A porous substrate and a method for fabricating multi-hole vias based on dynamic partitioning

Abstract

This invention belongs to the field of through-hole processing technology for substrates, specifically relating to a porous substrate and a multi-through-hole processing method based on dynamic partitioning. The substrate processing area is divided into multiple grid cells, a spatial distribution density map is constructed, and density ridges are obtained based on density gradients as partition boundaries, achieving efficient dynamic partitioning. Multiple rows are merged based on the hole coordinate spacing, and all hole coordinates are connected to form processing paths for sub-processing areas, achieving stroke optimization within the partition. By constructing an objective function, the processing sequence of sub-processing areas is optimized, ensuring that the path length is minimized, further improving processing efficiency and reducing processing costs. This invention optimizes multi-row through-hole processing into a single-row continuous processing path through a dynamic partitioning strategy and a multi-row merging algorithm, significantly reducing laser head idle travel time and the number of inter-row switching, achieving efficient processing of large-scale through-holes on substrates.

Description

Technical Field

[0001] This invention belongs to the field of substrate through-hole processing technology, specifically relating to a porous substrate and a multi-hole processing method based on dynamic partitioning. Background Technology

[0002] In the manufacturing process of large-scale, high-density substrates, through-hole processing technology is a core component for achieving interlayer electrical interconnection. With the miniaturization and high performance of electronic devices, the number of through-holes on substrates has increased dramatically, often reaching tens of thousands to hundreds of thousands, with increasingly smaller apertures and highly complex positional distributions. Against this backdrop, efficient and precise machining path planning technology has become one of the key bottlenecks for improving overall production efficiency and ensuring hole position accuracy and consistency. The core challenge lies in how to minimize the total machining stroke, shorten the single-board machining cycle, balance tool / equipment wear, and effectively avoid interference risks from pre-existing structures within the substrate or adjacent holes, while meeting stringent requirements for aperture accuracy, hole wall quality, and positional tolerances, through intelligent tool or beam movement path optimization strategies.

[0003] Existing large-scale through-hole processing methods, such as line-by-line scanning or raster-based path methods, require frequent switching of processing rows, resulting in a high proportion of idle laser head travel and redundant inter-row jumps. Static partitioning methods cannot adapt to scenarios with uneven hole density distribution, leading to suboptimal paths within partitions. Traditional manual or simple sequential planning methods can no longer meet the mass production requirements of modern high-density substrates. There is an urgent need for advanced path planning methods to achieve intelligent decision-making on processing paths under large-scale, multi-constraint conditions, thereby reducing production costs, improving equipment utilization, and increasing final product yield. Summary of the Invention

[0004] The technical problem to be solved by the present invention is to overcome the defects of the existing large-scale through-hole processing method, which has low processing efficiency due to the large idle travel time of the laser head and the number of inter-row switching times. The invention provides a multi-hole substrate and a multi-through-hole processing method based on dynamic partitioning.

[0005] A method for fabricating multi-hole vias based on dynamic partitioning includes the following steps:

[0006] Obtain the set of hole coordinates for multi-hole machining on the substrate;

[0007] The substrate processing area is divided into multiple grid units, and a spatial distribution density map of the substrate processing area is constructed based on the hole coordinate set. Dense and sparse areas in the spatial distribution density map are then identified.

[0008] The density gradient is calculated from the spatial distribution density map, and the density ridge line is obtained based on the density gradient. The density ridge line is used as the partition boundary to divide the processing area of ​​the substrate into multiple sub-processing areas.

[0009] The hole coordinates within the processing area are divided into multiple hole coordinate rows according to the vertical coordinate. The hole coordinate spacing between two adjacent hole coordinate rows is calculated. When the hole coordinate spacing is less than a preset threshold, the two hole coordinate rows are merged.

[0010] Within the sub-processing area, the hole coordinates in odd-numbered rows are arranged in ascending order, and the hole coordinates in even-numbered rows are arranged in descending order. From the first row to the last row, all hole coordinates are connected sequentially to form the processing path of the sub-processing area.

