Technology for laser drilling of hole deviation of high-density through holes in ultra-thin substrate

Abstract

The invention relates to the field of intelligent detection, and provides a process for laser drilling of hole deviation of high-density through holes in an ultrathin substrate, which adopts an image recognition and processing technology based on computer vision to extract drill hole distribution characteristics of a drill hole distribution detection image and a drill hole distribution design image and enhance grid significant characteristics. And intelligently obtaining a hole deviation identification result according to the drill hole distribution difference between the enhanced features of the drill hole distribution detection image and the drill hole distribution design image. Thus, finer aperture details can be captured, the accuracy and precision of high-density through hole deviation identification are improved, the influence of human factors is reduced, the consistency and reliability of detection are ensured, and the identification process is more automatic and efficient.

Description

Technical Field

[0001] The present application relates to the field of intelligent detection, and more specifically, to a process for laser drilling high-density through holes on an ultra-thin substrate. Background Art

[0002] As electronic products develop towards smaller, thinner and lighter, higher requirements are placed on the design and manufacture of circuit boards. Ultra-thin substrates are one of the key technologies to achieve thin and light electronic devices. They not only help reduce the overall size and weight of the equipment, but also improve the integration and performance of electronic products. In the production of ultra-thin substrates, laser drilling is usually used to form through holes. However, due to the extremely thin thickness of the substrate, which is usually only tens of microns, and the smaller and smaller apertures, this high-density aperture is usually between tens of microns and hundreds of microns, which makes the position of the through holes easily shifted. This shift can lead to serious quality problems such as poor conduction, which not only affects the reliability of the product, but also greatly reduces the production yield. Therefore, the identification of hole deviation is crucial to ensure product quality and performance.

[0003] Traditional hole deviation identification methods usually rely on manual inspection or simple automated machine vision systems in ultra-thin substrate laser drilling high-density through-hole processes. However, manual inspection is easily affected by factors such as operator fatigue and lack of concentration, which will lead to a decrease in the consistency and accuracy of the inspection results, and cannot meet the dual requirements of speed and precision in modern high-density circuit board manufacturing. In addition, traditional machine vision systems may lack sufficient resolution and algorithm support in ultra-thin substrate laser drilling high-density through-hole processes, making it difficult to accurately identify tiny hole deviations. In other words, such systems usually rely on ordinary optical imaging equipment with limited resolution, which is insufficient to capture the details of tiny apertures on ultra-thin substrates, especially when the hole spacing is extremely small, resulting in inaccurate inspection results.

[0004] Therefore, an optimized process for laser drilling of high-density through holes on ultra-thin substrates is desired. Summary of the invention

[0005] In view of the shortcomings of the prior art, the present application provides a process for laser drilling of high-density through holes on an ultra-thin substrate.

[0006] According to one aspect of the present application, a process for radium drilling of high-density through holes on an ultra-thin substrate is provided, which comprises: Provide ultra-thin substrate materials; Cleaning the surface of the ultra-thin substrate material to obtain a cleaned ultra-thin substrate material; Fixing the cleaned ultra-thin substrate material on a processing table, and using a visual system to accurately position the cleaned ultra-thin substrate material; Using high-precision laser equipment to perform laser drilling on the cleaned ultra-thin substrate material to obtain a drilled ultra-thin substrate material; An optical detection device is used to identify the hole deviation of the ultra-thin substrate material after drilling to obtain a hole deviation identification result, and the hole deviation identification result is used to indicate whether there is a hole deviation.

[0007] In combination with the first aspect of the present application, in a process for detecting hole deviation of high-density through holes in an ultra-thin substrate by laser drilling of the first aspect of the present application, an optical detection device is used to identify hole deviation of the ultra-thin substrate material after drilling to obtain a hole deviation identification result, and the hole deviation identification result is used to indicate whether there is hole deviation, including: obtaining a drilling distribution detection image of the ultra-thin substrate material after drilling collected by the optical detection device; extracting a drilling distribution design image from a database; inputting the drilling distribution detection image and the drilling distribution design image into a drilling distribution feature extractor to obtain a drilling distribution detection feature map and a drilling distribution design feature map; inputting the drilling distribution detection feature map and the drilling distribution design feature map into a feature gating enhancement module based on grid energy significance guidance to obtain a grid-granularity drilling distribution detection enhancement feature map and a grid-granularity drilling distribution design enhancement feature map; calculating a drilling distribution differential feature map between the grid-granularity drilling distribution detection enhancement feature map and the grid-granularity drilling distribution design enhancement feature map; and obtaining the hole deviation identification result based on the drilling distribution differential feature map.

