Method and system for predicting dry film type solder mask thickness range of product

Abstract

The invention discloses a method for predicting the dry film type solder mask thickness range of a product, and the method comprises the steps: obtaining the support plate design data of a to-be-processed IC support plate, carrying out the grid division of a copper surface of the support plate, and extracting the copper area proportion data in each grid unit through an image processing technology; calculating the predicted dry film type solder mask thickness of each grid unit through a dry film type solder mask thickness calculation formula by combining the copper area proportion data of each grid unit with dry film type ink characteristic parameters; based on the predicted dry film type solder resist thicknesses of all the grid units, generating a dry film type solder resist thickness distribution thermodynamic diagram of the whole plate through numerical simulation, and identifying a risk area in which the dry film type solder resist thickness range exceeds a preset threshold value; and recommending process parameters for optimizing the dry film type solder resist thickness based on the identified risk area and the dry film type solder resist thickness range thereof. According to the method, accurate prediction and control of the dry film type solder mask thickness range can be realized.

Description

Technical Field

[0001] This invention relates to the field of IC substrate manufacturing technology, specifically to a method and system for predicting the thickness variation of dry film solder resist in products. Background Technology

[0002] In IC substrate manufacturing, the solder mask dry film process is a critical step to ensure circuit reliability. In traditional processes, uneven copper surface pattern distribution leads to variations in ink leveling, which in turn affects local dry film thickness, resulting in excessively large differences in overall board thickness (the difference between the maximum and minimum values). Existing technologies mainly suffer from the following problems: (1) The dry film thickness is calculated by relying on the overall residual copper rate of the whole board or unit, which cannot accurately reflect the local thickness changes; (2) The lack of a quantitative prediction model based on the distribution of copper surface patterns leads to inaccurate control of thickness range and high product defect rate; (3) The adjustment of process parameters mainly relies on experience and lacks data support, making it difficult to meet the strict requirements of high-density packaging for thickness uniformity. Summary of the Invention

[0003] The technical problem to be solved by the present invention is to provide a method and system for predicting the thickness difference of dry film solder resist in order to overcome the shortcomings of the prior art. The method achieves accurate prediction and control of the thickness difference of dry film solder resist by correlating the copper area distribution with the thickness of dry film solder resist.

[0004] To achieve the above objectives, according to one aspect of the present invention, a method for predicting the thickness variation of dry film solder resist in a product is provided, comprising: Obtain the substrate design data of the IC substrate to be processed, divide the copper surface of the substrate into grids, and extract the copper area ratio data in each grid cell through image processing technology; The copper area ratio of each grid cell is combined with the dry film ink characteristic parameters, and the predicted dry film solder resist thickness of each grid cell is calculated using the dry film solder resist thickness calculation formula. Based on the predicted dry film solder resist thickness of all grid cells, a heat map of the dry film solder resist thickness distribution of the entire board is generated through numerical simulation, and risk areas where the dry film solder resist thickness range exceeds a preset threshold are identified. Based on the identified risk areas and their extreme differences in dry film solder resist thickness, recommended process parameters are provided for optimizing dry film solder resist thickness.

[0005] In the above scheme, the formula for calculating the thickness of the dry film solder resist is established based on the dry film incoming thickness, residual copper rate, copper thickness, and thickness loss coefficient. The specific calculation formula is as follows: H=(L-(1 R)×h)×k Where H is the dry film solder resist thickness, L is the dry film incoming material thickness (i.e., the original material thickness), R is the residual copper rate, h is the copper thickness, and k is the thickness loss coefficient. The residual copper rate is the percentage of copper area within each grid cell; the thickness loss coefficient is specifically set based on the dry film ink characteristic parameters.

[0006] In the above scheme, the characteristic parameters of the dry film ink include ink type, resin content, and filler content.

[0007] In the above scheme, the meshing of the copper surface of the carrier board specifically includes: Based on the dimensions of the carrier board and the preset grid accuracy requirements, the copper surface of the carrier board is divided into square grid units with a side length of 0.5-2mm.

[0008] In the above scheme, the image processing technology includes Incampro image processing technology.

[0009] In the above scheme, the dry film solder resist thickness distribution heat map uses different colors to represent the dry film solder resist thickness in different areas, intuitively displaying the thickness distribution in the entire effective area.

