Manufacturing method and device of flexible circuit board

Abstract

The invention provides a manufacturing method and device of a flexible circuit board. The method comprises the following steps: acquiring a first preset pressing parameter; obtaining a bubble prediction result according to the first preset pressing parameter and a trained bubble prediction model; adjusting the first preset pressing parameter according to the bubble prediction result to obtain a second preset pressing parameter; obtaining a first flexible circuit board according to the second preset pressing parameter, a cover film and a flexible circuit board substrate; adjusting the second preset pressing parameter in real time according to the first flexible circuit board to obtain a third preset pressing parameter; and obtaining a target flexible circuit board according to the third preset pressing parameter, a cover film and a flexible circuit board substrate. According to the invention, the bubble rate on the flexible circuit board can be reduced and the use safety of the flexible circuit board can be improved.

Description

Technical Field

[0001] The present invention relates to the technical field of flexible circuit board manufacturing, and also relates to a method and device for manufacturing a flexible circuit board. Background Art

[0002] In the production process of a flexible printed circuit (FPC), after etching to form a circuit pattern, a polyimide film and an adhesive layer need to be laminated on the circuit pattern as a cover layer to protect the circuit, enhance the reliability of the flexible circuit board, and ensure the realization of flexible functions. However, in the existing process of laminating the cover layer and the substrate, due to incomplete discharge of gas or insufficient fluidity of the adhesive layer, air bubbles remain between the cover layer and the substrate. The bubble area is prone to cause copper foil oxidation and circuit breakage, and accelerates delamination failure during bending, which has a great impact on the safety of using the flexible circuit board. Summary of the Invention

[0003] The technical problem to be solved by the present invention is to provide a method and device for manufacturing a flexible circuit board to reduce air bubbles and improve the safety of using the flexible circuit board.

[0004] To solve the above technical problem, the technical solution of the present invention is as follows:

[0005] In the first aspect of the present invention, a method for manufacturing a flexible circuit board is provided, including:

[0006] Obtaining first preset lamination parameters;

[0007] According to the first preset lamination parameters and a trained air bubble prediction model, obtaining an air bubble prediction result;

[0008] Adjusting the first preset lamination parameters according to the air bubble prediction result to obtain second preset lamination parameters;

[0009] According to the second preset lamination parameters, a cover film, and a flexible circuit board substrate, obtaining a first flexible circuit board; the cover film includes a polyimide film and an adhesive layer, and a circuit has been etched on the flexible circuit board substrate;

[0010] According to the first flexible circuit board, adjusting the second preset lamination parameters in real time to obtain third preset lamination parameters;

[0011] According to the third preset lamination parameters, a cover film, and a flexible circuit board substrate, obtaining a target flexible circuit board.

[0012] Optionally, the first preset lamination parameters include:

[0013] A preset lamination temperature, a preset lamination pressure, a preset lamination time, and a preset adhesive layer thickness.

[0014] Optionally, according to the first preset lamination parameter and the trained bubble prediction model, a bubble prediction result is obtained, including:

[0015] Preprocess the first preset lamination parameter to obtain preprocessed data;

[0016] Convert the format of the preprocessed data to obtain a feature vector;

[0017] Input the feature vector into the trained bubble prediction model to obtain a bubble prediction result.

[0018] Optionally, the training process of the bubble prediction model includes:

[0019] Obtain historical data; the historical data includes historical lamination parameters and corresponding bubble area ratios;

[0020] Process the historical data to obtain sample data;

[0021] Divide the sample data to obtain a training set and a test set;

[0022] Train a preset network model according to the training set to obtain a first network model;

[0023] Test the first network model according to the test set to obtain a test result;

[0024] Adjust the first network model according to the test result and a preset evaluation index to obtain a bubble prediction model.

[0025] Optionally, adjust the first preset lamination parameter according to the bubble prediction result to obtain a second preset lamination parameter, including:

[0026] Adjust the first preset lamination parameter according to the bubble prediction result and a preset bubble rate to obtain a second preset lamination parameter.

[0027] Optionally, obtain a first flexible circuit board according to the second preset lamination parameter, a cover film, and a flexible circuit board substrate, including:

[0028] Input the second preset lamination parameter into a laminator, so that the laminator laminates the cover film and the flexible circuit board substrate according to the second preset lamination parameter to obtain a first flexible circuit board.