[0008] This application has significant technical effects due to the adoption of the above technical solutions: The process of ultra-thin substrate laser drilling of high-density through-hole deviation provided in the present application adopts computer vision-based image recognition and processing technology to extract drilling distribution features and enhance grid significant features of drilling distribution detection images and drilling distribution design images, so as to intelligently obtain the hole deviation identification result based on the drilling distribution difference between the enhanced features of the drilling distribution detection image and the drilling distribution design image. In this way, finer aperture details can be captured, the accuracy and precision of high-density through-hole deviation identification are improved, and the influence of human factors is reduced, ensuring the consistency and reliability of detection, making the identification process more automated and efficient. BRIEF DESCRIPTION OF THE DRAWINGS

[0009] By describing the embodiments of the present application in more detail in conjunction with the accompanying drawings, the above and other purposes, features and advantages of the present application will become more apparent. The accompanying drawings are used to provide a further understanding of the embodiments of the present application and constitute a part of the specification. Together with the embodiments of the present application, they are used to explain the present application and do not constitute a limitation of the present application. In the accompanying drawings, the same reference numerals generally represent the same components or steps.

[0010] Figure 1The present invention is a flow chart of a process for laser drilling high-density through-holes on an ultra-thin substrate according to an embodiment of the present application.

[0011] Figure 2 In the process of radium drilling high-density through holes on ultra-thin substrates according to an embodiment of the present application, an optical detection device is used to identify the hole deviation of the ultra-thin substrate material after drilling to obtain a hole deviation identification result, and the hole deviation identification result is used to indicate whether there is a flow chart of hole deviation.

[0012] Figure 3 In the process of radium drilling high-density through holes on an ultra-thin substrate according to an embodiment of the present application, an optical detection device is used to identify the hole deviation of the ultra-thin substrate material after drilling to obtain a hole deviation identification result, and the hole deviation identification result is a data flow diagram for indicating whether there is a hole deviation.

[0013] Figure 4 In the process of radium drilling high-density through-holes on ultra-thin substrates according to an embodiment of the present application, the drilling distribution detection feature map and the drilling distribution design feature map are input into a feature gating enhancement module guided by grid energy significance to obtain a flowchart of a grid granularity drilling distribution detection enhancement feature map and a grid granularity drilling distribution design enhancement feature map. DETAILED DESCRIPTION

[0014] Below, the exemplary embodiments according to the present application will be described in detail with reference to the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all the embodiments of the present application, and it should be understood that the present application is not limited to the exemplary embodiments described here.

[0015] As electronic devices pursue thinner and lighter development trends, the design and manufacture of circuit boards face more stringent challenges. Ultra-thin substrates, as one of the key technologies to achieve thinner and lighter devices, play a significant role in improving the integration and performance of electronic products. In the manufacturing process of ultra-thin substrates, laser drilling technology is often used to form through holes. Since the thickness of the substrate is usually only tens of microns, coupled with the reduction of the aperture, the positioning accuracy of high-density through holes becomes particularly important. Any slight deviation in the hole position may lead to a decrease in conductivity, thereby affecting the reliability and production efficiency of the product.

[0016] Traditional hole deviation identification methods, whether relying on manual inspection or basic automated vision systems, have certain limitations. Manual inspection is susceptible to operator fatigue and distraction, making it difficult to maintain consistency and accuracy, and unable to meet the speed and precision requirements of high-density circuit board manufacturing. Traditional machine vision systems often have difficulty accurately identifying hole deviations when dealing with tiny apertures on ultra-thin substrates due to insufficient resolution and insufficient algorithm support.

[0017] Based on this, the present application proposes a process for radium drilling of high-density through holes on ultra-thin substrates. Figure 1 FIG. 1 is a flow chart of a process for laser drilling high-density through-holes on an ultra-thin substrate according to an embodiment of the present application. Figure 1 As shown, the process of laser drilling high-density through-hole deviation of an ultra-thin substrate according to an embodiment of the present application includes: S110, providing an ultra-thin substrate material; S120, cleaning the surface of the ultra-thin substrate material to obtain a cleaned ultra-thin substrate material; S130, fixing the cleaned ultra-thin substrate material on a processing table, and using a visual system to accurately position the cleaned ultra-thin substrate material; S140, using a high-precision laser device to laser drill the cleaned ultra-thin substrate material to obtain a drilled ultra-thin substrate material; S150, using an optical detection device to identify the hole deviation of the drilled ultra-thin substrate material to obtain a hole deviation identification result, and the hole deviation identification result is used to indicate whether there is a hole deviation.

