, wherein each photoresist bubble distribution includes the bubble coordinates of a plurality of bubbles; combining the sample preparation parameter set and the sample photoresist bubble distribution set to obtain a sample preparation prediction data set; randomly selecting K pieces of data with replacement in the sample preparation prediction data set to obtain K pieces of preparation prediction training data; based on a generative adversarial network, constructing K photoresist preparation prediction branches, wherein each photoresist preparation prediction branch includes a generative network and a discriminative network; respectively using the K pieces of preparation prediction training data to train and test the K photoresist preparation prediction branches, and after passing the test, combining the K photoresist preparation prediction branches to obtain a photoresist preparation predictor.

[0009] Preferably, the method for preparing a photoresist for a high-resolution display panel further includes: obtaining the etching specification parameters for the current etching, wherein the etching specification parameters include the minimum etching pattern size; according to the etching specification parameters, randomly selecting etching positions in the plurality of photoresist bubble distributions respectively to obtain a plurality of etching position distributions, wherein each etching position distribution includes a plurality of etching position coordinates, and the distance between adjacent etching position coordinates is the minimum etching pattern size; respectively calculating the interval distances between the etching position coordinates and the nearest bubble position coordinates in the plurality of etching position distributions and the plurality of photoresist bubble distributions, and calculating the proportion of the etching position coordinates with the interval distance less than a preset distance threshold to obtain a plurality of etching blur rates.

[0010] Preferably, the method for preparing a photoresist for a high-resolution display panel further includes: constructing a resolution evaluation function, and based on the resolution evaluation function, calculating and obtaining a resolution fitness according to the plurality of photoresist bubble distributions and the plurality of etching blur rates; continuing to randomly generate preparation parameters within the preparation parameter range, calculating the resolution fitness, and performing iterative optimization of the photoresist preparation parameters until the optimization converges; outputting the preparation parameters with the maximum resolution fitness to obtain the optimal photoresist preparation parameters, and performing photoresist preparation.

[0011] Preferably, the method for preparing a photoresist for a high-resolution display panel further includes: constructing a resolution evaluation function as follows: ; where RESfit is the resolution fitness, K is the number of distributions of photoresist bubbles, and are weights, is the preset number of bubbles, is a small real number, is the number of bubbles in the i-th distribution of photoresist bubbles, is the preset etching blur rate, is the i-th etching blur rate; obtaining the number of bubbles in the multiple distributions of photoresist bubbles, combining the multiple etching blur rates, and calculating and obtaining the resolution fitness based on the resolution evaluation function.

[0012] In a second aspect, the present invention also provides a photoresist preparation system for a high-resolution display panel, which is used to execute the method for preparing a photoresist for a high-resolution display panel as described in the first aspect, including: a preparation parameter range acquisition module, which is used to acquire the range of preparation parameters in the photoresist preparation process, where the preparation parameters include at least one of photosensitive dose, resin amount, solvent amount, and stirring parameters; a photoresist preparation prediction module, which is used to randomly generate the preparation parameters of the photoresist within the range of the preparation parameters, perform integrated prediction of photoresist preparation, and obtain multiple distributions of photoresist bubbles; a random etching simulation module, which is used to randomly perform etching simulation within the multiple distributions of photoresist bubbles to obtain multiple etching blur rates; a preparation parameter optimization module, which is used to calculate and obtain the lithography fitness according to the multiple distributions of photoresist bubbles and the multiple etching blur rates, perform iterative optimization of the photoresist preparation parameters, obtain the optimal photoresist preparation parameters, and perform photoresist preparation.