[0011] Using the center of the sub-processing area as a node, construct an objective function, which includes the path length connecting all nodes. Solve the objective function to obtain the processing order of the sub-processing area.

[0012] The sub-processing areas are processed sequentially according to the processing order of the sub-processing areas, and each through hole within the sub-processing area is processed sequentially based on the processing path of the sub-processing area.

[0013] Furthermore, constructing a spatial distribution density map of the processing area of ​​the substrate based on the hole coordinate set includes the following steps: for each grid cell, count the number of hole coordinates falling into the grid cell, and the hole density of the grid cell is the number of hole coordinates / grid cell area. Based on the hole density of each grid cell, construct a spatial distribution density map of the processing area of ​​the substrate.

[0014] Furthermore, constructing a spatial distribution density map of the processing area of ​​the substrate based on the hole coordinate set includes the following steps: using each hole coordinate as the center, calculating its influence on each grid cell using a kernel function, the closer the hole coordinate is to the grid cell, the greater the influence; adding the influence of all the hole coordinates on the grid cell, which is the hole density of the grid cell, and constructing a spatial distribution density map of the processing area of ​​the substrate based on the hole density of each grid cell.

[0015] Furthermore, based on the pore density of each grid cell, a spatial distribution density map is drawn using color to represent the magnitude of the pore density. The grid cells are arranged in order of pore density, and grid cells with pore density ranking greater than a preset quantile are defined as dense regions, while grid cells with pore density less than a preset quantile are defined as sparse regions. Dense regions and sparse regions in the spatial distribution density map are then identified.

[0016] Furthermore, after dividing the substrate processing area into multiple sub-processing areas, the process also includes sub-processing area optimization, comprising the following steps:

[0017] Calculate the blank space ratio, hole distribution compactness, and density uniformity of each of the sub-processing regions;

[0018] If the area of ​​the sub-processing region is less than a preset threshold and the number of hole coordinates is less than a preset threshold, then the sub-processing region is merged into the adjacent sub-processing region with the closest hole density. If the proportion of blank space in the merged partition decreases and the compactness of the hole distribution increases, then the merged partition is retained; otherwise, the merged partition is rolled back.

[0019] When the density uniformity of the sub-processing area is greater than a preset threshold and the proportion of blank space is greater than a preset threshold, the sub-processing area is divided based on the density gradient ridge. If the proportion of blank space in the merged partition decreases and the compactness of the hole distribution increases, the divided partition is retained; otherwise, the divided partition is reverted.

[0020] When the proportion of blank space and the compactness of hole distribution in all the sub-processing areas are within a preset range, the sub-processing area optimization ends.

[0021] When the changes in the proportion of blank space and the compactness of hole distribution in multiple iterations are less than a preset threshold, the sub-processing region optimization ends.

[0022] Furthermore, the calculation method for the blank space ratio is as follows: blank space ratio = (total area of ​​sub-processing area − hole coverage area) / total area of ​​sub-processing area.

[0023] Furthermore, the calculation method for the blank space ratio is as follows: the hole distribution compactness is the lateral and longitudinal standard deviations of the hole coordinates within the sub-processing area:

[0024] .

[0025] Furthermore, the hole coordinate spacing between two adjacent hole coordinate rows within the sub-processing area is calculated. When the hole coordinate spacing is less than a preset threshold, the two hole coordinate rows are merged, including the following method steps:

[0026] For adjacent hole coordinate rows, calculate the average ordinate of the hole coordinates in the two hole coordinate rows respectively. If the difference between the corresponding average ordinates is less than a preset threshold, merge the two hole coordinate rows and update the abscissa of the hole coordinates in the two hole coordinate rows to the average abscissa.

[0027] Repeat the above steps until the hole coordinates of all adjacent rows are greater than the preset threshold.

[0028] Furthermore, taking the center of the sub-processing region as the node, an objective function is constructed. The objective function includes the path length connecting all nodes and also includes the following method steps:

[0029] A heat load weight is assigned to each of the sub-processing areas. The temperature of each sub-processing area is monitored in real time by a temperature sensor. When the temperature of a sub-processing area rises, the heat load weight is reduced; when the temperature of a sub-processing area falls, the heat load weight is increased. The heat load weight is then incorporated into the objective function.