[0018] In the process of ultra-thin substrate laser drilling of high-density through-hole deviation, it is first necessary to provide ultra-thin substrate materials. Ultra-thin substrate materials are generally extremely thin but have sufficient strength and durability. Next, the surface of the ultra-thin substrate material needs to be cleaned to remove impurities, oil stains and dust on the surface of the substrate material, which is conducive to improving the positioning accuracy of the visual system and the quality of laser drilling, and avoiding processing errors caused by surface contaminants. Next, the cleaned ultra-thin substrate material is fixed on the processing table and accurately positioned using the visual system. It should be understood that fixing the ultra-thin substrate material can ensure stability during the processing process, and accurate positioning is the prerequisite for achieving high-density through-hole drilling. Any positioning deviation may cause hole deviation, thereby affecting the conductive properties of the circuit board. Then, a high-precision laser device is used to laser drill the cleaned ultra-thin substrate material. Laser drilling technology is used to form high-density through holes on ultra-thin substrates. These through holes are the key to electrical connections between different layers on the circuit board. Finally, optical detection equipment is needed to identify the hole deviation of the ultra-thin substrate material after drilling. Hole deviation identification is a key step in quality control. It can timely detect and correct deviations in the drilling process and improve product yield and reliability.

[0019] Accordingly, in the process of using an optical inspection device to identify the hole deviation of the ultra-thin substrate material after drilling to obtain the hole deviation identification result, the technical concept of the present application is to obtain the drilling distribution detection image of the ultra-thin substrate material after drilling collected by the optical inspection device, and extract the drilling distribution design image from the database, and use computer vision-based image recognition and processing technology to extract the drilling distribution features and enhance the grid significant features of the drilling distribution detection image and the drilling distribution design image, so as to intelligently obtain the hole deviation identification result based on the drilling distribution difference between the enhanced features of the drilling distribution detection image and the drilling distribution design image. In this way, finer aperture details can be captured, the accuracy and precision of high-density through-hole hole deviation identification are improved, and the influence of human factors is reduced, ensuring the consistency and reliability of detection, making the identification process more automated and efficient.

[0020] Figure 2 In the process of radium drilling high-density through holes on ultra-thin substrates according to an embodiment of the present application, an optical detection device is used to identify the hole deviation of the ultra-thin substrate material after drilling to obtain a hole deviation identification result, and the hole deviation identification result is used to indicate whether there is a flow chart of hole deviation. Figure 3 In the process of drilling high-density through-holes on ultra-thin substrates according to the embodiment of the present application, an optical detection device is used to identify the hole deviation of the ultra-thin substrate material after drilling to obtain a hole deviation identification result, and the hole deviation identification result is used to indicate whether there is a hole deviation. Figure 2 and Figure 3 As shown, an optical detection device is used to identify the hole deviation of the ultra-thin substrate material after drilling to obtain a hole deviation identification result, and the hole deviation identification result is used to indicate whether there is a hole deviation, including: S151, obtaining a drilling distribution detection image of the ultra-thin substrate material after drilling collected by the optical detection device; S152, extracting a drilling distribution design image from a database; S153, inputting the drilling distribution detection image and the drilling distribution design image into a drilling distribution feature extractor to obtain a drilling distribution detection feature map and a drilling distribution design feature map; S154, inputting the drilling distribution detection feature map and the drilling distribution design feature map into a feature gating enhancement module based on grid energy significance guidance to obtain a grid-size drilling distribution detection enhancement feature map and a grid-size drilling distribution design enhancement feature map; S155, calculating a drilling distribution differential feature map between the grid-size drilling distribution detection enhancement feature map and the grid-size drilling distribution design enhancement feature map; S156, based on the drilling distribution differential feature map, obtaining the hole deviation identification result.

[0021] In step S151 and step S152, the drilling distribution detection image of the ultra-thin substrate material after drilling collected by the optical detection equipment is obtained, and the drilling distribution design image is extracted from the database. It should be understood that the drilling distribution detection image contains the actual position, size and distribution information of all the drill holes on the ultra-thin substrate, which reflects the physical state after actual processing. The drilling distribution design image contains the drilling layout information expected in the design stage, specifically including the ideal position, size and shape of the hole, the expected distance and arrangement between the holes, etc. By automatically analyzing the drilling distribution detection image and the drilling distribution design image, the deviation between the actual drilling and the design can be identified, and the hole deviation identification result can be obtained, which can greatly improve the detection speed and accuracy.