[0013] The embodiments of the present invention have the following advantages:

[0014] By obtaining the range of preparation parameters in the preparation process of photoresist, where the preparation parameters include at least one of photosensitive dose, resin amount, solvent amount, and stirring parameters; then randomly generating the preparation parameters of photoresist within the range of the preparation parameters, performing integrated prediction of photoresist preparation to obtain multiple photoresist bubble distributions; then randomly performing etching simulation within each of the multiple photoresist bubble distributions to obtain multiple etching blurring rates; further calculating the photolithography fitness based on the multiple photoresist bubble distributions and multiple etching blurring rates, performing iterative optimization of the photoresist preparation parameters to obtain the optimal photoresist preparation parameters; and finally preparing the photoresist according to the optimal photoresist preparation parameters. That is to say, through integrated prediction and algorithm optimization, the accuracy and reliability of setting photoresist preparation parameters can be significantly improved, thereby effectively controlling the generation and distribution of bubbles, reducing their influence on the photolithography pattern, and thus improving the accuracy, clarity, and details of the pattern. Description of the Drawings

[0015] Figure 1 It is a flowchart of the steps of a method for preparing a photoresist for a high-resolution display panel according to the present invention;

[0016] Figure 2 It is a schematic structural diagram of a system for preparing a photoresist for a high-resolution display panel according to the present invention.

[0017] Description of the Reference Numerals:

[0018] Preparation parameter range acquisition module 11, photoresist preparation prediction module 12, random etching simulation module 13, preparation parameter optimization module 14. Detailed Description of the Embodiment

[0019] The present invention provides a method and system for preparing a photoresist for a high-resolution display panel, which solves the technical problem that the traditional photoresist preparation method cannot effectively control the interference of bubbles, resulting in the influence on the accuracy and details of the photolithography pattern. Through integrated prediction and algorithm optimization, the accuracy and reliability of setting photoresist preparation parameters can be significantly improved, thereby effectively controlling the generation and distribution of bubbles, reducing their influence on the photolithography pattern, and thus improving the accuracy, clarity, and details of the pattern.

[0020] Next, the technical solutions in the present invention will be described clearly and completely with reference to the drawings. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments of the present invention. It should be understood that the present invention is not limited by the exemplary embodiments described herein. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without making creative efforts fall within the scope of the present invention. Additionally, it should be noted that for the sake of description, only the parts related to the present invention are shown in the drawings rather than all of them.

[0021] Example 1, please refer to the appendix Figure 1 , the present invention provides a method for preparing a photoresist for a high-resolution display panel, which is applied to a system for preparing a photoresist for a high-resolution display panel, and specifically includes the following steps:

[0022] S10: Obtain the range of preparation parameters during the preparation of the photoresist, where the preparation parameters include at least one of photosensitizer dose, resin amount, solvent amount, and stirring parameters.

[0023] Furthermore, step S10 of the present invention further includes:

[0024] S11: Obtain the ranges of photosensitizer dose, resin amount, solvent amount, and stirring parameters during the preparation of the photoresist; S12: Combine the ranges of at least one of the photosensitizer dose, resin amount, solvent amount, and stirring parameters to obtain the range of preparation parameters.

[0025] Specifically, first, obtain the ranges of photosensitizer dose, resin amount, solvent amount, and stirring parameters during the preparation of the photoresist. Among them, the photosensitizer dose refers to the concentration of the photosensitizer in the photoresist, which determines the reactivity of the photoresist to light during exposure. Excessive photosensitizer may cause the photoresist to react excessively, resulting in blurred patterns or uneven photoresist, while too little photosensitizer may cause incomplete exposure of the photoresist. Usually, the photosensitizer dose range is 0.5% to 10% (by the proportion of the photosensitizer in the total amount of the photoresist); the resin amount affects the curing performance and fluidity of the photoresist. Too high a concentration of the resin will make the photoresist too viscous, resulting in uneven coating; too low a resin amount may make the photoresist too thin, affecting the stability and resolution of the pattern. Usually, the resin amount range is 20% to 50% (by the proportion of the resin in the total amount of the photoresist); the solvent amount determines the fluidity and coating uniformity of the photoresist. The use of the solvent not only helps to dissolve the resin and the photosensitizer, but also can adjust the viscosity of the photoresist. Too much solvent will increase the volatility of the photoresist, which may cause bubble formation; too little solvent may cause the photoresist to be too thick and difficult to coat evenly. Usually, the solvent amount range is 30% to 60% (by the proportion of the solvent in the total amount of the photoresist). Stirring is a key step in controlling bubble generation during the preparation of the photoresist. Ultrasonic stirring is usually used to reduce the generation of large bubbles, but insufficient stirring will cause bubble residues, while excessive stirring may generate too many small bubbles, affecting the uniformity and performance of the photoresist. Among them, the stirring parameter range includes a frequency range (usually in the range of 20 kHz to 40 kHz), a power range (generally between 100 W and 500 W), and a time (generally controlled within 5 to 30 minutes).