[0029] Optionally, adjust the second preset lamination parameter in real time according to the first flexible circuit board to obtain a third preset lamination parameter, including:

[0030] Obtain bubble data according to the first flexible circuit board; the bubble data includes bubble position data and bubble area data;

[0031] Adjust the second preset lamination parameter in real time according to the bubble data and a preset bubble rate to obtain a third preset lamination parameter.

[0032] In a second aspect of the present invention, there is provided a manufacturing apparatus for a flexible circuit board, including:

[0033] An acquisition module, configured to acquire a first preset lamination parameter;

[0034] A processing module, configured to obtain a bubble prediction result according to the first preset lamination parameter and a trained bubble prediction model; adjust the first preset lamination parameter according to the bubble prediction result to obtain a second preset lamination parameter; obtain a first flexible circuit board according to the second preset lamination parameter, a cover film, and a flexible circuit board substrate; the cover film includes a polyimide film and an adhesive layer, and circuits have been etched on the flexible circuit board substrate; adjust the second preset lamination parameter in real time according to the first flexible circuit board to obtain a third preset lamination parameter; obtain a target flexible circuit board according to the third preset lamination parameter, the cover film, and the flexible circuit board substrate.

[0035] In a third aspect of the present invention, there is provided a computing device, including: a processor and a memory storing a computer program, and when the computer program is run by the processor, it executes the method described in the first aspect.

[0036] In a fourth aspect of the present invention, there is provided a computer-readable storage medium storing instructions, and when the instructions are run on a computer, the computer is caused to execute the method described in the first aspect.

[0037] The above solution of the present invention at least includes the following beneficial effects:

[0038] In the above solution of the present invention, bubble prediction is performed through a bubble prediction model and the acquired first preset lamination parameter, then the first preset lamination parameter is adjusted according to the prediction result, and then a first flexible circuit board is manufactured according to the second preset lamination parameter, the cover film, and the flexible circuit board substrate. Then, the second preset lamination parameter is adjusted in real time according to the bubble data on the first flexible circuit board, and finally the target flexible circuit board is manufactured, which is beneficial to reducing the bubble rate on the flexible circuit board and improving the use safety of the flexible circuit board. Description of the Drawings

[0039] Figure 1 is a schematic flowchart of a method for manufacturing a flexible circuit board in an embodiment of the present invention;

[0040] Figure 2It is a schematic structural diagram of a manufacturing apparatus for a flexible circuit board in an embodiment of the present invention. Detailed Embodiment

[0041] Hereinafter, exemplary embodiments of the present invention will be described in more detail with reference to the accompanying drawings. Although the exemplary embodiments of the present invention are shown in the drawings, it should be understood that the present invention can be implemented in various forms and should not be limited by the embodiments set forth herein. On the contrary, these embodiments are provided so that the present invention can be more thoroughly understood and the scope of the present invention can be fully conveyed to those skilled in the art.

[0042] As Figure 1 shown, an embodiment of the present invention provides a method for manufacturing a flexible circuit board, including the following steps:

[0043] Step 101, obtaining first preset lamination parameters;

[0044] Step 102, obtaining a bubble prediction result according to the first preset lamination parameters and a trained bubble prediction model;

[0045] Step 103, adjusting the first preset lamination parameters according to the bubble prediction result to obtain second preset lamination parameters;

[0046] Step 104, obtaining a first flexible circuit board according to the second preset lamination parameters, a cover film, and a flexible circuit board substrate; the cover film includes a polyimide film and an adhesive layer, and circuits have been etched on the flexible circuit board substrate;

[0047] Step 105, adjusting the second preset lamination parameters in real time according to the first flexible circuit board to obtain third preset lamination parameters;

[0048] Step 106, obtaining a target flexible circuit board according to the third preset lamination parameters, a cover film, and a flexible circuit board substrate.

[0049] In the method for manufacturing a flexible circuit board according to the embodiment of the present invention, bubble prediction is performed through a bubble prediction model and the obtained first preset lamination parameters, then the first preset lamination parameters are adjusted according to the prediction result, and then a first flexible circuit board is manufactured according to the second preset lamination parameters, a cover film, and a flexible circuit board substrate. Then, the second preset lamination parameters are adjusted in real time according to the bubble data on the first flexible circuit board, and finally, a target flexible circuit board is manufactured, which is beneficial to reducing the bubble rate on the flexible circuit board and improving the use safety of the flexible circuit board.

[0050] In an alternative embodiment of the present invention, the first preset lamination parameters in step 101 include:

[0051] A preset lamination temperature, a preset lamination pressure, a preset lamination time, and a preset adhesive layer thickness.

