A SLT equipment optimization design method and system based on discrete event simulation

Abstract

The present invention relates to the field of SLT equipment technology, and in particular to a SLT equipment optimization design method and system based on discrete event simulation, which is applied to a SLT equipment based on discrete event simulation. The detection efficiency of the SLT equipment is jointly determined by resource allocation and action rhythm. The optimization design method mainly revolves around the following core links: problem definition and parameter extraction, simulation model construction, algorithm optimization, and optimal parameter selection and verification, so as to solve reasonable resource allocation and action rhythm under site constraints and mechanical structure movement speed constraints to obtain the highest possible SLT equipment detection efficiency. The present invention solves the deficiencies of traditional SLT equipment design methods in terms of resource allocation, design scheme evaluation and verification.

Description

Technical Field

[0001] The present invention relates to the technical field of SLT equipment, and in particular to an SLT equipment optimization design method and system based on discrete event simulation. Background Art

[0002] SLT, short for System Level Test, is a testing method that verifies chip functionality under conditions that simulate real-world end-use environments. Unlike traditional automated test equipment (ATE), SLT equipment eliminates the need to create test vectors. Instead, it performs tests by running and utilizing the chip under test.

[0003] Currently, the traditional design process for new SLT equipment design requirements relies primarily on drawing on historical proposals to develop preliminary designs. Designers then repeatedly revise and adjust the new proposals, relying on experience and a series of performance calculation formulas to verify whether they meet the design requirements. However, this traditional approach has exposed several shortcomings in practice.

[0004] First, resource allocation and dynamic tach are two key factors that are often considered separately during the design process. Designers often first determine a resource allocation based on experience and then fine-tune the dynamic tach. This step-by-step approach can lead to a mismatch between resource allocation and dynamic tach, which in turn affects overall performance.

[0005] Secondly, the design solution is mainly determined based on whether it meets basic design requirements, but lacks a systematic and comprehensive evaluation standard. This leads to a large degree of blindness in the design solution adjustment process, making it difficult to ensure the superiority and feasibility of the final solution.

[0006] Finally, verification methods also have limitations. Traditional verification methods often fail to accurately simulate the complexities of equipment in real-world operating environments, which can lead to design flaws being discovered only during actual use, increasing the cost of subsequent modification and optimization.

[0007] In summary, the traditional SLT equipment design method has shortcomings in resource allocation, design scheme evaluation and verification, and it is necessary to explore more scientific and efficient design optimization methods to cope with increasingly complex and diverse design needs. Summary of the Invention

[0008] One purpose of the present invention is to propose an SLT equipment optimization design method based on discrete event simulation to solve the shortcomings of traditional SLT equipment design methods in resource allocation, design scheme evaluation and verification, etc.

[0009] One of the purposes of the present invention is to propose an SLT equipment optimization design system based on discrete event simulation, which can further improve the design efficiency and performance level of SLT equipment by introducing advanced simulation technology, optimization algorithms and intelligent tools.

[0010] To achieve this object, the present invention adopts the following technical solutions:

[0011] A discrete event simulation-based SLT equipment optimization design method is applied to a discrete event simulation-based SLT equipment, wherein the detection efficiency of the SLT equipment is determined by resource allocation and action rhythm;

[0012] The optimization design method comprises the following steps:

[0013] S1. Determine the optimization problem definition corresponding to the detection efficiency problem of SLT equipment based on the optimization goal and constraints;

[0014] S2. Based on the mechanical structure and optimization problem of the SLT equipment, quantitatively characterize the resource allocation and action rhythm through parameter settings, extract the core design parameters required for parameter optimization, and establish an optimization problem model based on the optimization problem and core design parameters;

[0015] S3. Establish a discrete event simulation model based on the mechanical structure of the SLT equipment and perform parameterization on the discrete event simulation model, including taking core design parameters as input and the optimization objectives corresponding to the core design parameters as output;

[0016] S4. Use the GA-PSO hybrid algorithm to perform particle encoding on the core design parameters, generate an initialized particle swarm within the value range of the optimization problem model, input the discrete event simulation model for simulation analysis, and calculate the fitness of the initialized particle swarm and each particle in combination with the fitness function;

[0017] S5. Perform selection, crossover, and mutation operations on the initialized particle swarm to obtain a new particle swarm, input the new particle swarm into the discrete event simulation model for simulation analysis, and calculate the fitness of each new particle swarm and each particle in combination with the fitness function;

[0018] S6. Define the historical optimal fitness of a single particle as the local optimal value, and define the historical optimal fitness of the particle swarm as the global optimal value. After each generation of particle swarm completes the fitness calculation, the local optimal value and the global optimal value are updated.