[0022] In step S153, the drilling distribution detection image and the drilling distribution design image are input into a drilling distribution feature extractor to obtain a drilling distribution detection feature map and a drilling distribution design feature map. Specifically, in an embodiment of the present application, the drilling distribution detection image and the drilling distribution design image are input into a drilling distribution feature extractor to obtain a drilling distribution detection feature map and a drilling distribution design feature map, including: inputting the drilling distribution detection image and the drilling distribution design image into a drilling distribution feature extractor based on a deep neural network model to obtain the drilling distribution detection feature map and the drilling distribution design feature map. The drilling distribution feature extractor based on a deep neural network model described in the present application is a drilling distribution feature extractor based on a hole convolutional neural network model. It should be understood that the drilling distribution detection image and the drilling distribution design image reflect key feature information about detecting and designing drilling distribution. Considering that the hole convolution encoding method in the hole convolutional neural network model can expand the receptive field without increasing the amount of calculation, this helps to capture the details of tiny apertures on ultra-thin substrates, and then helps to identify whether there is a hole deviation. Based on this, in the technical solution of the present application, the drilling distribution detection image and the drilling distribution design image are input into a drilling distribution feature extractor based on a hole convolutional neural network model to capture and mine the key drilling features in the detection image and the design image, respectively, to obtain a drilling distribution detection feature map and a drilling distribution design feature map.

[0023] In step S154, the drilling distribution detection feature map and the drilling distribution design feature map are input into a feature gating enhancement module guided by grid energy significance to obtain a grid-size drilling distribution detection enhancement feature map and a grid-size drilling distribution design enhancement feature map. Accordingly, considering that some areas or features in the drilling distribution detection feature map and the drilling distribution design feature map are more prominent or important than other parts, that is, different local drilling areas have different contributions and significance to the whole, such as holes located on key signal paths or holes connecting important components, and their position accuracy directly affects the functional performance of the circuit board. If these holes are offset, signal transmission may be distorted or interrupted, affecting the overall performance of the circuit board. Therefore, in order to enhance the module to highlight the drilling features of important areas and suppress unimportant features, the accuracy of hole deviation identification is improved. In the technical solution of the present application, the drilling distribution detection feature map and the drilling distribution design feature map are input into a feature gating enhancement module guided by grid energy significance to obtain a grid-size drilling distribution detection enhancement feature map and a grid-size drilling distribution design enhancement feature map. It is worth mentioning that the purpose of the feature gating enhancement module guided by grid energy saliency is to define the focus area by defining a grid and simulate the attention mechanism based on the energy saliency principle, so as to enhance the expressiveness of the feature map and improve the overall performance of the model. In other words, this method identifies and emphasizes the key areas in the image while suppressing those relatively unimportant parts, so that the model can focus more on the features that are more critical to the task, thereby improving the recognition accuracy and processing efficiency.

[0024] Specifically, taking the borehole distribution detection feature map as an example, first, the borehole distribution detection feature map is grid-divided to obtain a set of local feature maps for borehole distribution detection. It should be understood that by dividing the feature map into multiple grids, the features of each local area of ​​borehole distribution detection can be analyzed more carefully, so that local details can be better captured, especially for those key areas that have a significant impact on the overall performance, such as high-density hole areas or holes on key signal paths. Next, the energy significance descriptor of each local feature map is calculated to obtain a set of local energy significance descriptors for borehole distribution detection. In this way, the feature strength and importance in a certain area can be reflected, helping the system to distinguish which local features have a greater impact on the overall hole deviation detection result. Subsequently, the set of local energy significance descriptors is input into the local feature adaptive selector based on the gate function to obtain a set of local significant modulation weights for borehole distribution detection. That is, the gate function can selectively retain those features with higher significance and ignore those features that have less impact on the detection results, and through the adaptive selector, it can be ensured that those features that are most critical to hole deviation detection are prioritized. This can improve the accuracy of detection, reduce false positives and false negatives, and make hole bias identification more accurate and reliable. Then, each significant modulation weight in the set of significant modulation weights is used as a weight, and each local feature map in the set of local feature maps is weighted to obtain a set of enhanced local feature maps. That is, the essence of the weighted enhancement step is attention application, which can make those local features with higher significance be given greater weights, so that they are more prominent in the final feature map. Finally, the weighted feature map is subjected to feature aggregation in the manner of grid division, and the aggregated features are input into the feature uniform dispersion module based on the hole convolution layer to obtain the final grid granularity drilling distribution detection enhanced feature map. In particular, since the module mainly focuses on the details at the grid level, unnatural feature transitions may occur at the edge of the grid. In order to achieve smooth diffusion of features, the feature map after aggregation is processed by applying the hole convolution layer to reduce the abrupt changes in feature expression at the edge of the grid.