[0052] Specifically, temperature, pressure, time, and adhesive layer thickness are key parameters for laminating a cover layer (such as a polyimide film adhesive layer) and a flexible circuit board substrate with circuits (or circuit patterns, etc.). In a specific embodiment, the preset lamination temperature can be 160°C, the preset lamination pressure can be 1.5 MPa, the preset lamination time can be 30 seconds, and the preset adhesive layer thickness can be 25 μm. The first preset lamination parameters can be set according to the type of the cover layer and the material of the flexible circuit board substrate, etc., and subsequent bubble prediction and parameter adjustment are performed according to the first preset lamination parameters.

[0053] In an alternative embodiment of the present invention, step 102 includes:

[0054] Step 10211, preprocess the first preset lamination parameters to obtain preprocessed data;

[0055] Specifically, preprocessing the first preset lamination parameters may include: verifying the first preset lamination parameters to obtain verified parameters to ensure the rationality and effectiveness of the first preset lamination parameters; generating target interaction parameters based on the verified parameters, and using the target interaction parameters can effectively capture the non-linear relationship in the flexible circuit board cover layer laminating process and improve the prediction accuracy of the subsequent prediction model for the bubble area ratio; the preprocessed data includes the verified parameters and the target interaction parameters.

[0056] Here, the interaction parameters may include a first interaction parameter and a second interaction parameter. Among them, the first interaction parameter is obtained by A1 = T × P, where A1 is the first interaction parameter, T is the preset lamination temperature, and P is the preset lamination pressure; the first interaction parameter reflects the synergistic effect of temperature and pressure. For example, high temperature enhances the fluidity of the adhesive layer, while high pressure promotes the adhesive layer to fill the gap, and the product of the two can represent the comprehensive effect; the second interaction parameter is obtained by A2 = t / d, where A2 is the second interaction parameter, t is the preset lamination time, and d is the preset adhesive layer thickness; the second interaction parameter represents the processing time required for a unit thickness of the adhesive layer. For example, a thinner adhesive layer requires a shorter time to complete gas discharge, and the ratio of time to adhesive layer thickness can quantify this relationship; the larger the ratio of the preset lamination time to the preset adhesive layer thickness, the longer the processing time for a unit thickness of the adhesive layer, which will affect the bubble residue rate.

[0057] Step 10212, perform format conversion on the preprocessed data to obtain a feature vector;

[0058] Specifically, the temperature, pressure, time, adhesive layer thickness, first interaction parameter, and second interaction parameter in the preprocessed data can be sorted according to a preset order and then arranged into an array, that is, a feature vector. For example, the feature vector of a specific embodiment can be expressed as [T, P, t, d, A1, A2]. This facilitates the subsequent bubble prediction model to process the data in order and improves the processing efficiency.

[0059] Step 10213: Input the feature vector into the trained bubble prediction model to obtain a bubble prediction result.

[0060] Specifically, the bubble prediction model receives the feature vector as input. Each tree in the bubble prediction model independently predicts the input feature vector to obtain a prediction result. The prediction results of all trees are aggregated (which can be taking the average or majority voting) to obtain the final predicted value of the bubble area ratio as the output result of the model. In addition, the bubble prediction model can also output the importance of each feature in the feature vector, such as temperature, pressure, time, and adhesive layer thickness, so as to optimize the first preset pressing parameters according to this importance. The calculation method of the importance of each feature is as follows:

[0061]

[0062] where D is the feature set contained in the node, Entropy(D) is the information entropy, c is the number of categories, p i is the probability that the feature belongs to the i-th category, Entropy split (D, feature, threshold) is the weighted information entropy after splitting, |D left | is the number of features in the left child node after splitting, |D right | is the number of features in the right child node after splitting, Entropy(D left ) is the information entropy of the left child node, Entropy(D right ) is the information entropy of the right child node, Gain(D, feature, threshold) is the information gain, TotalGain f is the total information gain of the f-th feature, F is the total number of features, N is the number of trees in the bubble prediction model, and Importance f is the importance of the f-th feature.

[0063] It should be noted that the bubble prediction results may include the predicted value of the bubble area ratio, the importance of temperature, the importance of pressure, the importance of time, and the importance of the adhesive layer thickness. In a specific embodiment, the bubble prediction results include: the predicted value of the bubble area ratio is 0.5%, the importance of temperature is 35%, the importance of pressure is 28%, the importance of time is 20%, and the importance of the adhesive layer thickness is 12%. Since the predicted value of the bubble area ratio is 0.5% which is greater than the preset bubble rate of 0.2%, it is necessary to adjust the first preset pressing parameters. When adjusting the first preset pressing parameters, the temperature and pressure with higher importance can be preferentially adjusted.