[0019] S7, judging whether the local optimal value output by the iteration meets the termination condition, if not, returning to S5 to continue the iteration, if so, jumping out of the loop, selecting the particle with the largest historical optimal fitness as the optimal core design parameter;

[0020] S8. Use virtual debugging technology to apply the optimal core design parameters to the virtual SLT equipment for verification and debugging.

[0021] Preferably, in S1, the optimization target is the maximum number of chips detected when the SLT equipment works continuously for 8 hours;

[0022] The constraints are site constraints and mechanical structure motion speed constraints;

[0023] The optimization problem is defined as solving reasonable resource allocation and action rhythm to obtain the highest possible SLT equipment detection efficiency under site constraints and mechanical structure movement speed constraints.

[0024] Preferably, in S2, the mechanical structure of the SLT equipment includes k detection units, one detection unit includes a tray management unit and n chip detection units, one chip detection unit includes m chip detection mechanisms, wherein k>=1, n>=1, m>=2;

[0025] The tray management unit includes a tray management mechanism for testing trays, an empty tray management mechanism, four BIN tray management mechanisms, a transfer trolley and a lifting mechanism;

[0026] The chip detection unit includes five tray buffer mechanisms, a BIN separation robot, two shuttles, a material change robot and multiple chip detection mechanisms;

[0027] A total of 8 core design parameters are extracted from the resource configuration and the action rhythm, including the number of single detection machines C1, the number of chip detection units C2, the number of chip detection mechanisms C3, the transfer trolley speed V1, the lifting mechanism speed V2, the BIN sorting robot speed V3, the shuttle speed V4, and the material changing robot speed V5;

[0028] Among them, C1=k, C2=k*m, C3=k*m*n, C1, C2 and C3 are all integer data, and V1, V2, V3, V4 and V5 are all floating-point data.

[0029] Preferably, in S2, the optimization problem model is as follows:

[0030]

[0031]

[0032] in, Indicates the maximum number of chips detected when the SLT equipment works continuously for 8 hours. 、 and Respectively represent the length, width and height of the SLT equipment site, Indicates the width of the detection unit. Indicates the width between adjacent detection units. Indicates the height spacing of the chip detection unit, Indicates the base height of the SLT equipment, Indicates the front and rear distance of the chip detection mechanism, Indicates the basic length of the chip detection unit.

[0033] Preferably, in S3, the following steps are specifically included:

[0034] S31. Use the Desmo-J open source discrete event simulation engine to build a discrete event simulation model, including:

[0035] S311, extracting entities based on the mechanical structure of SLT equipment;

[0036] S312, extracting events from each entity to form three event streams, namely, a chip detection event stream, a tray entry event stream, and a tray exit event stream;

[0037] S313. Create the life cycle of each entity to manage events, that is, set the execution timing of the event;

[0038] S32. Add counters and timers to the discrete event simulation model to set the output parameters, with the core design parameters as input and the maximum number of tests for the SLT equipment to work continuously for 8 hours, as well as the sum of the waiting time for the disk to be tested, the waiting time for the BIN disk, the waiting time for the BIN robot, and the waiting time for the material changing robot as output.

[0039] Preferably, in S4, the GA-PSO hybrid algorithm is used to perform particle encoding on the core design parameters and generate an initialized particle swarm, specifically comprising the following steps:

[0040] S41. Use real number encoding for particles and define the particle gene length as 8 bits. The first to eighth bits correspond to eight core design parameters, namely the number of detection units C1, the number of chip detection units C2, the number of chip detection mechanisms C3, the transfer trolley speed V1, the lifting mechanism speed V2, the BIN sorting robot speed V3, the shuttle speed V4, and the material changing robot speed V5. C1, C2, and C3 are all integer data, and V1, V2, V3, V4, and V5 are all floating-point data.

[0041] S42, set the size of the particle swarm to 20, and make the first three core design parameters of the particle gene length be arranged and combined within their respective value ranges in the optimization problem model to generate a set of integer gene segments; make the last five core design parameters of the particle gene length be divided into five equal points within their respective value ranges in the optimization problem model and make arrangements and combinations to generate a set of integer gene segments of size 5.5 Floating point gene segment;

[0042] S43. Randomly select 20 times from the groups of integer gene segments and floating-point gene segments respectively to form 20 complete codes and generate an initialized particle swarm.

[0043] Preferably, in S4 and S5, the fitness function is as follows:

[0044]

[0045] in, is the number of tests for the current individual, is the maximum number of detections present in the current population, is the minimum number of detections that appear in the current population, It is the sum of the time for the current individual to wait for the disk to be tested, the time for waiting for the BIN disk, the time for waiting for the BIN robot, and the time for waiting for the material changing robot. is the maximum sum of waiting times in the current population, is the maximum sum of waiting times in the current population;

[0046] right Normalize ,right Perform reverse normalization , each of the two items has a weight coefficient and , ,Pick ,Pick .