[0025] Specifically, Figure 4 In the process of drilling high-density through-holes on ultra-thin substrates according to the embodiment of the present application, the drilling distribution detection feature map and the drilling distribution design feature map are input into a feature gating enhancement module guided by grid energy significance to obtain a grid-size drilling distribution detection enhancement feature map and a grid-size drilling distribution design enhancement feature map. Figure 4As shown, the drilling distribution detection feature map and the drilling distribution design feature map are input into a feature gating enhancement module based on grid energy significance guidance to obtain a grid-sized drilling distribution detection enhancement feature map and a grid-sized drilling distribution design enhancement feature map, including: S1541, gridding the drilling distribution detection feature map to obtain a set of drilling distribution detection local feature maps; S1542, calculating the energy significance description factor of each drilling distribution detection local feature map in the set of drilling distribution detection local feature maps to obtain a set of drilling distribution detection local energy significance description factors; S1543, inputting the set of drilling distribution detection local energy significance description factors into a local feature adaptive selector based on a gating function to obtain to a set of local significant modulation weights for drilling distribution detection; S1544, taking each local significant modulation weight for drilling distribution detection in the set of local significant modulation weights for drilling distribution detection as a weight, weighting each local feature map for drilling distribution detection in the set of local feature maps for drilling distribution detection respectively to obtain a set of local enhanced feature maps for drilling distribution detection; S1545, performing feature aggregation on the set of local enhanced feature maps for drilling distribution detection according to the grid division method to obtain a local significant guided enhanced feature map for drilling distribution detection; S1546, inputting the local significant guided enhanced feature map for drilling distribution detection into a feature dispersion module based on a hole convolution layer to obtain the grid-granularity drilling distribution detection enhanced feature map.

[0026] More specifically, in an embodiment of the present application, the energy significance descriptor factors of each local feature map of borehole distribution detection in the set of local feature maps of borehole distribution detection are calculated to obtain a set of local energy significance descriptor factors of borehole distribution detection, including: extracting the maximum value and the minimum value of the local feature map of borehole distribution detection respectively to obtain the local feature maximum value and the local feature minimum value of borehole distribution detection; calculating the difference between the local feature maximum value and the local feature minimum value of borehole distribution detection to obtain the local feature difference value of borehole distribution detection; calculating the global mean and variance of the local feature map of borehole distribution detection respectively to obtain the local feature mean value and the local feature variance of borehole distribution detection; adding the value obtained by multiplying the local feature variance of borehole distribution detection by a constant of two and the local feature mean value of borehole distribution detection to a hyperparameter to obtain the local feature statistical modulation value of borehole distribution detection; dividing the local feature difference value of borehole distribution detection by the local feature statistical modulation value of borehole distribution detection to obtain the local energy significance descriptor factor of borehole distribution detection corresponding to the local feature map of borehole distribution detection.

[0027] More specifically, in an embodiment of the present application, the set of local energy significance descriptors for drilling distribution detection is input into a local feature adaptive selector based on a gating function to obtain a set of local significant modulation weights for drilling distribution detection, including: normalizing the set of local energy significance descriptors for drilling distribution detection to obtain a set of normalized local energy significance descriptors for drilling distribution detection; and inputting each normalized local energy significance descriptor for drilling distribution detection in the set of normalized local energy significance descriptors for drilling distribution detection into a gating function for masking to obtain a set of local significant modulation weights for drilling distribution detection.

[0028] More specifically, in an embodiment of the present application, the set of local energy significance description factors for borehole distribution detection is normalized to obtain a set of normalized local energy significance description factors for borehole distribution detection, including: calculating an exponential function with the natural constant e as the base and the negative of each local energy significance description factor for borehole distribution detection in the set of local energy significance description factors for borehole distribution detection as the exponent to obtain a set of local energy significance description exponential factors for borehole distribution detection; calculating the reciprocal of the sum of each local energy significance description exponential factor for borehole distribution detection in the set of local energy significance description exponential factors for borehole distribution detection and a constant one to obtain the set of normalized local energy significance description factors for borehole distribution detection.

