PCB two-end wiring method based on ant colony bidirectional search, medium and device

Abstract

The invention discloses a PCB two-end wiring method based on ant colony bidirectional search, a medium and a PCB two-end wiring device, and mainly solves the problems of long time consumption and low wiring efficiency of PCB two-end wiring of a traditional ant colony algorithm. According to the implementation scheme, a layout-completed PCB design drawing is topologically changed into a grid diagram, and the positions of circuit pins for wiring at the two ends are determined on the grid diagram; determining a public target point to be reached by the ant colony in the bidirectional search according to the pin position; the ant colony is divided into two populations with different search directions, and ants in each population search towards a public target point; determining adjacent points of ants in the searching process on the grid chart, and calculating the probability that each adjacent point is selected so as to determine the next adjacent point of ant colony searching movement; and repeating the above steps until all ants in the two populations reach the common target point. According to the method, the wiring time can be greatly shortened, the number of inflection points of wiring is further reduced on the premise of ensuring the shortest wiring path, the wiring efficiency is improved, and the method can be used for designing an integrated circuit.

Description

Technical Field

[0001] The present invention belongs to the technical field of printed circuit board (PCB) design, and specifically relates to a wiring method, medium and device at both ends of a PCB, which can be used for the design of integrated circuits. Background Art

[0002] PCB design is a very important part of the integrated circuit industry. Modern engineering technology is constantly developing in the direction of electronicization, integration and systematization, which places higher and higher requirements on PCB design. In PCB design, wiring is usually the last step, and the wiring result usually determines the final quality of the circuit board. Therefore, in the PCB design process, it is necessary to abide by the PCB design principles and meet the requirements of circuit anti-interference design to achieve the best performance of the circuit. However, the difficulty of PCB design will also increase exponentially with the increase of circuit scale, and there will be more and more factors to consider, such as: wiring obstacle area restrictions, number of through holes, wiring length, line inflection point, etc., which will affect the performance of the circuit.

[0003] Traditional PCB wiring mainly uses manual wiring, which is time-consuming and labor-intensive, and will result in a large number of errors. Nowadays, PCB boards have the characteristics of a large number of electronic components, high density, and many ports, and the efficiency of manual wiring is greatly reduced. Although some existing automatic wiring algorithms can give wiring results to a certain extent, they may fall into a local optimal situation, and as the scale of PCB wiring increases, the wiring speed will be greatly reduced, and the wiring efficiency is not high.

[0004] Patent document with publication number CN110220525A discloses a path planning method based on potential field ant colony algorithm. In order to solve the blindness problem in the early search of ant colony algorithm, an artificial potential field method is introduced to guide the search of ants from the beginning. During the whole optimization process, the attraction of the target point to ants always exists. The distance from the feasible node to the end point is calculated by the artificial potential field method to comprehensively construct heuristic information. Although this method reduces the "blindness" of the early search of ant colony to a certain extent, as the scale of the problem increases and the number of obstacles on the path increases, this method of improving heuristic information by introducing artificial potential field may still cause the ant colony to fall into the risk of local optimum or even search stagnation during the search process.

[0005] The patent document with the publication number CN116702694B discloses a wiring method, medium and device for printed circuit boards at both ends based on an ant colony algorithm. The basic idea is: the printed circuit board is topologically formed into a grid diagram, on which ants are placed on one of the pins of the wiring at both ends, and the adjacent points that the ants can select at the current position are determined; the probability of each adjacent point being selected is calculated according to the probability calculation formula; the above steps are repeated until the ants reach the other pin of the wiring at both ends. Compared with the traditional ant colony search algorithm, the invention adopts a method of dynamically changing the ant search step size, which can provide a shorter path in dealing with small-scale PCB wiring problems and has a faster search speed. However, since this method still adopts a one-way serial search method, that is, the ants search from one pin of the wiring at both ends to another pin, as the scale of PCB wiring increases, the wiring complexity becomes higher and higher. Using this method to deal with PCB wiring problems will greatly increase the wiring time, and due to the dynamic change of the ant search step size, the number of inflection points of the wiring will increase, which cannot meet the actual requirements. Summary of the invention

[0006] The purpose of the present invention is to address the deficiencies of the above-mentioned prior art and provide a wiring method, medium and device for both ends of a PCB based on ant colony bidirectional search, so as to reduce the length and number of inflection points of the wiring and improve the wiring efficiency.