[0064] In an alternative embodiment of the present invention, the training process of the bubble prediction model in step 102 includes:

[0065] Step 10221, obtaining historical data; the historical data includes historical pressing parameters and the corresponding bubble area ratio;

[0066] Specifically, a preset number of historical data can be obtained as needed, such as obtaining 500 groups of historical data from the manufacturer. The historical data includes historical pressing parameters and the corresponding bubble area ratio. The historical pressing parameters include historical pressing temperature, historical pressing pressure, historical pressing time, and historical adhesive layer thickness. In addition, the historical pressing parameters need to cover the first preset pressing parameters. For example, if the first preset pressing parameters include: the preset pressing temperature is 160°C, the preset pressing pressure is 1.5 MPa, the preset pressing time is 30 seconds, and the preset adhesive layer thickness is 25 μm, then the range of the historical pressing temperature in the historical pressing parameters can be 150 to 200°C, the range of the historical pressing pressure can be 1 to 4 MPa, the range of the historical pressing time can be 20 to 80 seconds, and the range of the historical adhesive layer thickness can be 15 to 50 μm.

[0067] Step 10222, performing data processing on the historical data to obtain sample data;

[0068] Specifically, performing missing value processing on the historical data to obtain the first historical data; the method of missing value processing can be filling (such as mean imputation) or deleting incomplete data.

[0069] Performing outlier removal processing on the first historical data to obtain the second historical data; the method of outlier removal processing can be to identify and remove outliers through a box plot or the 3σ principle (such as removing abnormal batches with a bubble area ratio > 10%).

[0070] Performing standardization processing on the second historical data to obtain the third historical data; the method of standardization processing can be: through Performing standardization processing on the second historical data, where X 2 is the third historical data, X1 is the second historical data, μ is the mean of the second historical data, and σ is the standard deviation of the second historical data.

[0071] Generate historical interaction parameters according to the third historical data, where the historical interaction parameters include the first historical interaction parameter and the second historical interaction parameter; among them, the first interaction parameter is obtained through A1 ′ = T ′ × P ′ where A1 ′ is the first historical interaction parameter, T ′ is the historical pressing temperature, and P ′ is the historical pressing pressure; the second interaction parameter is obtained through A2 ′ = t ′ / d ′ where A2 ′ is the second historical interaction parameter, t ′ is the historical pressing time, and d ′ is the historical adhesive layer thickness. The purpose of calculating the historical interaction parameters is to enhance the model's ability to capture non-linear relationships and improve the accuracy of the model.

[0072] Obtain sample data according to the third historical data and the historical interaction parameters; among them, the sample data includes the third historical data and the historical interaction parameters.

[0073] Step 10223: Divide the sample data to obtain a training set and a test set;

[0074] Specifically, the sample data can be divided in a ratio of 8:2, with the training set being 80% and the test set being 20%.

[0075] Step 10224: Train a preset network model according to the training set to obtain a first network model;

[0076] Specifically, the number of trees in the preset network model is 100 to 500, the maximum depth of the trees is 5 to 20, the minimum number of samples for node splitting is 2 to 10, and the maximum number of features for each tree is sqrt (the number of features that can be considered when splitting nodes for each tree is the square root of the total number of features) or log2 (the number of features that can be considered when splitting nodes for each tree is the base-2 logarithm of the total number of features). After converting the training set into a feature matrix X = [T, P, t, d, A1, A2] and inputting it into the preset network model, and using the minimization of the mean squared error as the objective function to train the preset network model, a first network model is obtained. The objective function is where MSE is the mean squared error, M is the number of the training set, is the predicted value of the i-th training data, is the actual value of the i-th training data.

[0077] Step 10225: Test the first network model according to the test set to obtain a test result.

[0078] Specifically, use the test set to test the first network model to obtain a test result, where the test result includes the output result of the model and the proportion of the corresponding bubble area in the test set.

[0079] Step 10226: Adjust the first network model according to the test result and a preset evaluation index to obtain a bubble prediction model.