[0026] Next, combine the ranges of at least one parameter among the photosensitive dose, resin amount, solvent amount, and stirring parameter, that is, select at least one parameter among the photosensitive dose, resin amount, solvent amount, and stirring parameter as the preparation parameter. The preparation parameter can be set according to the actual preparation scenario to obtain the preparation parameter range. For example, the preparation parameter can include the photosensitive dose and resin amount, or can also include the photosensitive dose, resin amount, and solvent amount.

[0027] S20: Randomly generate the preparation parameters of the photoresist within the preparation parameter range, perform integrated prediction of photoresist preparation, and obtain multiple photoresist bubble distributions.

[0028] Furthermore, step S20 of the present invention further includes:

[0029] S21: Randomly generate the preparation parameters of the photoresist within the preparation parameter range to obtain the preparation parameters.

[0030] Specifically, randomly select any preparation parameter within the preparation parameter range. For example, assume that the preparation parameter includes the photosensitive dose and resin amount, where the photosensitive dose range is 0.5% to 10%, and the resin amount range is 20% to 50%. Then any preparation parameter can be a photosensitive dose of 3% and a resin amount of 30%.

[0031] S22: Input the preparation parameters into a pre-trained photoresist preparation predictor, and predict and output to obtain multiple photoresist bubble distributions, where the photoresist preparation predictor includes multiple pre-trained photoresist preparation prediction branches.

[0032] Furthermore, step S22 of the present invention further includes:

[0033] S221: According to the photoresist preparation data within the historical time, collect the sample preparation parameter set, and collect the bubble position distributions of the bubbles detected in the photoresist actually prepared with different sample preparation parameters, and label them as the sample photoresist bubble distribution set, where each photoresist bubble distribution includes the bubble coordinates of multiple bubbles; S222: Combine the sample preparation parameter set and the sample photoresist bubble distribution set to obtain the sample preparation prediction data set; S223: Randomly select K pieces of data with replacement within the sample preparation prediction data set to obtain K pieces of preparation prediction training data; S224: Based on the generative adversarial network, construct K photoresist preparation prediction branches, where each photoresist preparation prediction branch includes a generative network and an adversarial network; S225: Respectively use the K pieces of preparation prediction training data to train and test the K photoresist preparation prediction branches. After passing the test, combine the K photoresist preparation prediction branches to obtain the photoresist preparation predictor.

[0034] Specifically, first, according to the photoresist preparation data within a historical time period (such as within the most recent three months), key preparation parameters in the photoresist preparation process are collected. These data are from historical experiments or production processes, and a set of sample preparation parameters is obtained by constructing multiple sample preparation parameters to form a sample preparation parameter set. Then, the bubble position distributions of the photoresists actually prepared with different sample preparation parameters are collected for bubble detection. For example, techniques such as microscopes and scanning electron microscopes are used to detect the bubbles in the photoresist. Among them, the bubbles are marked as different coordinate points (for example, positions on the X and Y coordinate systems). The specific position of each bubble will form a data point. The bubble positions of each detected photoresist sample are recorded to form a bubble position distribution, which is labeled as the bubble distribution of the sample photoresist, and a set of bubble distributions of the sample photoresist is constructed.

[0035] Next, the sample preparation parameters and the sample photoresist bubble distributions with corresponding relationships in the sample preparation parameter set and the sample photoresist bubble distribution set are combined as a set of sample preparation prediction data, and multiple sets of sample preparation prediction data are obtained to form a sample preparation prediction data set. Then, the sample preparation prediction data set is evenly divided into K parts to obtain K sample data sets, and K iterations are performed for selection in the K sample data sets to construct the first set of preparation prediction training data, and the same method is used for K iterations to obtain K sets of preparation prediction training data, where K is an integer greater than or equal to 10.