[0007] To achieve the above object, the technical solution of the present invention includes the following:

[0008] 1. A PCB two-end wiring method based on ant colony bidirectional search, characterized in that it includes the following steps

[0009] S1) Topologically transforming a PCB design drawing that has been completed into a grid diagram;

[0010] S2) determining the positions of the circuit pins that need to be wired at both ends on the grid diagram and initializing the ant colony algorithm parameters;

[0011] S3) Determine the common target point that the ant colony needs to reach during the bidirectional search process based on the endpoint selection theory:

[0012] Connect the two pins of the wiring at both ends. If there is no electronic component at the midpoint of the connection, the midpoint is regarded as the common target point of the ant colony in the bidirectional search process;

[0013] Connect the two pins of the wiring at both ends. If an electronic component is placed at the midpoint of the connection, find the coordinates of the non-electronic component closest to the midpoint and located on the connection line between the two pins, and regard it as the common target point of the ant colony in the bidirectional search process;

[0014] S4) The ant colony is divided into two populations with different search directions, and the ants in each population search towards a common target point:

[0015] S4a) The initial ant colony is divided into two populations with the same number but different search directions;

[0016] S4b) using multithreading technology to set up two threads that can process the ant colony's search for wiring paths in parallel, the two threads respectively processing the search tasks of the two populations, that is, the ants simultaneously start from the two pins of the wiring at both ends to search for a common target point;

[0017] S5) determining the neighboring points of the ants in the search process on the grid map, calculating the probability of each neighboring point being selected according to the optimized probability selection formula, and then determining the next neighboring point for the ant colony to search and move;

[0018] S6) Repeat step S5 until all ants in the two populations reach the common target point, and then comprehensively consider the information of wiring length, number of vias and number of inflection points obtained by the ant colony search to select the optimal wiring path.

[0019] 2. A PCB two-end wiring medium based on ant colony bidirectional search, storing a computer program, characterized in that when the computer program is executed by a processor, the PCB two-end wiring method based on ant colony bidirectional search is implemented.

[0020] 3. A PCB two-end wiring device based on ant colony bidirectional search, characterized by comprising a processor and a memory;

[0021] The memory is used to store program instructions;

[0022] The processor is used to run program instructions to execute a PCB two-end wiring method based on ant colony bidirectional search.

[0023] Compared with the prior art, the present invention has the following advantages:

[0024] Firstly, since the present invention adopts a grid wiring method, compared with non-grid wiring, grid wiring simplifies the design process and greatly reduces the wiring complexity. Grid wiring discretizes the design space, reduces the possible paths that need to be considered, and can further improve the wiring rate of the network.

[0025] Secondly, since the present invention uses thread pool technology, it can realize bidirectional parallel search of PCB wiring paths by ant colonies. Compared with the existing traditional ant colony algorithm that uses a unidirectional serial search method, it can greatly reduce the search space of ants, thereby reducing PCB wiring time and improving wiring efficiency.

[0026] Thirdly, since the present invention adds a target point guidance function to the state transition probability formula, the ant colony always has a greater probability of moving towards the target point during the search process, effectively avoiding the risk of the ant colony falling into local optimality. At the same time, due to the guiding role of the target point, the ant colony can avoid unnecessary searches, thereby reducing the wiring length and the number of turning points of the wiring.

[0027] Fourthly, since the present invention adopts the method of adaptively changing the pheromone factor and the heuristic factor, in the early stage of the ant colony search, the weight of the heuristic function is relatively high, which can avoid the ant colony from conducting blind search; in the later stage of the ant colony search, the guiding role of pheromone on the ant search path is improved, which is conducive to the ant colony to conduct global search and obtain the global optimal solution. BRIEF DESCRIPTION OF THE DRAWINGS

[0028] Figure 1 It is a flow chart for realizing the present invention;

[0029] Figure 2 This is an example diagram of searching for a common target point in the present invention.