[0030] A porous substrate, wherein the porous substrate is formed by the above-described multi-hole processing method.

[0031] Beneficial Effects: This invention discloses a multi-via processing method and a porous substrate based on dynamic partitioning. The substrate processing area is divided into multiple grid cells, a spatial distribution density map is constructed, and density ridges are obtained based on density gradients as partition boundaries, achieving efficient dynamic partitioning. Multiple rows are merged based on the spacing between hole coordinates, and all hole coordinates are connected to form processing paths for sub-processing areas, achieving stroke optimization within the partition. By constructing an objective function, the processing sequence of sub-processing areas is optimized, ensuring that the path length is minimized, further improving processing efficiency and reducing processing costs. This invention optimizes multi-row vias into a single-row continuous processing path through a dynamic partitioning strategy and a multi-row merging algorithm, significantly reducing laser head idle travel time and the number of inter-row switching times, achieving efficient processing of large-scale vias on the substrate. Attached Figure Description

[0032] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0033] Figure 1 This is a schematic flowchart illustrating the main method steps of the present invention. Detailed Implementation

[0034] To make the above-mentioned objectives, features, and advantages of this application more apparent and understandable, the specific embodiments of this application are described in detail below with reference to the accompanying drawings. Many specific details are set forth in the following description to provide a thorough understanding of this application. However, this application can be implemented in many other ways different from those described herein, and those skilled in the art can make similar modifications without departing from the spirit of this application. Therefore, this application is not limited to the specific embodiments disclosed below.

[0035] In the description of this application, it should be understood that the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Therefore, a feature defined as "first" or "second" may explicitly or implicitly include at least one of that feature. In the description of this application, "multiple" means at least two, such as two, three, etc., unless otherwise explicitly specified.

[0036] It should be noted that when an element is referred to as being "fixed to" or "set on" another element, it can be directly on the other element or there may be an intervening element. When an element is considered to be "connected to" another element, it can be directly connected to the other element or there may be an intervening element. The terms "vertical," "horizontal," "upper," "lower," "left," "right," and similar expressions used herein are for illustrative purposes only and do not represent the only possible implementation.

[0037] Example 1:

[0038] Reference Figure 1 As shown, this embodiment provides a multi-hole processing method based on dynamic partitioning, including the following method steps:

[0039] Step S1: Obtain the hole coordinate set for the multi-hole machining of the substrate;

[0040] Step S2: Divide the substrate processing area into multiple grid units, construct a spatial distribution density map of the substrate processing area based on the hole coordinate set, and identify the dense and sparse areas in the spatial distribution density map;

[0041] Step S3: Calculate the density gradient of the spatial distribution density map, obtain the density ridge line based on the density gradient, and use the density ridge line as the partition boundary to divide the processing area of ​​the substrate into multiple sub-processing areas;

[0042] Step S4: Divide the hole coordinates in the sub-processing area into multiple hole coordinate rows according to the vertical coordinate, calculate the hole coordinate spacing between two adjacent hole coordinate rows, and merge two hole coordinate rows when the hole coordinate spacing is less than a preset threshold.

[0043] Step S5: Within the sub-processing area, arrange the hole coordinates in odd-numbered rows in ascending order and the hole coordinates in even-numbered rows in descending order. Connect all hole coordinates sequentially from the first row to the last row to form the processing path of the sub-processing area.

[0044] Step S6: Using the center of the sub-processing area as the node, construct an objective function, which includes the path length connecting all nodes. Solve the objective function to obtain the processing order of the sub-processing area.

[0045] Step S7: Process the sub-processing areas sequentially according to the processing order of the sub-processing areas, and process each through hole in the sub-processing area sequentially based on the processing path of the sub-processing area.