[0080] Specifically, the preset evaluation index may include the coefficient of determination and the mean absolute error. The coefficient of determination can measure the proportion of the data variation explained by the model, and the mean absolute error can directly reflect the absolute value of the prediction error. Calculate the coefficient of determination through Calculate the mean absolute error through where \(R\) 2 is the coefficient of determination, \(n\) is the number of data in the test set, \(y\) i is the \(i\)-th actual observed value, is the predicted value of the \(i\)-th model, is the average value of all actual observed values, and MAE is the mean absolute error. If the coefficient of determination is greater than a first preset value and the mean absolute error is less than a second preset value, then the first network model at this time is the bubble prediction model; if the coefficient of determination is less than the first preset value or the mean absolute error is greater than the second preset value, then adjust the number of numbers, the maximum depth of the tree, the minimum number of samples for node splitting, and the maximum number of features per tree in the model so that the coefficient of determination is greater than the first preset value and the mean absolute error is less than the second preset value to obtain the bubble prediction model.

[0081] In an alternative embodiment of the present invention, step 103 includes:

[0082] Adjust the first preset pressing parameter according to the bubble prediction result and a preset bubble rate to obtain a second preset pressing parameter.

[0083] Specifically, if the predicted value of the bubble area ratio in the bubble prediction result is greater than the preset bubble rate, the adjustment order is determined according to the importance of each parameter in the bubble prediction result, and the first preset lamination parameter is adjusted according to this adjustment order. In a specific embodiment, the predicted value of the bubble area ratio is 0.5%, the importance of temperature is 35%, the importance of pressure is 28%, the importance of time is 20%, and the importance of the adhesive layer thickness is 12%. Since the predicted value of the bubble area ratio is greater than the preset bubble rate of 0.2%, the first preset lamination parameter needs to be adjusted. Since the importance of temperature and pressure is relatively high, the temperature and pressure are adjusted first to obtain the second preset lamination parameter, which includes the adjusted temperature, the adjusted pressure, the adjusted time, and the adjusted adhesive layer thickness.

[0084] In a specific embodiment, the method for adjusting the first preset lamination parameter is as follows:

[0085] Obtain the preset bubble rate and constraint conditions. For example, the preset bubble rate is 0.2%, and the constraint conditions are that the adjusted temperature is within the actual feasible temperature range, the adjusted pressure is within the actual feasible pressure range, the adjusted time is within the actual feasible time range, the adjusted adhesive layer thickness is within the actual feasible thickness range, and the target bubble rate is within the actual feasible bubble rate range.

[0086] Determine the adjustment objective function according to the preset bubble rate. For example, minimize the absolute value of the difference between the target bubble rate and the preset bubble rate.

[0087] Randomly generate a set of lamination parameters as the initial population; calculate the objective function value for each individual (i.e., each set of parameter combinations), including the absolute value of the difference between the target bubble rate and the preset bubble rate; select superior individuals for reproduction according to the fitness value; generate offspring individuals by exchanging part of the genes of the parent individuals; randomly change the genes of the offspring individuals with a preset probability to increase the diversity of the population; repeat the steps of calculating the objective function value for each individual to randomly changing the genes of the offspring individuals with a preset probability until the predetermined number of iterations or convergence conditions are reached.

[0088] After each iteration, use non-dominated sorting to extract the Pareto front according to the objective function value of the individuals.

[0089] Select one or more final solutions from the Pareto front as the second preset lamination parameter according to the preset bubble rate.

[0090] In an alternative embodiment of the present invention, step 104 includes:

[0091] Input the second preset lamination parameter into the laminator, so that the laminator laminates the cover film and the flexible circuit board substrate according to the second preset lamination parameter to obtain the first flexible circuit board.

[0092] Specifically, the second preset lamination parameters can be first converted into control parameters recognizable by the control system of the laminator, and then the control parameters are input into the laminator, so that the laminator performs a lamination process on the cover film and the flexible circuit board substrate according to the control parameters to obtain a first flexible circuit board.

[0093] In an alternative embodiment of the present invention, step 105 includes:

[0094] Step 1051, obtaining bubble data according to the first flexible circuit board; the bubble data includes bubble position data and bubble area data;

[0095] Step 1052, adjusting the second preset lamination parameters in real time according to the bubble data and a preset bubble rate to obtain third preset lamination parameters.