[0030] Figure 3 The figure is a wiring result diagram of two ends of a PCB obtained by using the existing method and the method of the present invention. DETAILED DESCRIPTION

[0031] In order to enable those skilled in the art to better understand the solutions of the present invention, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings in the embodiments of the present invention.

[0032] Reference Figure 1 , the implementation steps of this example are as follows:

[0033] Step 1: Build a PCB wiring grid diagram.

[0034] 1.1) According to the scale of the PCB board, the location, quantity and size of the electronic components on the board, and the circuit pins that need to be wired, a two-dimensional wiring grid diagram is constructed using the grid method;

[0035] 1.2) Take the upper left corner of the grid diagram as the coordinate origin, horizontally to the right is the positive direction of the X axis, and vertically downward is the positive direction of the Y axis. In the grid diagram, white grids represent positions that can be wired, and black grids represent electronic components on the PCB board, that is, positions that cannot be wired;

[0036] 1.3) Each grid has a unique serial number and its corresponding coordinates. Assume that the coordinates obtained by wiring are (x i ,y i ), the corresponding sequence number on the grid map is:

[0037] N=y i ×Ngrid +x i

[0038] Where N is the serial number on the corresponding grid map, N grid is the size of the grid map;

[0039] 1.4) Determine the locations of the circuit pins that require two-end routing and mark them on the grid diagram.

[0040] Step 2: Search for common target points.

[0041] Reference Figure 2 The specific implementation of this step includes the following:

[0042] 2.1) Connect the two pins of the wiring at both ends and find the grid where the midpoint of the connection is located, such as Figure 2 (a)

[0043] 2.2) Find the common target point according to the midpoint position described in step 2.1):

[0044] If there is no electronic component placed at the midpoint of the line, the midpoint is regarded as the common target point of the ant colony in the bidirectional search process;

[0045] If the midpoint of the connection line is used to place electronic components, such as Figure 2 As shown in (b), the coordinates of the non-electronic components located on the line connecting the two pins and closest to the midpoint are searched along the line direction, and the coordinates are regarded as the common target points of the ant colony in the bidirectional search process;

[0046] 2.3) Repeat step 2.2), that is, from Figure 2 (c) Repeat the search for the common target point to Figure 2 (e) until a non-electronic component position coordinate is found and regarded as the common target point of the ant colony in the bidirectional search process. The final wiring effect is as follows: Figure 2 (f) as shown.

[0047] Step 3: The ant colony searches the PCB wiring path in parallel and bidirectionally.

[0048] 3.1) Divide the initial ant colony into two populations with the same number but different search directions;

[0049] 3.2) Select a suitable multithread library. For different programming languages, the selected multithread library may be different. In this embodiment, the programming language used is but not limited to Java, so the thread pool in java.util.concurrent provided by Java is used;

[0050] 3.3) Iterate the search for the routing path and assign routing tasks to each ant:

[0051] 3.3.1) Initialize the population size and the number of iterations of the algorithm. The population size is set to 100 and the maximum number of iterations of the algorithm is set to 100.

[0052] 3.3.2) At the initial moment, place the ants from the two populations at the positions of the two pins of the wiring at both ends;

[0053] 3.3.3) Ants in the two populations start from the positions of the two pins of the wiring at both ends and search for wiring paths to the common target point;

[0054] 3.3.3) When all ants in the two populations reach the common target point, this iteration ends and the next round of iterative search continues;

[0055] 3.3.4) After reaching the maximum number of iterations, the iteration is terminated, the wiring path obtained by the ant colony search is recorded, and information such as the wiring length, the number of vias, and the number of inflection points is calculated;

[0056] 3.4) According to the multithread library selected in step 3.2), that is, the thread pool in java.util.concurrent that comes with Java, use the ThreadPoolExecutor component in the multithreading technology to set two threads that can process the ant colony search wiring path in parallel, and submit these two threads to the thread pool for subsequent parallel processing of the search tasks of the two populations;

[0057] Step 4: Determine the neighboring points that the ant chooses to move to during the search process.