[0046] This embodiment provides a porous substrate and a multi-via processing method based on dynamic partitioning. The substrate processing area is divided into multiple grid cells, a spatial distribution density map is constructed, and density ridges are obtained based on density gradients as partition boundaries, achieving efficient dynamic partitioning. Multiple rows are merged based on the spacing between hole coordinates, and all hole coordinates are connected to form processing paths for sub-processing areas, achieving stroke optimization within each partition. By constructing an objective function, the processing sequence of sub-processing areas is optimized, ensuring that the path length is minimized, further improving processing efficiency and reducing processing costs. This invention optimizes multi-row vias into a single-row continuous processing path through a dynamic partitioning strategy and a multi-row merging algorithm, significantly reducing laser head idle travel time and the number of inter-row switching times, achieving efficient processing of large-scale vias on the substrate.

[0047] Specifically, step S2 also includes the following method steps: for each grid cell, count the number of hole coordinates falling into the grid cell, the hole density of the grid cell is the number of hole coordinates / grid cell area, and construct a spatial distribution density map of the processing area of ​​the substrate based on the hole density of each grid cell.

[0048] Step S2 also includes the following method steps: using each hole coordinate as the center, a kernel function is used to calculate its influence on each grid cell, and the closer the hole coordinate is to the grid cell, the greater the influence; the influence of all the hole coordinates on the grid cell is added together to obtain the hole density of the grid cell, and a spatial distribution density map of the processing area of ​​the substrate is constructed based on the hole density of each grid cell.

[0049] As a further improvement to this embodiment, a spatial distribution density map is drawn based on the pore density of each grid cell, using color to represent the magnitude of the pore density. The grid cells are arranged in order of pore density, and grid cells with pore density greater than a preset quantile are defined as dense regions, while grid cells with pore density less than a preset quantile are defined as sparse regions. Dense regions and sparse regions in the spatial distribution density map are then identified.

[0050] After dividing the substrate processing area into multiple sub-processing areas, the process also includes sub-processing area optimization, which includes the following steps:

[0051] Calculate the blank space ratio, hole distribution compactness, and density uniformity of each of the sub-processing regions;

[0052] If the area of ​​the sub-processing region is less than a preset threshold and the number of hole coordinates is less than a preset threshold, then the sub-processing region is merged into the adjacent sub-processing region with the closest hole density. If the proportion of blank space in the merged partition decreases and the compactness of the hole distribution increases, then the merged partition is retained; otherwise, the merged partition is rolled back.

[0053] When the density uniformity of the sub-processing area is greater than a preset threshold and the proportion of blank space is greater than a preset threshold, the sub-processing area is divided based on the density gradient ridge. If the proportion of blank space in the merged partition decreases and the compactness of the hole distribution increases, the divided partition is retained; otherwise, the divided partition is reverted.

[0054] When the proportion of blank space and the compactness of hole distribution in all the sub-processing areas are within a preset range, the sub-processing area optimization ends.

[0055] When the changes in the proportion of blank space and the compactness of hole distribution in multiple iterations are less than a preset threshold, the sub-processing region optimization ends.

[0056] The blank space ratio is calculated as follows: Blank space ratio = (Total area of ​​sub-processing area − Hole coverage area) / Total area of ​​sub-processing area.

[0057] The calculation method for the blank space ratio is as follows: the hole distribution compactness is the lateral and longitudinal standard deviations of the hole coordinates within the sub-processing area.

[0058] .

[0059] The method for calculating density uniformity is as follows:

[0060] Coefficient of variation (CV) of density values ​​within the sub-processing area: CV = standard deviation of density / mean density.

[0061] For each subregion i, calculate its density ρi:

[0062] ρi = Subregion area / Number of holes in the subregion (number of holes / unit area).

[0063] Calculate the mean and standard deviation:

[0064] Mean density (μ): ;

[0065] Density standard deviation (σ): .

[0066] In step S3, the gradient of the density field is obtained by calculating the density change rate of each grid point. By calculating the gradient of the discretized grid points in the continuous space covered by the density field, the trend of density change in the space is quantified.