[0036] A generative adversarial network is a deep learning model composed of two parts, a generator and a discriminator. These two parts are jointly trained in an adversarial manner to achieve the purpose of generating high-quality data. Then, based on the generative adversarial network, K photoresist preparation prediction branches are constructed. Each photoresist preparation prediction branch includes a generation network and an adversarial network. The generation network is used to generate fake sample data (for example, the bubble distribution of the photoresist) to confuse the adversarial network; the adversarial network is used to discriminate whether the sample is "real" or "generated", that is, to distinguish between the real photoresist bubble distribution and the generated fake bubble distribution; these two parts compete with each other through adversarial training, and ultimately the generation network can generate more and more real fake samples.

[0037] Next, the above-mentioned K sets of preparation prediction training data are respectively used to train and test the K photoresist preparation prediction branches. During the training process, the generator accepts the photoresist preparation parameters as input and generates the predicted photoresist bubble distribution; the discriminator accepts the generated bubble distribution and the real bubble distribution, and calculates their authenticity probabilities respectively. The goal of the generator is to maximize the discriminator's judgment probability for false data, so that the generated data can be considered "real" by the discriminator. The task of the discriminator is to judge the gap between the generated bubble distribution and the real data. After each forward propagation, by calculating the loss function of the discriminator, the backpropagation algorithm is executed to update the weights of the discriminator; after the discriminator is updated, the generator is trained, and by calculating the loss function of the generator, the backpropagation is executed to update the weights of the generator. During the testing process, a different dataset (test set) from the training is used. The photoresist preparation parameters are input, and through the trained photoresist preparation prediction branch, the bubble distribution is generated; then, the bubble distribution generated by the generator is compared with the real bubble distribution to evaluate the prediction accuracy of the model; the discriminator evaluates the authenticity of the generated bubbles and outputs its judgment result; if the generated bubble distribution cannot be distinguished as real or false by the discriminator, the performance of the generator is better; if the output of the generator meets the set accuracy standard and the discriminator successfully distinguishes true and false data, this branch is considered to pass the test. At this time, K photoresist preparation prediction branches that pass the test are obtained, and an integrated photoresist preparation predictor is constructed.

[0038] By training K branches and independently training and testing each branch, the generalization ability of the model can be improved, and the integrated method is used to combine each branch, and finally a photoresist preparation predictor with strong robustness and high precision is obtained.

[0039] Finally, the above-mentioned preparation parameters are input into the pre-trained photoresist preparation predictor. Through forward propagation calculation, the predictor will generate the corresponding photoresist bubble distribution according to the input preparation parameters and output multiple photoresist bubble distributions.

[0040] S30: Randomly perform etching simulations within the above-mentioned multiple photoresist bubble distributions to obtain multiple etching blur rates.

[0041] Furthermore, step S30 of the present invention further includes:

[0042] S31: Obtain the etching specification parameters for the current etching, where the etching specification parameters include the minimum etching pattern size; S32: According to the etching specification parameters, randomly select etching positions within the multiple photoresist bubble distributions respectively to obtain multiple etching position distributions, where each etching position distribution includes multiple etching position coordinates, and the distance between adjacent etching position coordinates is the minimum etching pattern size; S33: Calculate the interval distance between the etching position coordinates and the nearest bubble position coordinates within the multiple etching position distributions and the multiple photoresist bubble distributions respectively, and calculate the proportion of the etching position coordinates with the interval distance less than a preset distance threshold to obtain multiple etching blur rates.

[0043] Specifically, first, obtain the etching specification parameters for the current etching, where the etching specification parameters include the minimum etching pattern size, and the minimum etching pattern size determines the size of the smallest pattern for each etching. For example, assume the minimum etching pattern size is 50 nm. Then, for each photoresist bubble distribution, randomly select an etching position within this bubble distribution, and ensure that the distance between adjacent etching positions meets the minimum etching pattern size. For example, randomly select an etching position from the bubble distribution area, and then when selecting a new position, ensure that the distance from the previously selected etching position is at least the minimum etching pattern size, to obtain multiple etching position distributions, where each etching position distribution includes multiple etching position coordinates.