[0058] 4.1) Add the target point guidance function H to the existing probability selection formula ij :

[0059]

[0060] Where H is a constant, ranging from 2 to 4. L ij is the Euclidean distance between the ant’s current position i and its neighboring point j, L ik is the Euclidean distance between the ant's current position i and the target point k; when the angle between the ant's direction to the selected neighboring point and the ant's direction to the target is smaller, The larger the value of ij The greater the guiding role in the ant colony's search process;

[0061] 4.2) Optimize the heuristic information in the probability selection formula to obtain the optimized heuristic information η jk :

[0062]

[0063] in: λ is the heuristic information η jk The coefficient of λ gradually decreases with the increase of the number of algorithm iterations, so in the early stage of ant colony search, it has higher heuristic information, which can avoid the ant colony from performing too much useless search;

[0064] d ij is the Manhattan distance from the ant’s current position i to the neighboring point j, d jk is the Manhattan distance from the neighboring point j to the end point k, w 1 and w 2 They are d ij and d jk The weight coefficient, w 1 With w 2 The sum of is 1, in this example w 1 With w 2 The values ​​of are set to 0.3 and 0.7 respectively;

[0065] n is the number of iterations of the current ant’s search, N is the total number of iterations of the algorithm, and the value of N is set to 100;

[0066] 4.3) In order to avoid the blindness of the ant colony in the early stage of search and enhance the global search ability of the ant colony in the later stage of search, the pheromone factor α and the heuristic factor β are dynamically changed, and the pheromone factor and the heuristic factor in the probability selection formula are optimized to obtain the optimized pheromone factor α and the heuristic factor β:

[0067]

[0068] Where α(0) is the initial value of the pheromone factor α, and β(0) is the initial value of the heuristic factor β. In this example, α(0) = [1, 4], β(0) = [0, 5] are set but not limited to;

[0069] 4.4) Substitute the optimization results of step 4.1) to step 4.3) into the existing probability selection formula to obtain the final optimized probability selection formula:

[0070]

[0071] Where: p ij is the probability of the ant choosing neighboring point j when it is at position i, τ ij represents the pheromone on the neighboring point j, next dot(i) represents the set of all neighboring points when the ant is at position i;

[0072] 4.5) Using the final optimized probability selection formula obtained in step 4.4), the probability of each neighboring point being selected is calculated;

[0073] 4.6) Use the roulette method to determine the neighboring point that the ant finally chooses, that is, take a random number q and customize a fixed number q 0， Compare the two:

[0074] If q>q 0 , then directly select the neighboring point with the highest probability among all neighboring points as the next neighboring point that the ant chooses to move to;

[0075] If q 0 Since the probability calculation formula ensures that the sum of the probabilities of all neighboring points is 1, the interval of length 1 is divided according to the probability of each neighboring point, and a random number in the range of (0,1] is taken. The ant chooses to move to the neighboring point where the random number falls in the interval.

[0076] Step 5: Select the optimal routing path.

[0077] 5.1) Design a multi-objective optimization function f(p):

[0078] f(p)=α l k l +β g k g +γ h k h

[0079] Where: k l is the total wiring length, k g is the number of inflection points, k h is the number of vias, α l is the weight coefficient of the total wiring length, β g is the weight coefficient of the number of inflection points, γ h is the weight coefficient of the number of vias;

[0080] 5.2) Use the multi-objective optimization function designed in step 5.2) to balance the performance indicators such as wiring length, number of vias and number of inflection points to obtain the best wiring effect at the lowest cost.

[0081] The specific implementation of the above method is completed by a computer program, which is stored in a wiring medium at both ends of a PCB based on an ant colony bidirectional search, and the computer storage medium includes a magnetic storage medium, an optical recording medium or a memory, wherein the magnetic storage medium includes a floppy disk, a hard disk, the optical recording medium includes a CD-ROM, a DVD, and the memory includes a memory card, a ROM or a RAM. The computer storage medium can also be distributed on a computer system connected to a network, such as an application store. The computer program stored in the medium is used to complete any one of the five steps when executed by the processor. ​

[0082] In this example, a wiring device at both ends of a PCB based on ant colony bidirectional search includes a processor and a memory.

[0083] The memory is used to store program instructions, which are obtained by compiling a computer program;

[0084] The processor is used to run program instructions to execute any one of the steps in the above method.