[0047] Preferably, in S5, performing selection, crossover and mutation operations on the initialized particle swarm to obtain a new particle swarm specifically includes the following steps:

[0048] S51. Set the crossover probability to 0.6, that is, use the roulette wheel method to select 60% of the particles in the current population to perform genetic crossover to generate new individuals;

[0049] Among them, the crossover method adopts a discrete recombination method suitable for real number coding, that is, the same bit of the two parent individuals is selected with the same probability as the gene value corresponding to the child individual;

[0050] S52, set the mutation probability to variable value, obey the function , n is the number of particle swarm evolutions;

[0051] Among them, the particle decides whether to mutate based on the mutation probability. If mutation is required, a gene is randomly selected for mutation. The mutation rule is to add or subtract one to integer data within the respective value ranges in the optimization problem model, and to multiply or divide floating-point data by 1.1.

[0052] Preferably, in S6, the termination condition is that the fluctuation of the historical optimal fitness of the particle swarm in five consecutive generations does not exceed 5%.

[0053] A discrete event simulation-based SLT equipment system is designed using the above-described discrete event simulation-based SLT equipment optimization design method and is applied to a discrete event simulation-based SLT equipment, comprising:

[0054] The optimization problem definition module is used to determine the optimization problem definition corresponding to the detection efficiency problem of SLT equipment according to the optimization objectives and constraints;

[0055] The core design parameter extraction module is used to quantitatively characterize resource allocation and action rhythm through parameter settings based on the mechanical structure and optimization problem of SLT equipment, extract the core design parameters required for parameter optimization, and establish an optimization problem model based on the optimization problem and core design parameters;

[0056] Construct a simulation model module to establish a discrete event simulation model based on the mechanical structure of the SLT equipment and perform parameterization on the discrete event simulation model, including taking core design parameters as input and the optimization objectives corresponding to the core design parameters as output;

[0057] The initialization particle swarm calculation module is used to use the GA-PSO hybrid algorithm to perform particle encoding on the core design parameters, generate the initialization particle swarm within the value range of the optimization problem model, input the discrete event simulation model for simulation analysis, and calculate the fitness of the initialization particle swarm and each particle in combination with the fitness function;

[0058] Iterative particle swarm calculation module is used to perform selection, crossover and mutation operations on the initialized particle swarm to obtain a new particle swarm, input the discrete event simulation model for simulation analysis, and calculate the fitness of each new particle swarm and each particle in combination with the fitness function;

[0059] Iterative optimization module is used to define the historical optimal fitness of a single particle as the local optimal value and the historical optimal fitness of a particle swarm as the global optimal value. After each generation of particle swarm completes fitness calculation, the local optimal value and the global optimal value are updated.

[0060] The judgment module is used to judge whether the local optimal value output by the iteration meets the termination condition. If not, it returns to S5 to continue the iteration. If so, it jumps out of the loop and selects the particle with the largest historical optimal fitness as the optimal core design parameter;

[0061] The verification and debugging module is used to use virtual debugging technology to apply the optimal core design parameters to the virtual SLT equipment for verification and debugging.

[0062] One of the above technical solutions has the following beneficial effects:

[0063] 1. Improve inspection efficiency: By optimizing resource allocation and action rhythm, the inspection efficiency of SLT equipment can be significantly improved, thereby improving overall production efficiency and product quality.

[0064] 2. Reduce design costs: Use discrete event simulation and GA-PSO hybrid algorithm for optimized design to avoid the high design and time costs brought by traditional trial and error methods.

[0065] 3. Enhanced design flexibility: Ability to handle multiple optimization objectives and constraints, providing greater flexibility and customizability for the design of SLT equipment.

[0066] 4. Improve design reliability: Use virtual debugging technology to verify and debug the optimal core design parameters to ensure that the actual effect of the optimized design is consistent with expectations, thereby improving the reliability and stability of the design. BRIEF DESCRIPTION OF THE DRAWINGS

[0067] Figure 1 It is a flow chart of an SLT equipment optimization design method based on discrete event simulation of the present invention;

[0068] Figure 2 This is a schematic diagram of particle selection, crossover, and mutation operations in a discrete event simulation-based SLT equipment optimization design method of the present invention;

[0069] Figure 3 It is a structural schematic diagram of an SLT device based on discrete event simulation of the present invention;

[0070] Figure 4 This is a flow chart of a chip detection event flow in SLT equipment based on discrete event simulation according to the present invention;

[0071] Figure 5 This is a flow chart of a tray entry event flow in SLT equipment based on discrete event simulation according to the present invention;

[0072] Figure 6 The present invention is a flowchart of a flow of a tray-out event in an SLT device based on discrete event simulation. DETAILED DESCRIPTION

[0073] The technical solution of the present invention will be further described below with reference to the accompanying drawings and through specific implementation methods.