[0044] Then, calculate the interval distance between the etching position coordinates and the nearest bubble position coordinates within the multiple etching position distributions and the multiple photoresist bubble distributions respectively, that is, for each etching position, find the bubble position closest to this etching position and calculate its distance; then count the proportion of the etching position coordinates with the interval distance less than a preset distance threshold (which can be set according to the standard preparation requirements, such as 100 nm). If the distance is less than this threshold, it is considered that the etching position is interfered by the bubble, resulting in pattern blur, and set the proportion of the etching positions affected by the bubble as the etching blur rate to obtain multiple etching blur rates. By calculating the distance between each etching position and the bubble position and judging whether it is interfered by the bubble (the distance is less than the preset threshold), the blur rate of each etching position distribution can be calculated. This process helps to analyze and optimize the preparation process of the photoresist to improve the accuracy and details of the lithography pattern.

[0045] S40: Calculate the lithography fitness based on the multiple photoresist bubble distributions and the multiple etching blur rates, perform iterative optimization of the photoresist preparation parameters to obtain the optimal photoresist preparation parameters, and carry out photoresist preparation.

[0046] Furthermore, step S40 of the present invention further includes:

[0047] S41: Construct a resolution evaluation function, and based on the multiple photoresist bubble distributions and multiple etching blur rates, calculate the resolution fitness according to the resolution evaluation function.

[0048] Further, step S41 of the present invention further includes:

[0049] S411: Construct a resolution evaluation function as follows: ; where RESfit is the resolution fitness, K is the number of multiple photoresist bubble distributions, and are weights, is the preset number of bubbles, is a small real number, is the number of bubbles in the i-th photoresist bubble distribution, is the preset etching blur rate, is the i-th etching blur rate; S412: Obtain the multiple numbers of bubbles in the multiple photoresist bubble distributions, combine the multiple etching blur rates, and calculate the resolution fitness based on the resolution evaluation function.

[0050] Specifically, in the resolution evaluation function, RESfit is the resolution fitness. Among them, the larger the resolution fitness, the better the preparation parameters are characterized; K is the number of multiple photoresist bubble distributions, and are weights. Among them, is the bubble number weight, is the etching blur rate weight, and The sum of them is 1. The weight values can be set according to the influence degree of the indicators on the resolution fitness. The greater the influence degree, the greater the weight; is the preset number of bubbles, is a small real number, which can be set according to actual needs. For example, 10; is the number of bubbles in the i-th photoresist bubble distribution, is the preset etching blur rate, usually an ideal small value, representing the ideal situation without bubble interference, and can be set according to the preparation requirements, is the i-th etching blur rate. By constructing the resolution evaluation function, the influence of the bubble distribution and etching blur rate on the resolution during the photoresist preparation process can be accurately quantified, thereby providing a specific numerical basis for optimizing the preparation parameters.

[0051] Obtain the multiple numbers of bubbles in the multiple photoresist bubble distributions, and then use the resolution evaluation function to calculate the resolution fitness according to the multiple numbers of bubbles and multiple etching blur rates. It reflects the comprehensive influence of the number of bubbles and etching blur rate on the pattern clarity.

[0052] S42: Continuously generate preparation parameters randomly within the range of the said preparation parameters, calculate the resolution fitness, and perform iterative optimization of the photoresist preparation parameters until the optimization converges; S43: Output the preparation parameters with the maximum resolution fitness, obtain the optimal photoresist preparation parameters, and carry out photoresist preparation.

[0053] Specifically, then randomly generate other preparation parameters within the range of the said preparation parameters, and calculate the resolution fitness; use the same method for iterative selection of preparation parameters and calculation of fitness until a predetermined number of iterations is reached. For example, set the number of iterations to 500 times. At this time, multiple preparation parameters and multiple resolution fitness values are obtained. Finally, select the preparation parameters with the maximum resolution fitness as the optimal photoresist preparation parameters and carry out photoresist preparation. By performing iterative optimization within the range of preparation parameters, the preparation parameters that can achieve the best effects on the pattern accuracy and clarity of the photoresist are found, which can effectively improve the quality of photoresist preparation.