[0029] In an embodiment of the present application, specifically, the drilling distribution detection feature map and the drilling distribution design feature map are input into a feature gating enhancement module based on grid energy significance guidance to obtain a grid-size drilling distribution detection enhancement feature map and a grid-size drilling distribution design enhancement feature map, including: inputting the drilling distribution detection feature map into a feature gating enhancement module based on grid energy significance guidance, and processing it with the following significant gating enhancement formula to obtain the grid-size drilling distribution detection enhancement feature map; wherein the significant gating enhancement formula is: in, is the drilling distribution detection feature map, is the meshing operation, are respectively the local feature maps of drilling distribution detection in the set of local feature maps of drilling distribution detection, The first local feature map in the set of local feature maps for drilling distribution detection The local feature map of drilling distribution detection, and Extract the maximum and minimum values ​​in the feature map respectively. and They are The mean and variance of is a hyperparameter, Indicates The local energy significance description factor of the drilling distribution detection corresponding to the local feature map of drilling distribution detection, For masking, It is The local significant modulation weight of the drilling distribution detection corresponding to the local feature map of drilling distribution detection is is a predetermined threshold, To perform feature aggregation operations on each weighted feature map, is the local significant guided enhancement feature map for drilling distribution detection, is the dilated convolution operation, is the grid granularity drilling distribution detection enhancement feature map. Similarly, the encoding method of the drilling distribution design feature map is also shown in the above formula. In step S155, a drilling distribution differential feature map between the grid granularity drilling distribution detection enhanced feature map and the grid granularity drilling distribution design enhanced feature map is calculated. Specifically, in an embodiment of the present application, the drilling distribution differential feature map between the grid granularity drilling distribution detection enhanced feature map and the grid granularity drilling distribution design enhanced feature map is calculated, including: calculating the position difference between the grid granularity drilling distribution detection enhanced feature map and the grid granularity drilling distribution design enhanced feature map to obtain the drilling distribution differential feature map. It should be understood that in order to identify and quantify the deviation between the actual drilling results and the design expectations, so as to highlight the differences between the actual drilling and the design, including position offset, size difference or shape deviation, etc., in the technical solution of the present application, the position difference between the grid granularity drilling distribution detection enhanced feature map and the grid granularity drilling distribution design enhanced feature map is calculated to obtain the drilling distribution differential feature map.

[0030] In step S156, the hole deviation identification result is obtained based on the borehole distribution differential feature map. Specifically, in an embodiment of the present application, the hole deviation identification result is obtained based on the borehole distribution differential feature map, including: inputting the borehole distribution differential feature map into a hole deviation identifier based on a classifier to obtain the hole deviation identification result. That is, the borehole distribution differential feature map obtained by performing differential calculation between the grid granularity borehole distribution detection enhancement feature map and the grid granularity borehole distribution design enhancement feature map is used for classification processing to intelligently obtain the hole deviation identification result. In this way, finer aperture details can be captured, the accuracy and precision of high-density through-hole hole deviation identification are improved, and the influence of human factors is reduced, ensuring the consistency and reliability of detection, making the identification process more automated and efficient.

[0031] In a preferred example, since the grid-granularity drilling distribution detection enhancement feature map and the grid-granularity drilling distribution design enhancement feature map respectively represent the local spatial enhanced image semantic features of the drilling distribution detection image and the drilling distribution design image based on the image semantic feature grid distribution energy significance, when calculating the positional difference therebetween to obtain the drilling distribution differential feature map, the drilling distribution differential feature map will also have the image semantic feature differential aggregation offset caused by the inconsistency of the local spatial energy enhancement of the image semantic features under the semantic distribution of the source image, thereby expecting to improve the comprehensibility of the image semantic feature differential aggregation of the drilling distribution differential feature map, thereby improving the accuracy of the hole deviation identification result obtained by inputting the drilling distribution differential feature map into the classifier-based hole deviation identifier.

[0032] Therefore, when the borehole distribution differential feature map is input into the classifier-based hole deviation identifier, the present application optimizes the borehole distribution differential feature map, including the steps of: expanding the borehole distribution differential feature map into a borehole distribution differential feature vector; subtracting the 0 norm of the borehole distribution differential feature vector from the length of the borehole distribution differential feature vector to obtain a borehole distribution differential isolated representation value; calculating the logarithm of the square of the borehole distribution differential isolated representation value and the sum of the borehole distribution differential isolated representation value with base 2 to obtain a borehole distribution differential information order value; calculating the borehole distribution differential isolated representation value with each eigenvalue of the borehole distribution differential feature vector as the base. The difference of the indication value minus one is an exponential power function, and is point-multiplied with the borehole distribution differential information order value to obtain the borehole distribution differential pilot; the borehole distribution differential feature vector is point-multiplied with the difference of the borehole distribution differential isolated representation value minus one, and then point-multiplied with the reciprocal of the borehole distribution differential isolated representation value to obtain the borehole distribution differential field constraint vector; an exponential function of each eigenvalue of the borehole distribution differential field constraint vector is calculated with the natural constant as the base to obtain the borehole distribution differential field bias vector; the borehole distribution differential pilot vector is point-added with the borehole distribution differential field bias vector to obtain the optimized borehole distribution differential feature vector.

[0033] The optimization process of the drilling distribution differential feature vector is expressed as: in, represents the differential eigenvector of the drilling distribution, represents the length of the borehole distribution differential eigenvector, represents the zero norm of a vector, represents the isolated representation value of the borehole distribution difference, It means point multiplication by position. represents the logarithm with base 2, Indicates adding by position point. represents the natural exponential function, To calculate a power function with each eigenvalue of the borehole distribution differential eigenvector as the base and the difference of the borehole distribution differential isolated representation value minus one as the exponent, represents the optimized drilling distribution differential eigenvector.