[0010] In the above scheme, the step of generating a thermal map of the dry film solder resist thickness distribution of the entire board through numerical simulation and identifying risk areas where the dry film solder resist thickness difference exceeds a preset threshold includes: Numerical simulation software was used to generate a thermal map of the thickness distribution of dry film solder resist, and areas with abrupt changes in the copper area ratio were identified as risk areas.

[0011] In the above scheme, the preset threshold is 5μm.

[0012] In the above scheme, the process parameters are vacuum pressing parameters, including vacuuming time, vacuum pressure, vacuum pressing time, vacuum temperature, leveling time, leveling pressure, and leveling temperature.

[0013] According to another aspect of the present invention, a system for predicting the thickness variation of dry film solder resist in a product is provided, comprising: The image processing module is used to acquire the design data of the IC substrate to be processed, divide the copper surface of the substrate into grids, and extract the copper area ratio data in each grid cell through image processing technology. The thickness calculation module is used to combine the copper area ratio data of each grid cell with the dry film ink characteristic parameters and calculate the predicted dry film solder resist thickness of each grid cell using the dry film solder resist thickness calculation formula. The risk identification module is used to predict the dry film solder resist thickness based on all grid cells, generate a heat map of the dry film solder resist thickness distribution of the whole board through numerical simulation, and identify risk areas where the dry film solder resist thickness range exceeds a preset threshold. The process optimization module recommends process parameters for optimizing the dry film solder resist thickness based on the identified risk areas and their dry film solder resist thickness range.

[0014] In summary, compared with the prior art, the above-described technical solutions conceived by this invention can achieve the following beneficial effects: (1) This invention provides a method for predicting the thickness difference of dry film solder resist. This method uses a copper area-thickness correlation model to achieve quantitative prediction of the thickness difference of dry film solder resist, with an accuracy 30% better than the traditional empirical method.

[0015] (2) The method for predicting the thickness difference of dry film solder resist provided by the present invention reduces dry film waste and rework rate, and reduces the production cost of single board by 20%. The method provided by the present invention has strong compatibility and is applicable to the dry film lamination process of rigid substrate (BT / ABF) and flexible substrate (PI). Attached Figure Description

[0016] Various other advantages and benefits will become apparent to those skilled in the art upon reading the following detailed description of preferred embodiments. The accompanying drawings are for illustrative purposes only and are not intended to limit the invention. Furthermore, the same reference numerals denote the same parts throughout the drawings. In the drawings: Figure 1 This is a flowchart illustrating a method for predicting the thickness variation of dry film solder resist in a product according to Embodiment 1 of the present invention. Detailed Implementation

[0017] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention. Furthermore, the technical features involved in the various embodiments of this invention described below can be combined with each other as long as they do not conflict with each other.

[0018] It should be understood that the sequence number of each step in the embodiment does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of this application.

[0019] Example 1 This application provides a method for predicting the thickness variation of dry film solder resist in IC substrates, using the dry film solder resist process as an example. Please refer to [link to relevant documentation]. Figure 1,include: S1. Obtain the substrate design data of the IC substrate to be processed, divide the copper surface of the substrate into grids, and extract the copper area ratio data in each grid cell through image processing technology.

[0020] In this embodiment, after obtaining the carrier design data of the IC carrier to be processed, it is necessary to perform meshing on the copper surface of the carrier, specifically including: Based on the dimensions of the substrate and the preset grid precision requirements, the copper surface of the substrate is divided into square grid cells with a side length of 1mm. Then, Incampro image processing technology is used to extract the copper area percentage data within each grid cell.

[0021] S2, by combining the copper area ratio data of each grid cell with the dry film ink characteristic parameters, the predicted dry film solder resist thickness of each grid cell is calculated using the dry film solder resist thickness calculation formula.

[0022] Specifically, in this embodiment, the formula for calculating the thickness of the dry film solder resist is established based on the dry film incoming thickness, residual copper ratio, copper thickness, and thickness loss coefficient. The specific calculation formula is as follows: H=(L-(1 R)×h)×k Where H is the dry film solder resist thickness, L is the dry film incoming material thickness (i.e., the original material thickness), R is the residual copper rate, h is the copper thickness, and k is the thickness loss coefficient. The residual copper rate is the percentage of copper area within each grid cell; the thickness loss coefficient is set specifically according to the characteristic parameters of dry film inks.