[0085] The effect of the present invention can be further illustrated by the following simulation examples:

[0086] 1. Simulation Experiment Conditions

[0087] The hardware conditions of the simulation experiment of the present invention are Intel(R) Core(TM) i5-8250U CPU@1.60GHz and 16G memory.

[0088] The experimental simulation platform of the present invention is the Windows 10 operating system. The program is written in Java language for experimental verification. The development environment is IntelliJ IDEA 2021. The image drawing program is written in Python language, and the development environment is PyCharm 2022.

[0089] 2. Simulation Experiment Content and Result Analysis

[0090] Simulation 1: Under the above conditions, the method of the present invention and the existing traditional ant colony algorithm are used to perform wiring at both ends of a PCB with a size of 100×100. The results are as follows: Figure 3 ,in Figure 3 (a) is the wiring result diagram obtained by using the existing traditional ant colony algorithm. Figure 3 (b) is a diagram showing the wiring results obtained using the method of the present invention.

[0091] contrast Figure 3 (a) and Figure 3 (b) It can be seen that the method of the present invention has a better wiring effect in dealing with the wiring problem at both ends of the PCB, and the routing of the PCB is more reasonable, which further proves the effectiveness of the present invention.

[0092] The total routing length, time, number of inflection points and number of vias obtained by the above two methods are statistically analyzed, and the results are shown in Table 1:

[0093] Table 1 Comparison of data on wiring at both ends of PCB using the method of the present invention and the existing method

[0094] Algorithm Type Run 10 times / s Number of inflection points Number of vias Average total wiring length Traditional ant colony algorithm 86.912 25 8 569.88 Ant colony bidirectional search 55.279 21 6 559.79

[0095] As can be seen from Table 1, compared with the traditional ant colony algorithm, the wiring time, the number of inflection points, the number of vias and the average total wiring length at both ends of the PCB are significantly reduced in the present invention. The running time is reduced by 36.4% compared with the traditional ant colony algorithm, indicating that the present invention can greatly reduce the PCB wiring time and improve the efficiency of wiring at both ends of the PCB while ensuring the shortest total wiring length.

[0096] The above description is only a specific example of the present invention and does not constitute any limitation to the present invention. It is obvious that for professionals in this field, after understanding the content and principle of the present invention, it is possible to make various modifications and changes in form and details without departing from the principle and structure of the present invention. However, these modifications and changes based on the ideas of the present invention are still within the scope of protection of the claims of the present invention.

[0097] It should be noted that the step numbers in the specification and claims of the present invention are only for a clear description of the implementation scheme of the present invention to facilitate understanding, and the order of the step numbers is not limited.

Claims

1. A wiring method for PCB ends based on ant colony bidirectional search, characterized in that: The following steps are involved: S1) Topologically transforming a PCB design drawing that has been completed into a grid diagram; S2) determining the positions of the circuit pins that need to be wired at both ends on the grid diagram and initializing the ant colony algorithm parameters; S3) Determine the common target point that the ant colony needs to reach during the bidirectional search process based on the endpoint selection theory: Connect the two pins of the wiring at both ends. If there is no electronic component at the midpoint of the connection, the midpoint is regarded as the common target point of the ant colony in the bidirectional search process; Connect the two pins of the wiring at both ends. If an electronic component is placed at the midpoint of the connection, find the coordinates of the non-electronic component closest to the midpoint and located on the connection line between the two pins, and regard it as the common target point of the ant colony in the bidirectional search process; S4) The ant colony is divided into two populations with different search directions, and the ants in each population search towards a common target point: S4a) The initial ant colony is divided into two populations with the same number but different search directions; S4b) using multithreading technology to set up two threads that can process the ant colony's search for wiring paths in parallel, the two threads respectively processing the search tasks of the two populations, that is, the ants simultaneously start from the two pins of the wiring at both ends to search for a common target point; S5) determining the neighboring points of the ants in the search process on the grid map, calculating the probability of each neighboring point being selected according to the optimized probability selection formula, and then determining the next neighboring point for the ant colony to search and move; S6) Repeat step S5 until all ants in the two populations reach the common target point, and then comprehensively consider the information of wiring length, number of vias and number of inflection points obtained by the ant colony search to select the optimal wiring path.