[0074] A discrete event simulation-based SLT equipment optimization design method is applied to a discrete event simulation-based SLT equipment, wherein the detection efficiency of the SLT equipment is determined by resource allocation and action rhythm;

[0075] The optimization design method comprises the following steps:

[0076] S1. Determine the optimization problem definition corresponding to the detection efficiency problem of SLT equipment based on the optimization goal and constraints;

[0077] S2. Based on the mechanical structure and optimization problem of the SLT equipment, quantitatively characterize the resource allocation and action rhythm through parameter settings, extract the core design parameters required for parameter optimization, and establish an optimization problem model based on the optimization problem and core design parameters;

[0078] S3. Establish a discrete event simulation model based on the mechanical structure of the SLT equipment and perform parameterization on the discrete event simulation model, including taking core design parameters as input and the optimization objectives corresponding to the core design parameters as output;

[0079] S4. Use the GA-PSO hybrid algorithm to perform particle encoding on the core design parameters, generate an initialized particle swarm within the value range of the optimization problem model, input the discrete event simulation model for simulation analysis, and calculate the fitness of the initialized particle swarm and each particle in combination with the fitness function;

[0080] S5. Perform selection, crossover, and mutation operations on the initialized particle swarm to obtain a new particle swarm, input the new particle swarm into the discrete event simulation model for simulation analysis, and calculate the fitness of each new particle swarm and each particle in combination with the fitness function;

[0081] S6. Define the historical optimal fitness of a single particle as the local optimal value, and define the historical optimal fitness of the particle swarm as the global optimal value. After each generation of particle swarm completes the fitness calculation, the local optimal value and the global optimal value are updated.

[0082] S7, judging whether the local optimal value output by the iteration meets the termination condition, if not, returning to S5 to continue the iteration, if so, jumping out of the loop, selecting the particle with the largest historical optimal fitness as the optimal core design parameter;

[0083] S8. Use virtual debugging technology to apply the optimal core design parameters to the virtual SLT equipment for verification and debugging.

[0084] like Figure 1 As shown in Figure 2, this method focuses on solving the detection efficiency problem determined by resource allocation and action rhythm. The specific steps of this method are as follows:

[0085] S1. Define the optimization problem: First, based on the optimization goal and actual constraints, accurately define the optimization requirements for the SLT equipment inspection efficiency problem. This step provides a clear direction and framework for subsequent work.

[0086] S2. Extracting Core Design Parameters: We thoroughly analyze the mechanical structure of the SLT equipment and, based on the characteristics of the optimization problem, quantitatively describe resource allocation and motion tempo through parameterized settings. Based on this, we extract the core design parameters that significantly impact detection efficiency and construct an optimization model based on these parameters.

[0087] S3. Simulation Model Construction: Based on the mechanical characteristics of the SLT equipment, a discrete event simulation model is established. This discrete event simulation model must accurately reflect the actual operating conditions of the SLT equipment, including dynamic changes in resource allocation and action rhythm. Furthermore, the discrete event simulation model is parameterized to ensure that core design parameters can be used as input and the corresponding optimization target values ​​can be output.

[0088] S4. Initialize the particle swarm and perform selection, crossover, and mutation operations: Using a GA-PSO hybrid algorithm, we particle-encode the core design parameters and generate an initial particle swarm within the range of the optimization problem model, ensuring that the aforementioned constraints are met. These initial particle swarms represent different combinations of design parameters and provide a foundation for subsequent simulation analysis and optimization.

[0089] S5. Simulation Analysis and Fitness Calculation: The initialized and new particle swarms are input into the discrete event simulation model for simulation analysis. The fitness of each particle and its swarm is calculated using the fitness function. The fitness value reflects the degree of optimization of the SLT equipment's inspection efficiency under the current design parameter combination.

[0090] S6. Update the optimal value: After each generation of particle swarm completes the fitness calculation, update the local optimal value and the global optimal value. The local optimal value refers to the historical optimal fitness of a single particle, while the global optimal value refers to the historical optimal fitness of the entire particle swarm.

[0091] S7, Iterative Optimization: Perform selection, crossover, and mutation operations on the initialized particle swarm to generate new particle swarms. These new particle swarms inherit the best traits of the previous generation and introduce new mutations to increase the diversity of the search space. The new particle swarms are then fed into the simulation model for analysis and fitness calculations. This process is repeated until the fitness output from the iterations meets the pre-set termination criteria.

[0092] S8. Verification and Debugging: Finally, using virtual debugging technology, the optimized core design parameters are applied to the virtual SLT equipment for verification and debugging. This means that the real control program drives the virtual SLT equipment, and during this process, the mechanics and controls are continuously adjusted and verified. Because the virtual SLT equipment is highly consistent with the real SLT equipment in layout, motion, and control points, the debugging results are effective. Compared to traditional real-machine debugging, the development cycle is shortened, costs are reduced, and the results are better. This step ensures that the actual results of the optimized design are consistent with expectations, providing reliable support for subsequent practical applications.