[0023] In this embodiment, PSR-800 AUS SR1 ink is selected. The characteristic parameters of dry film ink include ink type, resin content, and filler content. In this embodiment, PSR-800 AUS SR1 ink is a developing type solder resist dry film used in PCB and carrier board fields. This ink contains 30-40% acrylic resin, 10-20% barium sulfate, 10-20% epoxy resin, and 10-20% silica. Therefore, in this embodiment, the thickness loss coefficient k is set to 0.95.

[0024] S3, based on the predicted dry film solder resist thickness of all grid cells, generates a heat map of the dry film solder resist thickness distribution of the entire board through numerical simulation, and identifies risk areas where the dry film solder resist thickness range exceeds a preset threshold.

[0025] In this embodiment, after obtaining the predicted dry film solder resist thickness for each grid cell, a heat map of the dry film solder resist thickness distribution of the entire board is generated through numerical simulation. The dry film solder resist thickness distribution heat map uses different colors to represent the dry film solder resist thickness in different regions, intuitively displaying the thickness distribution of the entire board; and identifies areas with abrupt changes in copper area ratio data (dry film solder resist thickness range exceeding a preset threshold, the preset threshold being 5μm) as risk areas.

[0026] S4, based on the identified risk areas and their dry film solder resist thickness variations, recommends process parameters for optimizing dry film solder resist thickness.

[0027] In this embodiment, based on the identified risk areas, the dry film solder resist thickness range (the difference between the maximum and minimum dry film solder resist thickness) is obtained, and the overall dry film solder resist thickness range is obtained. Recommended process parameters for optimizing the dry film solder resist thickness are specifically included in the vacuum pressing parameters, including vacuuming time, vacuum pressure, vacuum pressing time, vacuum temperature, leveling time, leveling pressure, and leveling temperature.

[0028] Specifically, based on the classification of range values, the recommended vacuum lamination parameters for optimizing the thickness of dry film solder resist are as follows: (1) Range ≤ 5μm, vacuum time is 30s, vacuum pressure is 5kg, vacuum pressure time is 20s, vacuum temperature is 70℃, leveling time is 30s, leveling pressure is 4kg, leveling temperature is 70℃; (2) 5μm Range ≤10μm, vacuum time is 30s, vacuum pressure is 6kg, vacuum pressure time is 20s, vacuum temperature is 80℃, leveling time is 30s, leveling pressure is 4kg, leveling temperature is 80℃. (3) 10μm Range ≤15μm, vacuum time is 30s, vacuum pressure is 10kg, vacuum pressure time is 30s, vacuum temperature is 90℃, leveling time is 40s, leveling pressure is 5kg, leveling temperature is 80℃; (4) 15μm Range ≤20μm, vacuum time 30s, vacuum pressure 11kg, vacuum pressure time 40s, vacuum temperature 90℃, leveling time 80s, leveling pressure 6kg, leveling temperature 90℃.

[0029] In summary, this embodiment provides a method for predicting the thickness variation of dry film solder resist. This method achieves accurate prediction and control of the thickness variation of dry film solder resist by correlating the copper area distribution with the thickness of the dry film solder resist.

[0030] Example 2 One embodiment of this application provides a method for predicting the thickness variation of dry film solder resist in a product. This method is basically the same as the method in Embodiment 1, except that: Based on the dimensions of the substrate and the preset grid precision requirements, the copper surface of the substrate is divided into square grid cells with a side length of 2mm. Then, image processing technology is used to extract the copper area percentage data within each grid cell.

[0031] Another aspect of this application provides a system for predicting the thickness variation of dry film solder resist in a product, comprising: The image processing module is used to acquire the design data of the IC substrate to be processed, divide the copper surface of the substrate into grids, and extract the copper area ratio data in each grid cell through image processing technology. The thickness calculation module is used to combine the copper area ratio data of each grid cell with the dry film ink characteristic parameters, and calculate the predicted dry film solder resist thickness of each grid cell using the dry film solder resist thickness calculation formula. The risk identification module is used to predict the dry film solder resist thickness based on all grid cells, generate a heat map of the dry film solder resist thickness distribution of the whole board through numerical simulation, and identify risk areas where the dry film solder resist thickness range exceeds a preset threshold. The process optimization module recommends process parameters for optimizing the dry film solder resist thickness based on the identified risk areas and their dry film solder resist thickness range.