[0054] In summary, the method for preparing a photoresist for a high-resolution display panel provided by the present invention has the following technical effects:

[0055] By obtaining the range of preparation parameters in the process of photoresist preparation, where the preparation parameters include at least one of photosensitive dose, resin amount, solvent amount, and stirring parameters; then randomly generate the preparation parameters of the photoresist within the range of the said preparation parameters, conduct integrated prediction of photoresist preparation, and obtain multiple photoresist bubble distributions; then randomly conduct etching simulations within the multiple photoresist bubble distributions respectively to obtain multiple etching blur rates; further calculate the lithography fitness based on the multiple photoresist bubble distributions and multiple etching blur rates, perform iterative optimization of the photoresist preparation parameters, and obtain the optimal photoresist preparation parameters; finally, carry out photoresist preparation according to the optimal photoresist preparation parameters. That is to say, through integrated prediction and algorithm optimization, the accuracy and reliability of the setting of photoresist preparation parameters can be significantly improved, thereby effectively controlling the generation and distribution of bubbles, reducing their influence on the lithography pattern, and thus improving the accuracy, clarity, and details of the pattern.

[0056] Embodiment 2, based on the same inventive concept as the method for preparing a photoresist for a high-resolution display panel in the foregoing embodiment, the present invention also provides a system for preparing a photoresist for a high-resolution display panel. Please refer to the appendix Figure 2, comprising: a preparation parameter range acquisition module 11, configured to acquire the preparation parameter range in the photoresist preparation process, wherein the preparation parameters include at least one of photosensitive dose, resin amount, solvent amount, and stirring parameter; a photoresist preparation prediction module 12, configured to randomly generate the preparation parameters of the photoresist within the preparation parameter range, perform integrated prediction of photoresist preparation, and obtain multiple photoresist bubble distributions; a random etching simulation module 13, configured to randomly perform etching simulation within the multiple photoresist bubble distributions respectively, and obtain multiple etching blur rates; a preparation parameter optimization module 14, configured to calculate and obtain the lithography fitness according to the multiple photoresist bubble distributions and the multiple etching blur rates, perform iterative optimization of the photoresist preparation parameters, obtain the optimal photoresist preparation parameters, and perform photoresist preparation.

[0057] Further, the photoresist preparation system for a high-resolution display panel is further configured to: acquire the ranges of photosensitive dose, resin amount, solvent amount, and stirring parameter in the photoresist preparation process; combine the ranges of at least one of the photosensitive dose, resin amount, solvent amount, and stirring parameter to obtain the preparation parameter range.

[0058] Further, the photoresist preparation system for a high-resolution display panel is further configured to: randomly generate the preparation parameters of the photoresist within the preparation parameter range to obtain the preparation parameters; input the preparation parameters into a pre-trained photoresist preparation predictor, and predict and output to obtain multiple photoresist bubble distributions, wherein the photoresist preparation predictor includes multiple pre-trained photoresist preparation prediction branches.

[0059] Further, the photoresist preparation system for a high-resolution display panel is further configured to: according to the photoresist preparation data within the historical time, collect a sample preparation parameter set, and collect the bubble position distribution of the bubbles detected in the photoresist actually prepared with different sample preparation parameters, and label it as a sample photoresist bubble distribution set, wherein each photoresist bubble distribution includes the bubble coordinates of multiple bubbles; combine the sample preparation parameter set and the sample photoresist bubble distribution set to obtain a sample preparation prediction data set; randomly select K pieces of data with replacement within the sample preparation prediction data set to obtain K pieces of preparation prediction training data; based on the generative adversarial network, construct K photoresist preparation prediction branches, wherein each photoresist preparation prediction branch includes a generative network and an adversarial network; respectively use the K pieces of preparation prediction training data to train and test the K photoresist preparation prediction branches, and after passing the test, combine the K photoresist preparation prediction branches to obtain the photoresist preparation predictor.

[0060] Further, the lithography resist preparation system for a high-resolution display panel is further configured to: obtain etching specification parameters for the current etching, where the etching specification parameters include the minimum etched pattern size; select etching positions randomly within the multiple lithography resist bubble distributions according to the etching specification parameters to obtain multiple etching position distributions, where each etching position distribution includes multiple etching position coordinates, and the distance between adjacent etching position coordinates is the minimum etched pattern size; calculate the interval distances between the etching position coordinates and the nearest bubble position coordinates within the multiple etching position distributions and the multiple lithography resist bubble distributions, and calculate the proportion of the etching position coordinates with the interval distance less than a preset distance threshold to obtain multiple etching blur rates.