[0093] In summary, this optimization design method mainly focuses on the following core links: problem definition and parameter extraction, simulation model construction, algorithm optimization, and optimal parameter selection and verification, and has the following beneficial effects:

[0094] 1. Improve inspection efficiency: By optimizing resource allocation and action rhythm, the inspection efficiency of SLT equipment can be significantly improved, thereby improving overall production efficiency and product quality.

[0095] 2. Reduce design costs: Use discrete event simulation and GA-PSO hybrid algorithm for optimized design to avoid the high design and time costs brought by traditional trial and error methods.

[0096] 3. Enhanced design flexibility: Ability to handle multiple optimization objectives and constraints, providing greater flexibility and customizability for the design of SLT equipment.

[0097] 4. Improve design reliability: Use virtual debugging technology to verify and debug the optimal core design parameters to ensure that the actual effect of the optimized design is consistent with expectations, thereby improving the reliability and stability of the design.

[0098] In summary, this optimization design method can systematically solve the detection efficiency problem of SLT equipment, improve the overall performance of the equipment, reduce design costs, and enhance design flexibility and reliability.

[0099] To further illustrate, in S1, the optimization goal is the maximum number of chips detected when the SLT equipment works continuously for 8 hours;

[0100] The constraints are site constraints and mechanical structure motion speed constraints;

[0101] The optimization problem is defined as solving reasonable resource allocation and action rhythm to obtain the highest possible SLT equipment detection efficiency under site constraints and mechanical structure movement speed constraints.

[0102] First, in the S1 phase, the optimization goal was to determine the maximum number of chips that the SLT equipment could detect within eight hours of continuous operation. This optimization goal was directly related to the production efficiency of the SLT equipment and customer needs.

[0103] Next, consider two key constraints: site constraints and mechanical motion speed constraints. Site constraints stem from the client's strict requirements for the SLT equipment's dimensions, which limit the equipment's overall layout and resource allocation. Mechanical motion speed constraints stem from the SLT equipment's physical characteristics and motion mechanisms, determining the upper limit of the equipment's speed when performing actions such as pallet transfer and chip transfer.

[0104] After defining the optimization objectives and constraints, the optimization problem was defined as: finding the optimal resource allocation and action tempo within the constraints of the site and mechanical structure to achieve the highest possible SLT equipment inspection efficiency. Resource allocation involves the distribution and layout of resources such as pallets, chips, and test equipment, while action tempo refers to the timing and rhythm of the SLT equipment's various actions.

[0105] To solve this optimization problem, the subsequent steps (S2 to S8) iteratively optimize the core design parameters through parameter settings, the establishment of discrete event simulation models, the application of GA-PSO hybrid algorithms, and virtual debugging technology until a resource configuration and action beat combination that meets the constraints and has the highest detection efficiency is found.

[0106] To further illustrate, in S2, the mechanical structure of the SLT equipment includes k detection units, one detection unit includes a tray management unit and n chip detection units, one chip detection unit includes m chip detection mechanisms, wherein k>=1, n>=1, and m>=2;

[0107] The tray management unit includes a tray management mechanism for testing trays, an empty tray management mechanism, four BIN tray management mechanisms, a transfer trolley and a lifting mechanism;

[0108] The chip detection unit includes five tray buffer mechanisms, a BIN separation robot, two shuttles, a material change robot and multiple chip detection mechanisms;

[0109] A total of 8 core design parameters are extracted from the resource configuration and the action rhythm, including the number of single detection machines C1, the number of chip detection units C2, the number of chip detection mechanisms C3, the transfer trolley speed V1, the lifting mechanism speed V2, the BIN sorting robot speed V3, the shuttle speed V4, and the material changing robot speed V5;

[0110] Among them, C1=k, C2=k*m, C3=k*m*n, C1, C2 and C3 are all integer data, and V1, V2, V3, V4 and V5 are all floating-point data.

[0111] Specifically, such as Figure 3 As shown in the figure, the tray management unit has two tasks: one is to transport the test trays or empty trays to the chip inspection unit, and the other is to collect BIN trays (chips are sorted into four grades after testing) or empty trays from the chip inspection unit. The chip inspection unit is responsible for testing the test chips and sorting the chips into BINs after testing.

[0112] Therefore, the optimization problem of SLT equipment is to improve the transfer efficiency of transfer pallets and chips under the constraints of site constraints and mechanical structure movement speed. Among them, site constraints also mean constraints on the composition of SLT equipment. Therefore, the parameters related to resource configuration are extracted as the setting quantity of each mechanical structure. It is important that the transfer trolley and the lifting mechanism are jointly responsible for the transfer of pallets, and the BIN robot, shuttle and material change robot are jointly responsible for the transfer of chips. The reasonable coordination of the speed of each transfer component can make the equipment rhythm more reasonable and thus improve the detection efficiency. The selection of these core design parameters is based on their direct impact on the detection efficiency and can be adjusted and optimized in the subsequent simulation and optimization process.