[0096] Specifically, use an AOI (Automated Optical Inspection) system to perform a high-resolution scan on the first flexible circuit board to obtain a clear image; perform preprocessing on the scanned image, including operations such as denoising and enhancing contrast, to improve the accuracy of image segmentation; apply a preset image segmentation algorithm (such as the UNet image segmentation algorithm) to segment the preprocessed image to obtain a segmentation result; calculate the position (such as centroid coordinates) and area of each bubble according to the segmentation result; convert the position and area information of the bubbles into a heat map to intuitively display the distribution of the bubbles on the cover layer. The heat map can use different colors or brightness levels to represent the density or size of the bubbles; calculate the real-time bubble rate according to the total area of the bubbles and the total area of the cover layer. The bubble rate is the percentage of the total area of the bubbles in the total area of the cover layer; if the real-time bubble rate is greater than the preset bubble rate, the second preset lamination parameters need to be adjusted in real time.

[0097] Specifically, the temperature in the second preset lamination parameters can be increased (for example, from 160°C to 180°C) to enhance the fluidity of the adhesive layer; the pressure can also be increased (adjusted from 1.5 MPa to 2.5 MPa) to promote the uniform extension of the adhesive layer; the lamination time can also be extended (from 30 seconds to 60 seconds) to ensure that the gas is fully discharged. It should be noted that the second preset lamination parameters can also be adjusted in the same way as in step 103 above.

[0098] In an alternative embodiment of the present invention, step 106 includes:

[0099] Input the third preset lamination parameters into the laminator, so that the laminator performs a lamination process on the cover film and the flexible circuit board substrate according to the third preset lamination parameters to obtain a target flexible circuit board.

[0100] Specifically, the third preset lamination parameter can be first converted into a control parameter recognizable by the control system of the laminator, and then the control parameter is input into the laminator, so that the laminator laminates the cover film and the flexible circuit board substrate according to the control parameter to obtain a target flexible circuit board.

[0101] A specific embodiment of the method for manufacturing a flexible circuit board according to an embodiment of the present invention includes:

[0102] Step 111, obtaining a first preset lamination parameter;

[0103] The first preset lamination parameter includes: a preset lamination temperature, a preset lamination pressure, a preset lamination time, and a preset adhesive layer thickness.

[0104] Step 112, bubble prediction;

[0105] Before manufacturing the flexible circuit board, bubble prediction is first performed according to the trained bubble prediction model to save manufacturing costs.

[0106] Step 113, adjusting the first preset lamination parameter;

[0107] If the predicted value of the bubble area ratio in the bubble prediction result is greater than the preset bubble rate, it means that there are more bubbles on the flexible circuit board manufactured according to the first preset lamination parameter and cannot meet the expected requirements. It is necessary to adjust the first preset lamination parameter to obtain a flexible circuit board with fewer bubbles.

[0108] Step 114, manufacturing a first flexible circuit board;

[0109] According to the adjusted preset lamination parameter and the laminator, the cover film is laminated on the flexible circuit board substrate to obtain a first flexible circuit board. The number of first flexible circuit boards can be one or more. A sample can be manufactured for subsequent parameter adjustment, or mass production can be carried out. Subsequently, the bubble rate and product quality are reduced by adjusting the parameters in real time.

[0110] Step 115, adjusting the second preset lamination parameter in real time;

[0111] Calculate the real-time bubble rate on the first flexible circuit board according to the image scanned from the first flexible circuit board. If it is greater than the preset bubble rate, it is necessary to increase the temperature, pressure, and time in the second preset lamination parameter to reduce the bubble rate.

[0112] Step 116, manufacturing a target flexible circuit board.

[0113] Input the second adjusted lamination parameter into the laminator, so that the laminator laminates the cover film and the flexible circuit board substrate according to the second adjusted lamination parameter to obtain a target flexible circuit board.

[0114] The manufacturing method of the flexible circuit board according to the embodiment of the present invention reduces the bubble rate on the target flexible circuit board by adjusting the lamination parameters multiple times, greatly improving the quality of the target flexible circuit board and the product qualification rate, which is beneficial to reducing the production cost and improving the use safety of the flexible circuit board.

[0115] As Figure 2 shown, an embodiment of the present invention provides a manufacturing device 200 for a flexible circuit board, including:

[0116] An acquisition module 201, configured to acquire first preset lamination parameters;

[0117] A processing module 202, configured to obtain a bubble prediction result according to the first preset lamination parameters and a trained bubble prediction model; adjust the first preset lamination parameters according to the bubble prediction result to obtain second preset lamination parameters; obtain a first flexible circuit board according to the second preset lamination parameters, a cover film and a flexible circuit board substrate; the cover film includes a polyimide film and an adhesive layer, and circuits have been etched on the flexible circuit board substrate; adjust the second preset lamination parameters in real time according to the first flexible circuit board to obtain third preset lamination parameters; obtain a target flexible circuit board according to the third preset lamination parameters, a cover film and a flexible circuit board substrate.