[0061] Further, the lithography resist preparation system for a high-resolution display panel is further configured to: construct a resolution evaluation function, and based on the multiple lithography resist bubble distributions and multiple etching blur rates, calculate a resolution fitness according to the resolution evaluation function; continue to randomly generate preparation parameters within the preparation parameter range, calculate the resolution fitness, and perform iterative optimization of the lithography resist preparation parameters until optimization convergence; output the preparation parameters with the maximum resolution fitness to obtain the optimal lithography resist preparation parameters and perform lithography resist preparation.

[0062] Further, the lithography resist preparation system for a high-resolution display panel is further configured to: construct a resolution evaluation function as follows: ; where RESfit is the resolution fitness, K is the number of multiple lithography resist bubble distributions, and are weights, is the preset number of bubbles, is a small real number, is the number of bubbles within the i-th lithography resist bubble distribution, is the preset etching blur rate, is the i-th etching blur rate; obtain the number of bubbles within the multiple lithography resist bubble distributions, combine with the multiple etching blur rates, and calculate the resolution fitness based on the resolution evaluation function.

[0063] In this specification, the various embodiments are described in a progressive manner. Each embodiment focuses on the differences from other embodiments. The method for preparing a photoresist for a high-resolution display panel and the specific examples in the foregoing Embodiment 1 are equally applicable to the photoresist preparation system for a high-resolution display panel in this embodiment. Through the foregoing detailed description of the method for preparing a photoresist for a high-resolution display panel, those skilled in the art can clearly know the photoresist preparation system for a high-resolution display panel in this embodiment. Therefore, for the sake of brevity of the specification, it will not be described in detail herein. For the system disclosed in the embodiment, since it corresponds to the method disclosed in the embodiment, the description is relatively simple. For the relevant parts, reference may be made to the description in the method section.

[0064] The foregoing description of the disclosed embodiments enables those skilled in the art to implement or use the present invention. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein can be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention will not be limited to the embodiments shown herein, but will be accorded the widest scope consistent with the principles and novel features disclosed herein.

[0065] 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 is also intended to include these changes and modifications.

Claims

1. A preparation method of a photoresist for a high-resolution display panel, characterized in that The method includes: Obtaining the range of preparation parameters in the photoresist preparation process, where the preparation parameters include at least one of photosensitive dose, resin amount, solvent amount, and stirring parameters; Randomly generating the preparation parameters of the photoresist within the range of the preparation parameters, performing integrated prediction of photoresist preparation, and obtaining multiple photoresist bubble distributions; Respectively within the multiple photoresist bubble distributions, randomly performing etching simulation to obtain multiple etching blur rates, including: Obtaining the etching specification parameters for the current etching, where the etching specification parameters include the minimum etching pattern size; According to the etching specification parameters, respectively randomly select etching positions within the multiple photoresist bubble distributions to obtain multiple etching position distributions, where each etching position distribution includes multiple etching position coordinates, and the distance between adjacent etching position coordinates is the minimum etching pattern size; Respectively within the multiple etching position distributions and the multiple photoresist bubble distributions, calculate the interval distance between the etching position coordinates and the nearest bubble position coordinates, and calculate the proportion of the etching position coordinates with the interval distance less than the preset distance threshold to obtain multiple etching blur rates; According to the multiple photoresist bubble distributions and multiple etching blur rates, calculate the lithography fitness, perform iterative optimization of the photoresist preparation parameters, obtain the optimal photoresist preparation parameters, and perform photoresist preparation.

2. The method for preparing a photoresist for a high-resolution display panel according to claim 1, wherein Obtaining the range of preparation parameters in the photoresist preparation process, including: Obtaining the ranges of photosensitive dose, resin amount, solvent amount, and stirring parameters in the photoresist preparation process; Combining the ranges of at least one of the photosensitive dose, resin amount, solvent amount, and stirring parameters to obtain the range of preparation parameters.