[0067] In step S4, the hole coordinate spacing between two adjacent hole coordinate rows within the sub-processing area is calculated. When the hole coordinate spacing is less than a preset threshold, the two hole coordinate rows are merged. This includes the following steps:

[0068] For adjacent hole coordinate rows, calculate the average ordinate of the hole coordinates in the two hole coordinate rows respectively. If the difference between the corresponding average ordinates is less than a preset threshold, merge the two hole coordinate rows and update the abscissa of the hole coordinates in the two hole coordinate rows to the average abscissa.

[0069] Repeat the above steps until the hole coordinates of all adjacent rows are greater than the preset threshold.

[0070] In step S6, a target function is constructed using the center of the sub-processing region as the node. The target function includes the path length connecting all nodes and also includes the following method steps:

[0071] The distance between nodes is represented by Euclidean distance:

[0072] ;

[0073] in, and Let i represent the x-coordinate and y-coordinate of point i, respectively. and Let x and y represent the x and y coordinates of point i, respectively.

[0074] A heat load weight is assigned to each of the sub-processing areas. The temperature of each sub-processing area is monitored in real time by a temperature sensor. When the temperature of a sub-processing area rises, the heat load weight is reduced; when the temperature of a sub-processing area falls, the heat load weight is increased. The heat load weight is then incorporated into the objective function.

[0075] In this embodiment, the objective function is to minimize the weighted total cost, where cost = distance cost + heat load cost, specifically expressed as:

[0076] ;

[0077] in, Represents a node arrive distance, Represents the target node Heat load weight (such as number of holes per unit area, power density, etc.). This represents the heat load weighting coefficient, in this embodiment, =0.5 indicates that the heat load weight accounts for 50% of the total cost.

[0078] Solution methods include:

[0079] Initialization: Select the starting partition Marked as "visited", path Current heat accumulation ;

[0080] For the current partition c, calculate its weighted cost with all unvisited partitions u, expressed as:

[0081] ;

[0082] in, This represents the heat accumulation coefficient, which makes the heat load have a greater impact when accumulated. In this embodiment... =0.1, This represents the hot accumulation of the current path, initially set to 0, and updated after each partition access:

[0083] ;

[0084] Termination condition: After all partitions have been visited, return to the starting point to form a closed loop.

[0085] Example 2:

[0086] This embodiment provides a porous substrate, which is formed by the multi-hole processing method described in Embodiment 1.

[0087] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0088] The embodiments described above are merely illustrative of several implementation methods of this application, and while the descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the patent application. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of this application, and these all fall within the protection scope of this application. Therefore, the protection scope of this patent application should be determined by the appended claims.

Claims

1. A method for processing multi-hole holes based on dynamic partitioning, characterized in that, The following steps are included: Obtain the set of hole coordinates for multi-hole machining on the substrate; The substrate processing area is divided into multiple grid units, and a spatial distribution density map of the substrate processing area is constructed based on the hole coordinate set. Dense and sparse areas in the spatial distribution density map are then identified. The density gradient is calculated from the spatial distribution density map, and the density ridge line is obtained based on the density gradient. The density ridge line is used as the partition boundary to divide the processing area of ​​the substrate into multiple sub-processing areas. The hole coordinates within the sub-processing area are divided into multiple hole coordinate rows according to their vertical coordinates. The hole coordinate spacing between two adjacent hole coordinate rows is calculated. When the hole coordinate spacing is less than a preset threshold, the two hole coordinate rows are merged. For adjacent hole coordinate rows, the average vertical coordinate of the hole coordinates in the two hole coordinate rows is calculated respectively. If the difference between the corresponding average vertical coordinates is less than a preset threshold, the two hole coordinate rows are merged, and the horizontal coordinates of the hole coordinates in the two hole coordinate rows are updated to the average horizontal coordinate. The above steps are repeated until the hole coordinates of all adjacent rows are greater than the preset threshold. Within the sub-processing area, the hole coordinates in odd-numbered rows are arranged in ascending order, and the hole coordinates in even-numbered rows are arranged in descending order. From the first row to the last row, all hole coordinates are connected sequentially to form the processing path of the sub-processing area. Using the center of the sub-processing area as a node, construct an objective function, which includes the path length connecting all nodes. Solve the objective function to obtain the processing order of the sub-processing area. The sub-processing areas are processed sequentially according to the processing order of the sub-processing areas, and each through hole within the sub-processing area is processed sequentially based on the processing path of the sub-processing area.