[0118] Optionally, the first preset lamination parameters include:

[0119] A preset lamination temperature, a preset lamination pressure, a preset lamination time and a preset adhesive layer thickness.

[0120] Optionally, obtaining a bubble prediction result according to the first preset lamination parameters and a trained bubble prediction model includes:

[0121] Preprocess the first preset lamination parameters to obtain preprocessed data;

[0122] Convert the format of the preprocessed data to obtain a feature vector;

[0123] Input the feature vector into the trained bubble prediction model to obtain a bubble prediction result.

[0124] Optionally, the training process of the bubble prediction model includes:

[0125] Obtain historical data; the historical data includes historical lamination parameters and the corresponding bubble area ratio;

[0126] Process the historical data to obtain sample data;

[0127] Divide the sample data to obtain a training set and a test set;

[0128] Train a preset network model according to the training set to obtain a first network model;

[0129] Test the first network model according to the test set to obtain a test result;

[0130] Adjust the first network model according to the test result and a preset evaluation index to obtain a bubble prediction model.

[0131] Optionally, adjusting the first preset lamination parameter according to the bubble prediction result to obtain a second preset lamination parameter includes:

[0132] Adjust the first preset lamination parameter according to the bubble prediction result and a preset bubble rate to obtain a second preset lamination parameter.

[0133] Optionally, obtaining a first flexible circuit board according to the second preset lamination parameter, a cover film, and a flexible circuit board substrate includes:

[0134] Input the second preset lamination parameter into a laminator, so that the laminator performs a lamination process on the cover film and the flexible circuit board substrate according to the second preset lamination parameter to obtain a first flexible circuit board.

[0135] Optionally, adjusting the second preset lamination parameter in real time according to the first flexible circuit board to obtain a third preset lamination parameter includes:

[0136] Obtain bubble data according to the first flexible circuit board; the bubble data includes bubble position data and bubble area data;

[0137] Adjust the second preset lamination parameter in real time according to the bubble data and a preset bubble rate to obtain a third preset lamination parameter.

[0138] The flexible circuit board manufacturing device according to the embodiment of the present invention predicts bubbles through a bubble prediction model and the obtained first preset lamination parameter, then adjusts the first preset lamination parameter according to the prediction result, and then manufactures a first flexible circuit board according to the second preset lamination parameter, a cover film, and a flexible circuit board substrate. Then, the second preset lamination parameter is adjusted in real time according to the bubble data on the first flexible circuit board to manufacture a target flexible circuit board, which is beneficial to reducing the bubble rate on the flexible circuit board and improving the use safety of the flexible circuit board.

[0139] It should be noted that this device corresponds to the above method, and all implementation manners in the above method embodiments are applicable to the embodiments of this device and can achieve the same technical effects. Details are not described again in this embodiment.

[0140] An embodiment of the present invention further provides a computing device, including: a processor and a memory storing a computer program. When the computer program is run by the processor, it executes the method described in any one of the above embodiments. All implementation manners in the above method embodiments are applicable to the embodiments of this device and can achieve the same technical effects. Details are not described herein again.

[0141] An embodiment of the present invention further provides a computer-readable storage medium, with instructions stored thereon. When the instructions are run on a computer, the computer is caused to execute the method described in any one of the above embodiments. All implementation manners in the above method embodiments are applicable to the embodiments of this device and can achieve the same technical effects. Details are not described herein again.

[0142] It should be noted that in the device and method of the present invention, obviously, each component or each step can be decomposed and / or recombined. These decompositions and / or recombinations shall be regarded as equivalent solutions of the present invention. Moreover, the steps of performing the above series of processes can naturally be executed in chronological order according to the described order, but it is not necessary to be executed in chronological order. Certain steps can be executed in parallel, crosswise, or independently of each other.

[0143] It should be noted that in the above embodiments, the term "comprising", "including" or any other variant thereof is intended to cover a non-exclusive inclusion, such that a process, method, article or device comprising a series of elements not only includes those elements but also includes other elements not expressly listed, or further includes elements inherent to such process, method, article or device. Without further limitation, an element defined by the phrase "comprising a..." does not exclude the existence of additional identical elements in the process, method, article or device comprising such element. In addition, it should be pointed out that the scope of the method and device in the implementation manners of the above embodiments is not limited to performing functions in the order shown or discussed, and may also include performing functions in a substantially simultaneous manner or in a reverse order according to the functions involved. For example, the described method may be executed in an order different from that described, and various steps may be added, omitted, or combined. Additionally, the features described with reference to certain examples may be combined in other examples.