[0113] To further illustrate, in S2, the optimization problem model is as follows:

[0114]

[0115]

[0116] in, Indicates the maximum number of chips detected when the SLT equipment works continuously for 8 hours. 、 and Respectively represent the length, width and height of the SLT equipment site, Indicates the width of the detection unit. Indicates the width between adjacent detection units. Indicates the height spacing of the chip detection unit, Indicates the base height of the SLT equipment, Indicates the front and rear distance of the chip detection mechanism, Indicates the basic length of the chip detection unit.

[0117] Specifically, an optimization model was established using the core design parameters obtained in the above steps. The goal of this optimization model was to maximize the SLT system's inspection efficiency by adjusting these core design parameters within the constraints of the site and the mechanical structure's motion speed. The site constraints limited the overall layout and size of the SLT system, while the mechanical structure's motion speed constraints set the upper limit for the speed of each transport component.

[0118] To further explain, in S3, the following steps are specifically included:

[0119] It is known that Desmo-j is an open source discrete event simulation engine based on Java language. Discrete event simulation can highly restore the actual execution process of SLT equipment. The status of SLT equipment changes at discrete time points, and the advancement of simulation is based on the occurrence of events.

[0120] S31. Use the Desmo-J open source discrete event simulation engine to build a discrete event simulation model, including:

[0121] S311. Extract entities based on the mechanical structure of the SLT equipment. Known entities are independent elements in a discrete event simulation model, representing individual objects in the real world. Specifically, based on the mechanical structure of the SLT equipment, the entities extracted from the pallet management unit include one test tray management mechanism, one empty tray management mechanism, four bin tray management mechanisms, one transfer mechanism, and one lift mechanism. The entities extracted from the chip inspection unit include five pallet buffer mechanisms, one bin sorting robot, two shuttles, one material exchange robot, and multiple chip inspection mechanisms.

[0122] S312. Extract the events of each entity to form three event flows, namely chip detection event flow, pallet entry event flow and pallet exit event flow; known events are the driving force of discrete event simulation. The essence of discrete event simulation execution is the end of old events and the occurrence of new events, which are called event flows. There are pallet disassembly events in the test pallet management mechanism, pallet disassembly and pallet encoding events in the empty pallet management mechanism, pallet encoding events in the BIN pallet management mechanism, and movement events in the transfer trolley and lifting mechanism; there are pallet entry and pallet exit events in the pallet buffer mechanism, and there are chip retrieval events, chip loading events and BIN separation events in the BIN separation robot. There are movement events in the shuttle, and there are events of heading to the target detection mechanism and chip replacement events in the material replacement robot. There are chip detection events in the chip detection mechanism. As Figure 4-6 As shown in the figure, the rectangular rectangle represents the event. The events of each entity of the SLT equipment form three event flows, namely the chip detection event flow, the tray entry event flow and the tray exit event flow.

[0123] S313, create the life cycle of each entity to manage events, that is, set the execution time of the event; Figure 3 As shown, taking the shuttle as an example, when the chip detection mechanism is in the waiting state, it waits for itself to become idle. After the bin-by-bin robot loads the chip to be tested, the shuttle begins to move. Once in position, it waits for the bin-by-bin robot to complete the chip-by-bin event. The shuttle then returns to its original position and waits for the bin-by-bin event to complete before its lifecycle ends. Creating lifecycles for other entities is similar. In the Demo-j simulation software, this can be achieved by inheriting the SimProcess class and programming the lifecycle in the lifeCycle() function.

[0124] S32. Add counters and timers to the discrete event simulation model to parameterize the output. Using core design parameters as input, the output is the maximum number of tests the SLT equipment can perform during 8 hours of continuous operation, as well as the sum of the waiting time for the test disk, the waiting time for the BIN disk, the waiting time for the BIN sorting robot, and the waiting time for the reloading robot. Parameterizing the discrete event simulation model improves versatility. Due to the modular nature of the SLT equipment, this parameterization can be implemented through simple programming.

[0125] To further illustrate, in S4, the GA-PSO hybrid algorithm is used to perform particle encoding on the core design parameters and generate an initialized particle swarm, specifically including the following steps:

[0126] S41. Use real number encoding for particles and define the particle gene length as 8 bits. The first to eighth bits correspond to eight core design parameters, namely the number of detection units C1, the number of chip detection units C2, the number of chip detection mechanisms C3, the transfer trolley speed V1, the lifting mechanism speed V2, the BIN sorting robot speed V3, the shuttle speed V4, and the material changing robot speed V5. C1, C2, and C3 are all integer data, and V1, V2, V3, V4, and V5 are all floating-point data.