2. The multi-hole processing method based on dynamic partitioning according to claim 1, characterized in that, Constructing a spatial distribution density map of the processing area of ​​a substrate based on a set of hole coordinates includes the following steps: For each grid cell, count the number of hole coordinates falling into the grid cell, and the hole density of the grid cell is the number of hole coordinates / grid cell area. Construct a spatial distribution density map of the processing area of ​​the substrate based on the hole density of each grid cell.

3. The multi-hole processing method based on dynamic partitioning according to claim 1, characterized in that, Constructing a spatial distribution density map of the processing area of ​​a substrate based on a set of hole coordinates includes the following steps: using each hole coordinate as the center, a kernel function is used to calculate its influence on each grid cell; the closer the hole coordinate is to the grid cell, the greater the influence; the influence of all the hole coordinates on the grid cell is added together to obtain the hole density of the grid cell; and a spatial distribution density map of the processing area of ​​the substrate is constructed based on the hole density of each grid cell.

4. The multi-hole processing method based on dynamic partitioning according to claim 1, characterized in that, Based on the pore density of each grid cell, a spatial distribution density map is drawn using color to represent the magnitude of the pore density. The grid cells are arranged in order of pore density, and grid cells with pore density greater than a preset quantile are defined as dense regions, while grid cells with pore density less than a preset quantile are defined as sparse regions. Dense and sparse regions in the spatial distribution density map are identified.

5. The multi-hole processing method based on dynamic partitioning according to claim 1, characterized in that, After dividing the substrate processing area into multiple sub-processing areas, the process also includes sub-processing area optimization, which includes the following steps: Calculate the blank space ratio, hole distribution compactness, and density uniformity of each of the sub-processing regions; If the area of ​​the sub-processing region is less than a preset threshold and the number of hole coordinates is less than a preset threshold, then the sub-processing region is merged into the adjacent sub-processing region with the closest hole density. If the proportion of blank space in the merged partition decreases and the compactness of the hole distribution increases, then the merged partition is retained; otherwise, the merged partition is rolled back. When the density uniformity of the sub-processing area is greater than a preset threshold and the proportion of blank space is greater than a preset threshold, the sub-processing area is divided based on the density gradient ridge. If the proportion of blank space in the merged partition decreases and the compactness of the hole distribution increases, the divided partition is retained; otherwise, the divided partition is reverted. When the proportion of blank space and the compactness of hole distribution in all the sub-processing areas are within a preset range, the sub-processing area optimization ends. When the changes in the proportion of blank space and the compactness of hole distribution in multiple iterations are less than a preset threshold, the sub-processing region optimization ends.

6. The multi-hole processing method based on dynamic partitioning according to claim 5, characterized in that, The blank space ratio is calculated as follows: Blank space ratio = (Total area of ​​sub-processing area − Hole coverage area) / Total area of ​​sub-processing area.

7. The multi-hole processing method based on dynamic partitioning according to claim 5, characterized in that, The compactness of the hole distribution is defined as the lateral and longitudinal standard deviations of the hole coordinates within the sub-processing area: 。 8. The multi-hole processing method based on dynamic partitioning according to claim 1, characterized in that, Using the center of the sub-processing region as a node, a target function is constructed. The target function includes the path length connecting all nodes and also includes the following method steps: A heat load weight is assigned to each of the sub-processing areas. The temperature of each sub-processing area is monitored in real time by a temperature sensor. When the temperature of a sub-processing area rises, the heat load weight is reduced; when the temperature of a sub-processing area falls, the heat load weight is increased. The heat load weight is then incorporated into the objective function.

9. A porous substrate, characterized in that, The porous substrate is formed by the multi-hole processing method according to any one of claims 1 to 8.

Images