[0034] Therefore, the high-dimensional feature manifold of the borehole distribution differential feature map is represented by the feature value of the feature set as the vector field of the aggregation dimension, and the overlapping value of the vector field of the borehole distribution differential feature vector after the borehole distribution differential feature map is expanded at the isolated zero position is used as the order information to fix the local position of the feature value of its feature set, and add the bias of the reversibility of the feature regression distribution field of the borehole distribution differential feature vector as a reward to achieve the mapping target tracking of the regression distribution of the borehole distribution differential feature vector for the feature value position, so that the feature set of the borehole distribution differential feature map can perceive the mapping migration to the aggregate distribution, thereby improving the accuracy of the hole deviation recognition result obtained by inputting it into the classifier-based hole deviation identifier by improving the comprehensibility of the image semantic feature differential aggregation mapping of the borehole distribution differential feature map. In this way, finer aperture details can be captured, the accuracy and precision of high-density through-hole hole deviation recognition are improved, and the influence of human factors is reduced, ensuring the consistency and reliability of detection, making the recognition process more automated and efficient.

[0035] In summary, the process of ultra-thin substrate laser drilling of high-density through-hole deviation based on the embodiment of the present application is explained, which uses image recognition and processing technology based on computer vision to extract drilling distribution features and enhance grid significant features of drilling distribution detection images and drilling distribution design images, so as to intelligently obtain the hole deviation identification result based on the drilling distribution difference between the enhanced features of the drilling distribution detection image and the drilling distribution design image. In this way, finer aperture details can be captured, the accuracy and precision of high-density through-hole deviation identification are improved, and the influence of human factors is reduced, ensuring the consistency and reliability of detection, making the identification process more automated and efficient.

[0036] The basic principles of the present application are described above in conjunction with specific embodiments. However, it should be noted that the advantages, strengths, effects, etc. mentioned in the present application are only examples and not limitations, and it cannot be considered that these advantages, strengths, effects, etc. are required by each embodiment of the present application. In addition, the specific details of the above application are only for the purpose of illustration and ease of understanding, rather than limitation, and the above details do not limit the present application to the use of the above specific details to be implemented.

Claims

1. A process for laser drilling high-density through-holes on ultra-thin substrates, characterized in that: include: Provide ultra-thin substrate materials; Cleaning the surface of the ultra-thin substrate material to obtain a cleaned ultra-thin substrate material; Fixing the cleaned ultra-thin substrate material on a processing table, and using a visual system to accurately position the cleaned ultra-thin substrate material; Using high-precision laser equipment to perform laser drilling on the cleaned ultra-thin substrate material to obtain a drilled ultra-thin substrate material; An optical detection device is used to identify the hole deviation of the ultra-thin substrate material after drilling to obtain a hole deviation identification result, and the hole deviation identification result is used to indicate whether there is a hole deviation.

2. The process for radium drilling high-density through-holes on ultra-thin substrates according to claim 1, characterized in that: Using an optical detection device to identify the hole deviation of the ultra-thin substrate material after drilling to obtain a hole deviation identification result, wherein the hole deviation identification result is used to indicate whether there is a hole deviation, including: Acquire a drilling distribution detection image of the ultra-thin substrate material after drilling collected by the optical detection device; extracting a drilling distribution design image from a database; Inputting the drilling distribution detection image and the drilling distribution design image into a drilling distribution feature extractor to obtain a drilling distribution detection feature map and a drilling distribution design feature map; Inputting the drilling distribution detection feature map and the drilling distribution design feature map into a feature gating enhancement module guided by grid energy significance to obtain a grid-size drilling distribution detection enhancement feature map and a grid-size drilling distribution design enhancement feature map; Calculating a drilling distribution differential feature map between the grid granularity drilling distribution detection enhancement feature map and the grid granularity drilling distribution design enhancement feature map; Based on the borehole distribution differential feature map, the hole deviation identification result is obtained.

3. The process for radium drilling high-density through-holes on ultra-thin substrates according to claim 2 is characterized in that: The drilling distribution detection image and the drilling distribution design image are input into a drilling distribution feature extractor to obtain a drilling distribution detection feature map and a drilling distribution design feature map, including: inputting the drilling distribution detection image and the drilling distribution design image into a drilling distribution feature extractor based on a deep neural network model to obtain the drilling distribution detection feature map and the drilling distribution design feature map.

4. The process for radium drilling of high-density through holes on ultra-thin substrates according to claim 3 is characterized in that: The drilling distribution feature extractor based on the deep neural network model is a drilling distribution feature extractor based on the void convolutional neural network model.