[0127] S42, set the size of the particle swarm to 20, and make the first three core design parameters of the particle gene length be arranged and combined within their respective value ranges in the optimization problem model to generate a set of integer gene segments; make the last five core design parameters of the particle gene length be divided into five equal points within their respective value ranges in the optimization problem model and make arrangements and combinations to generate a set of integer gene segments of size 5. 5 Floating point gene segment;

[0128] S43. Randomly select 20 times from the groups of integer gene segments and floating-point gene segments respectively to form 20 complete codes and generate an initialized particle swarm.

[0129] The basic idea behind the GA-PSO hybrid algorithm is to significantly improve solution efficiency and accuracy by combining the global search capabilities of GA with the local search capabilities of PSO. Specifically, GA facilitates local search and increases population diversity through mutation in the early stages of evolution, while in later stages, the mutation operator can disrupt an already stable population. PSO maintains stability during evolution by maintaining historical and current states through particle tracking. By introducing the GA crossover operation into PSO and reconfiguring the mutation operator, a behavior pattern is formed that behaves like PSO at the macro level and like GA at the micro level.

[0130] To further illustrate, in S4 and S5, the fitness function is as follows:

[0131]

[0132] in, is the number of tests for the current individual, is the maximum number of detections present in the current population, is the minimum number of detections that appear in the current population, It is the sum of the time for the current individual to wait for the disk to be tested, the time for waiting for the BIN disk, the time for waiting for the BIN robot, and the time for waiting for the material changing robot. is the maximum sum of waiting times in the current population, is the maximum total waiting time in the current population; since the number of tests and the waiting time have different dimensions, and the number of tests is maximized while the waiting time is minimized, Normalize ,right Perform reverse normalization , each of the two items has a weight coefficient and , ,Pick ,Pick .

[0133] To further illustrate, in S5, performing selection, crossover, and mutation operations on the initialized particle swarm to obtain a new particle swarm specifically includes the following steps:

[0134] S51. Set the crossover probability to 0.6, that is, use the roulette wheel method to select 60% of the particles in the current population to perform genetic crossover to generate new individuals;

[0135] Among them, the crossover method adopts a discrete recombination method suitable for real number coding, that is, the same bit of the two parent individuals is selected with the same probability as the gene value corresponding to the child individual.

[0136] S52, set the mutation probability to variable value, obey the function , n is the number of particle swarm evolutions;

[0137] Among them, the particle decides whether to mutate based on the mutation probability. If mutation is required, a gene is randomly selected for mutation. The mutation rule is to add or subtract one to integer data within the respective value ranges in the optimization problem model, and to multiply or divide floating-point data by 1.1.

[0138] To enhance individual diversity, e.g. Figure 2 As shown in the figure, the particle codes are crossed and mutated to complete the update of the particle position (particle code). The particles participating in the crossover and mutation need to be selected from the current particle group. The selection method can be the commonly used roulette method, which is a random selection method. The particles with higher fitness values ​​have a higher probability of being selected.

[0139] To further explain, in S6, the termination condition is that the fluctuation of the historical optimal fitness of the particle swarm over five consecutive generations does not exceed 5%. Meeting the termination condition means that even if the selection, crossover, and mutation operations are repeated, the probability of obtaining a better fitness is already very small. Therefore, it can be considered that the best particle, that is, the optimal core design parameter, has been found among the historical optimal fitness of the particle swarm over five consecutive generations.

[0140] A discrete event simulation-based SLT equipment system is designed using the above-described discrete event simulation-based SLT equipment optimization design method and is applied to a discrete event simulation-based SLT equipment, comprising:

[0141] The optimization problem definition module is used to determine the optimization problem definition corresponding to the detection efficiency problem of SLT equipment according to the optimization objectives and constraints;

[0142] The core design parameter extraction module is used to quantitatively characterize resource allocation and action rhythm through parameter settings based on the mechanical structure and optimization problem of SLT equipment, extract the core design parameters required for parameter optimization, and establish an optimization problem model based on the optimization problem and core design parameters;

[0143] Construct a simulation model module to establish a discrete event simulation model based on the mechanical structure of the SLT equipment and perform parameterization on the discrete event simulation model, including taking core design parameters as input and the optimization objectives corresponding to the core design parameters as output;

[0144] The initialization particle swarm calculation module is used to use the GA-PSO hybrid algorithm to perform particle encoding on the core design parameters, generate the initialization particle swarm within the value range of the optimization problem model, input the discrete event simulation model for simulation analysis, and calculate the fitness of the initialization particle swarm and each particle in combination with the fitness function;

[0145] Iterative particle swarm calculation module is used to perform selection, crossover and mutation operations on the initialized particle swarm to obtain a new particle swarm, input the discrete event simulation model for simulation analysis, and calculate the fitness of each new particle swarm and each particle in combination with the fitness function;

[0146] Iterative optimization module is used to define the historical optimal fitness of a single particle as the local optimal value and the historical optimal fitness of a particle swarm as the global optimal value. After each generation of particle swarm completes fitness calculation, the local optimal value and the global optimal value are updated.