[0032] It should be noted that, depending on the implementation needs, the various steps described in this application can be broken down into more steps, or two or more steps or parts of the steps can be combined into new steps to achieve the purpose of this invention.

[0033] Those skilled in the art will readily understand that the above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A method for predicting the thickness variation of dry film solder resist in a product, characterized in that, include: Obtain the substrate design data of the IC substrate to be processed, divide the copper surface of the substrate into grids, and extract the copper area ratio data in each grid cell through image processing technology; The copper area ratio of each grid cell is combined with the dry film ink characteristic parameters, and the predicted dry film solder resist thickness of each grid cell is calculated using the dry film solder resist thickness calculation formula. Based on the predicted dry film solder resist thickness of all grid cells, a heat map of the dry film solder resist thickness distribution of the entire board is generated through numerical simulation, and risk areas where the dry film solder resist thickness range exceeds a preset threshold are identified. Based on the identified risk areas and their extreme differences in dry film solder resist thickness, recommended process parameters are provided for optimizing dry film solder resist thickness.

2. The method for predicting the thickness difference of dry film solder resist in a product according to claim 1, characterized in that, The formula for calculating the thickness of dry film solder resist is established based on the dry film incoming material thickness, residual copper rate, copper thickness, and thickness loss coefficient. The residual copper rate is the percentage of copper area within each grid cell; the thickness loss coefficient is set according to the dry film ink characteristic parameters.

3. The method for predicting the thickness difference of dry film solder resist in a product according to claim 1, characterized in that, The dry film ink's characteristic parameters include ink type, resin content, and filler content.

4. The method for predicting the thickness difference of dry film solder resist in a product according to claim 1, characterized in that, The process of dividing the copper surface of the carrier board into a grid includes: Based on the dimensions of the carrier board and the preset grid accuracy requirements, the copper surface of the carrier board is divided into square grid units with a side length of 0.5-2mm.

5. The method for predicting the thickness difference of dry film solder resist in a product according to claim 1, characterized in that, The image processing technology mentioned includes Incampro image processing technology.

6. The method for predicting the thickness difference of dry film solder resist in a product according to claim 1, characterized in that, The dry film solder resist thickness distribution heatmap uses different colors to represent the dry film solder resist thickness in different areas, visually displaying the thickness distribution throughout the entire effective area.

7. The method for predicting the thickness difference of dry film solder resist in a product according to claim 1, characterized in that, The process of generating a thermal map of the dry film solder resist thickness distribution across the entire board through numerical simulation, and identifying risk areas where the dry film solder resist thickness variation exceeds a preset threshold, includes: Numerical simulation software was used to generate a thermal map of the thickness distribution of dry film solder resist, and areas with abrupt changes in the copper area ratio were identified as risk areas.

8. The method for predicting the thickness difference of dry film solder resist in a product according to claim 1, characterized in that, The preset threshold is 5μm.

9. The method for predicting the thickness difference of dry film solder resist in a product according to claim 1, characterized in that, The process parameters include vacuum pressing parameters, including vacuuming time, vacuum pressure, vacuum pressing time, vacuum temperature, leveling time, leveling pressure, and leveling temperature.

10. A system for predicting the thickness variation of dry film solder resist in products, characterized in that, include: The image processing module is used to acquire the design data of the IC substrate to be processed, divide the copper surface of the substrate into grids, and extract the copper area ratio data in each grid cell through image processing technology. The thickness calculation module is used to combine the copper area ratio data of each grid cell with the dry film ink characteristic parameters and calculate the predicted dry film solder resist thickness of each grid cell using the dry film solder resist thickness calculation formula. The risk identification module is used to predict the dry film solder resist thickness based on all grid cells, generate a heat map of the dry film solder resist thickness distribution of the whole board through numerical simulation, and identify risk areas where the dry film solder resist thickness range exceeds a preset threshold. The process optimization module recommends process parameters for optimizing the dry film solder resist thickness based on the identified risk areas and their dry film solder resist thickness range.

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