5. The process for radium drilling of high-density through holes on ultra-thin substrates according to claim 4 is characterized in that: Inputting the drilling distribution detection feature map and the drilling distribution design feature map into a feature gating enhancement module based on grid energy significance guidance to obtain a grid-size drilling distribution detection enhancement feature map and a grid-size drilling distribution design enhancement feature map, including: Performing grid division on the borehole distribution detection feature map to obtain a set of local feature maps of borehole distribution detection; Calculating the energy significance description factor of each local feature map of borehole distribution detection in the set of local feature maps of borehole distribution detection to obtain a set of local energy significance description factors of borehole distribution detection; Inputting the set of local energy significance descriptors for drilling distribution detection into a local feature adaptive selector based on a gating function to obtain a set of local significant modulation weights for drilling distribution detection; Taking each local significant modulation weight for drilling distribution detection in the set of local significant modulation weights for drilling distribution detection as a weight, weighting each local feature map for drilling distribution detection in the set of local feature maps for drilling distribution detection respectively to obtain a set of local enhanced feature maps for drilling distribution detection; Performing feature aggregation on the set of the local enhanced feature maps for borehole distribution detection according to the grid division method to obtain a local significant guided enhanced feature map for borehole distribution detection; The drilling distribution detection local significant guided enhanced feature map is input into the feature dispersion module based on the hole convolution layer to obtain the grid-granularity drilling distribution detection enhanced feature map.

6. The process for radium drilling of high-density through holes on ultra-thin substrates according to claim 5, characterized in that: Calculating the energy significance description factor of each local feature map of the borehole distribution detection in the set of local feature maps of the borehole distribution detection to obtain a set of local energy significance description factors of the borehole distribution detection, including: Respectively extracting the maximum value and the minimum value of the local feature map of the drilling distribution detection to obtain the local feature maximum value and the local feature minimum value of the drilling distribution detection; Calculating the difference between the maximum value of the local feature of the drilling distribution detection and the minimum value of the local feature of the drilling distribution detection to obtain a local feature difference value of the drilling distribution detection; Respectively calculating the global mean and variance of the local feature map of the borehole distribution detection to obtain the local feature mean of the borehole distribution detection and the local feature variance of the borehole distribution detection; The value obtained by multiplying the local feature variance of the borehole distribution detection by a constant of two and the mean value of the local feature of the borehole distribution detection are added to the hyperparameter to obtain a local feature statistical modulation value of the borehole distribution detection; The local feature difference value of the drilling distribution detection is divided by the local feature statistical modulation value of the drilling distribution detection to obtain a local energy significance description factor of the drilling distribution detection corresponding to the local feature map of the drilling distribution detection.

7. The process for radium drilling of high-density through holes on ultra-thin substrates according to claim 6, characterized in that: The set of local energy significance descriptors for drilling distribution detection is input into a local feature adaptive selector based on a gating function to obtain a set of local significant modulation weights for drilling distribution detection, including: Normalizing the set of local energy significance description factors for borehole distribution detection to obtain a set of normalized local energy significance description factors for borehole distribution detection; Each normalized local energy significance description factor of drilling distribution detection in the set of normalized local energy significance description factors of drilling distribution detection is input into a gating function for masking processing to obtain a set of local significance modulation weights of drilling distribution detection.

8. The process for radium drilling of high-density through holes on ultra-thin substrates according to claim 7, characterized in that: The set of local energy significance description factors for borehole distribution detection is normalized to obtain a set of normalized local energy significance description factors for borehole distribution detection, including: Calculate an exponential function with the natural constant e as the base and the negative number of each borehole distribution detection local energy significance description factor in the set of borehole distribution detection local energy significance description factors as the exponent to obtain a set of borehole distribution detection local energy significance description exponential factors; The reciprocal of the sum of each borehole distribution detection local energy significance description index factor in the set of borehole distribution detection local energy significance description index factors and a constant one is calculated to obtain the set of normalized borehole distribution detection local energy significance description factors.

9. The process for radium drilling of high-density through holes on ultra-thin substrates according to claim 8, characterized in that: Calculating a drilling distribution differential feature map between the grid granularity drilling distribution detection enhanced feature map and the grid granularity drilling distribution design enhanced feature map, including: calculating the positional difference between the grid granularity drilling distribution detection enhanced feature map and the grid granularity drilling distribution design enhanced feature map to obtain the drilling distribution differential feature map.

10. The process for radium drilling high-density through-holes on ultra-thin substrates according to claim 9, characterized in that: Based on the borehole distribution differential feature map, the borehole deviation identification result is obtained, including: inputting the borehole distribution differential feature map into a classifier-based borehole deviation identifier to obtain the borehole deviation identification result.

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