2. The method according to claim 1, characterized in that Step S1) converting a PCB design drawing that has been laid out into a topology grid diagram, the implementation steps include the following: 2a) Based on the scale of the PCB board, the location, quantity and size of the electronic components on the board, and the circuit pins that need to be wired, a two-dimensional wiring grid diagram is constructed using the grid method; 2b) Take the upper left corner of the grid diagram as the coordinate origin, horizontally to the right is the positive direction of the X axis, and vertically downward is the positive direction of the Y axis. In the grid diagram, white grids represent positions that can be wired, and black grids represent electronic components on the PCB board, that is, positions that cannot be wired; 2c) Each grid has a unique serial number and its corresponding coordinates. Assume that the coordinates obtained by routing are (x i ,y i ), the corresponding sequence number on the grid map is: N=y i ×N grid +x i Where N is the serial number on the corresponding grid map, N grid is the size of the grid.

3. The method according to claim 1, characterized in that In step S4b), two threads are set up by using multithreading technology to process the ant colony searching the wiring path in parallel, and the implementation steps include the following: S4b1) Select a multithreaded library written in Java and use the thread pool in java.util.concurrent that comes with Java; S4b2) iteratively searching the wiring path and assigning a wiring task to each ant; S4b3) Use ThreadPoolExecutor to create two parallel threads, submit the two threads to the thread pool for execution, and each thread is responsible for the wiring path search task of the ant.

4. The method according to claim 1, characterized in that: In step S5), the probability of each neighboring point being selected is calculated according to the optimized probability selection formula, and then the next neighboring point to be searched and moved by the ant colony is determined. The implementation steps include the following: 4a) Add the target point guidance function H to the existing probability selection formula ij : Where H is a constant, L ij is the Euclidean distance between the ant’s current position i and its neighboring point j, L ik is the Euclidean distance between the ant’s current position i and the target point k; 4b) Optimize the heuristic information in the probability selection formula to obtain the optimized heuristic information η jk : in: d ij is the Manhattan distance from the ant’s current position i to the neighboring point j, d jk is the Manhattan distance from the neighboring point j to the end point k, w1 and w2 are d ij and d jk The weight coefficient of , the sum of w1 and w2 is 1, n is the number of iterations of the current ant search, and N is the total number of iterations of the algorithm; 4c) Optimize the pheromone factor and heuristic factor in the probability selection formula to obtain the optimized pheromone factor α and heuristic factor β: Where α(0) and β(0) are constants. 4d) Substitute the optimization results of step 4a) to step 4c) into the existing probability selection formula to obtain the final optimized probability selection formula: Where: p ij is the probability of the ant choosing neighboring point j when it is at position i, τ ij represents the pheromone on the neighboring point j, next dot(i) represents the set of all neighboring points when the ant is at position i. The probability of all neighboring points being selected by the ant is calculated according to the optimized probability selection formula; 4e) The probability of each neighboring point being selected is calculated using the final optimized probability selection formula obtained in step 4d), and each ant uses the roulette method to determine the next neighboring point to be moved from all the selected neighboring points.

5. The method according to claim 1, characterized in that Step S6) comprehensively considers the information of wiring length, number of vias and number of inflection points obtained by ant colony search, and selects the optimal wiring path, which is implemented as follows: S6a) Design a multi-objective optimization function f(p): f(p)=α l k l +b g k g +g h k h Where: k l is the total wiring length, k g is the number of inflection points, k h is the number of vias, α l is the weight coefficient of the total wiring length, β g is the weight coefficient of the number of inflection points, γ h is the weight coefficient of the number of vias; S6b) utilizing the multi-objective optimization function designed in step S6a) to balance performance indicators such as wiring length, number of vias and number of inflection points, so as to obtain the best wiring effect at the lowest cost.

6. A PCB two-end wiring medium based on ant colony bidirectional search, storing a computer program, characterized in that: When the computer program is executed by a processor, the PCB two-end wiring method based on ant colony bidirectional search as described in any one of claims 1 to 5 is implemented.

7. A wiring device at both ends of a PCB based on ant colony bidirectional search, characterized in that: including a processor and a memory; The memory is used to store program instructions; The processor is used to run program instructions to execute the PCB two-end wiring method based on ant colony bidirectional search as described in any one of claims 1-5.

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