[0144] The above is the preferred implementation manner of the present invention. It should be pointed out that for those of ordinary skill in the art, without departing from the principle of the present invention, several improvements and refinements can be made, and these improvements and refinements should also be regarded as the protection scope of the present invention.

Claims

1. A method for manufacturing a flexible circuit board, characterized in that: include: Obtaining a first preset pressing parameter; Obtaining a bubble prediction result according to the first preset pressing parameters and the trained bubble prediction model; Adjusting the first preset pressing parameters according to the bubble prediction result to obtain second preset pressing parameters; According to the second preset lamination parameters, the cover film and the flexible circuit board substrate, a first flexible circuit board is obtained; the cover film includes a polyimide film and a glue layer, and a circuit is etched on the flexible circuit board substrate; Adjusting the second preset pressing parameters in real time according to the first flexible circuit board to obtain third preset pressing parameters; A target flexible circuit board is obtained according to the third preset lamination parameter, the cover film and the flexible circuit board substrate.

2. The method for manufacturing a flexible circuit board according to claim 1, characterized in that: The first preset pressing parameters include: Preset lamination temperature, preset lamination pressure, preset lamination time and preset adhesive layer thickness.

3. The method for manufacturing a flexible circuit board according to claim 1, characterized in that: According to the first preset pressing parameters and the trained bubble prediction model, a bubble prediction result is obtained, including: Preprocessing the first preset pressing parameters to obtain preprocessing data; Performing format conversion on the preprocessed data to obtain a feature vector; The feature vector is input into the trained bubble prediction model to obtain the bubble prediction result.

4. The method for manufacturing a flexible circuit board according to claim 1, characterized in that: The training process of the bubble prediction model includes: Acquire historical data; the historical data includes historical pressing parameters and corresponding bubble area ratios; Performing data processing on the historical data to obtain sample data; Dividing the sample data into a training set and a test set; Training a preset network model according to the training set to obtain a first network model; Testing the first network model according to the test set to obtain a test result; The first network model is adjusted according to the test results and preset evaluation indicators to obtain a bubble prediction model.

5. The method for manufacturing a flexible circuit board according to claim 1, characterized in that: The first preset lamination parameter is adjusted according to the bubble prediction result to obtain a second preset lamination parameter, including: The first preset pressing parameter is adjusted according to the bubble prediction result and the preset bubble ratio to obtain the second preset pressing parameter.

6. The method for manufacturing a flexible circuit board according to claim 1, characterized in that: According to the second preset lamination parameters, the cover film and the flexible circuit board substrate, a first flexible circuit board is obtained, including: The second preset lamination parameters are input into a laminator, so that the laminator performs a lamination process on the cover film and the flexible circuit board substrate according to the second preset lamination parameters to obtain a first flexible circuit board.

7. The method for manufacturing a flexible circuit board according to claim 1, characterized in that: The second preset pressing parameter is adjusted in real time according to the first flexible circuit board to obtain a third preset pressing parameter, including: According to the first flexible circuit board, bubble data is obtained; the bubble data includes bubble position data and bubble area data; The second preset lamination parameter is adjusted in real time according to the bubble data and the preset bubble ratio to obtain a third preset lamination parameter.

8. A device for manufacturing a flexible circuit board, characterized in that: include: An acquisition module, used for acquiring a first preset pressing parameter; A processing module is used to obtain a bubble prediction result based on the first preset lamination parameter and a trained bubble prediction model; adjust the first preset lamination parameter based on the bubble prediction result to obtain a second preset lamination parameter; obtain a first flexible circuit board based on the second preset lamination parameter, a covering film and a flexible circuit board substrate; the covering film includes a polyimide film and an adhesive layer, and a circuit is etched on the flexible circuit board substrate; adjust the second preset lamination parameter in real time based on the first flexible circuit board to obtain a third preset lamination parameter; obtain a target flexible circuit board based on the third preset lamination parameter, the covering film and the flexible circuit board substrate.

9. A computing device, characterized in that include: A processor and a memory storing a computer program, wherein when the computer program is executed by the processor, the method according to any one of claims 1 to 7 is performed.

10. A computer-readable storage medium, characterized in that: Instructions are stored, and when the instructions are executed on a computer, the computer is caused to execute the method according to any one of claims 1 to 7.

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