3. The method for preparing a photoresist for a high-resolution display panel according to claim 1, wherein, Randomly generating the preparation parameters of the photoresist within the range of the preparation parameters, and performing integrated prediction of photoresist preparation, including: Randomly generating the preparation parameters of the photoresist within the range of the preparation parameters to obtain the preparation parameters; Inputting the preparation parameters into a pre-trained photoresist preparation predictor, and predicting and outputting to obtain multiple photoresist bubble distributions, where the photoresist preparation predictor includes multiple pre-trained photoresist preparation prediction branches.

4. The method for preparing a photoresist for a high-resolution display panel according to claim 3, wherein, The pre-training steps of the photoresist preparation predictor include: According to the photoresist preparation data within the historical time, collecting a set of sample preparation parameters, and collecting the bubble position distributions of the bubbles detected in the photoresists actually prepared with different sample preparation parameters, and labeling them as a set of sample photoresist bubble distributions, where each photoresist bubble distribution includes the bubble coordinates of multiple bubbles; Combining the set of sample preparation parameters and the set of sample photoresist bubble distributions to obtain a set of sample preparation prediction data; Randomly selecting K pieces of data with replacement within the set of sample preparation prediction data to obtain K pieces of preparation prediction training data; Based on the generative adversarial network, constructing K photoresist preparation prediction branches, where each photoresist preparation prediction branch includes a generative network and an adversarial network; Respectively using the K pieces of preparation prediction training data to train and test the K photoresist preparation prediction branches, and after passing the test, combining the K photoresist preparation prediction branches to obtain the photoresist preparation predictor.

5. The method for preparing a photoresist for a high-resolution display panel according to claim 1, wherein Based on the multiple photoresist bubble distributions and multiple etching blur rates, calculate the lithography fitness, perform iterative optimization of the photoresist preparation parameters, obtain the optimal photoresist preparation parameters, and perform photoresist preparation, including: Construct a resolution evaluation function, and based on the resolution evaluation function, calculate the resolution fitness according to the multiple photoresist bubble distributions and multiple etching blur rates; Continuously randomly generate preparation parameters within the range of the preparation parameters, calculate the resolution fitness, and perform iterative optimization of the photoresist preparation parameters until optimization convergence; Output the preparation parameters with the maximum resolution fitness, obtain the optimal photoresist preparation parameters, and perform photoresist preparation.

6. The method for preparing a photoresist for a high-resolution display panel according to claim 5, wherein Construct a resolution evaluation function, and based on the resolution evaluation function, calculate the resolution fitness according to the multiple photoresist bubble distributions and multiple etching blur rates, including: Construct a resolution evaluation function as follows: ； Among them, RESfit is the resolution fitness, K is the number of photoresist bubble distributions, and is the weight, is the preset number of bubbles, X is a small real number, is the number of bubbles in the i-th photoresist bubble distribution, is the preset etching blur rate, is the i-th etching blur rate; Obtain the multiple bubble numbers within the multiple photoresist bubble distributions, combine the multiple etching blur rates, and based on the resolution evaluation function, calculate the resolution fitness.

7. A photoresist preparation system for a high-resolution display panel, characterized in that, Steps for implementing the method for preparing a photoresist for a high-resolution display panel according to any one of claims 1 to 6, including: A preparation parameter range acquisition module, configured to acquire the range of preparation parameters during photoresist preparation, where the preparation parameters include at least one of photosensitive dose, resin amount, solvent amount, and stirring parameters; A photoresist preparation prediction module, configured to randomly generate the preparation parameters of the photoresist within the range of the preparation parameters, perform integrated prediction of photoresist preparation, and obtain multiple photoresist bubble distributions; A random etching simulation module, configured to randomly perform etching simulation within each of the multiple photoresist bubble distributions to obtain multiple etching blur rates; A preparation parameter optimization module, configured to calculate the lithography fitness according to the multiple photoresist bubble distributions and multiple etching blur rates, perform iterative optimization of the photoresist preparation parameters, obtain the optimal photoresist preparation parameters, and perform photoresist preparation.

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