[0147] The judgment module is used to judge whether the local optimal value output by the iteration meets the termination condition. If not, it returns to S5 to continue the iteration. If so, it jumps out of the loop and selects the particle with the largest historical optimal fitness as the optimal core design parameter;

[0148] The verification and debugging module is used to use virtual debugging technology to apply the optimal core design parameters to the virtual SLT equipment for verification and debugging.

[0149] The technical principles of the present invention have been described above with reference to specific embodiments. These descriptions are intended solely to illustrate the principles of the present invention and are not to be construed in any way as limiting the scope of protection of the present invention. Based on the explanations herein, those skilled in the art will be able to devise other specific embodiments of the present invention without inventive effort, and such equivalent variations or substitutions are intended to be encompassed within the scope of the claims of this application.

Claims

1. A SLT equipment optimization design method based on discrete event simulation, characterized in that: The invention is applied to an SLT equipment based on discrete event simulation, wherein the detection efficiency of the SLT equipment is determined by resource allocation and action rhythm. The optimization design method comprises the following steps: S1. Determine the optimization problem definition corresponding to the detection efficiency problem of SLT equipment based on the optimization goal and constraints; S2. Based on the mechanical structure and optimization problem of the SLT equipment, quantitatively characterize the resource allocation and action rhythm through parameter settings, extract the core design parameters required for parameter optimization, and establish an optimization problem model based on the optimization problem and core design parameters; S3. Establish a discrete event simulation model based on the mechanical structure of the SLT equipment and perform parameterization on the discrete event simulation model, including taking core design parameters as input and the optimization objectives corresponding to the core design parameters as output; S4. Use the GA-PSO hybrid algorithm to perform particle encoding on the core design parameters, generate an initialized particle swarm within the value range of the optimization problem model, input the discrete event simulation model for simulation analysis, and calculate the fitness of the initialized particle swarm and each particle in combination with the fitness function; S5. Perform selection, crossover, and mutation operations on the initialized particle swarm to obtain a new particle swarm, input the new particle swarm into the discrete event simulation model for simulation analysis, and calculate the fitness of each new particle swarm and each particle in combination with the fitness function; S6. Define the historical optimal fitness of a single particle as the local optimal value, and define the historical optimal fitness of the particle swarm as the global optimal value. After each generation of particle swarm completes the fitness calculation, the local optimal value and the global optimal value are updated. S7, judging whether the local optimal value output by the iteration meets the termination condition, if not, returning to S5 to continue the iteration, if so, jumping out of the loop, selecting the particle with the largest historical optimal fitness as the optimal core design parameter; S8. Use virtual commissioning technology to apply the optimal core design parameters to the virtual SLT equipment for verification and commissioning; In S2, the mechanical structure of the SLT equipment includes k detection units, each detection unit includes a tray management unit and n chip detection units, and each chip detection unit includes m chip detection mechanisms, wherein k>=1, n>=1, and m>=2; The optimization problem model is shown as follows: in, Indicates the maximum number of chips detected when the SLT equipment works continuously for 8 hours. 、 and Respectively represent the length, width and height of the SLT equipment site, Indicates the width of the detection unit. Indicates the width between adjacent detection units. Indicates the height spacing of the chip detection unit, Indicates the base height of the SLT equipment, Indicates the front and rear distance of the chip detection mechanism, Represents the basic length of the chip detection unit; a total of 8 core design parameters are extracted from the resource configuration and the action rhythm, including the number of detection units C1, the number of chip detection units C2, the number of chip detection mechanisms C3, the transfer trolley speed V1, the lifting mechanism speed V2, the BIN sorting robot speed V3, the shuttle speed V4 and the material changing robot speed V5; Among them, C1=k, C2=k*m, C3=k*m*n, C1, C2 and C3 are all integer data, and V1, V2, V3, V4 and V5 are all floating-point data.

2. The SLT equipment optimization design method based on discrete event simulation according to claim 1, characterized in that: In S1, the optimization goal is the maximum number of chips detected when the SLT equipment works continuously for 8 hours; The constraints are site constraints and mechanical structure motion speed constraints; The optimization problem is defined as solving reasonable resource allocation and action rhythm to obtain the highest possible SLT equipment detection efficiency under site constraints and mechanical structure movement speed constraints.

3. The SLT equipment optimization design method based on discrete event simulation according to claim 2, characterized in that: In S2, the tray management unit includes a tray management mechanism for testing, an empty tray management mechanism, four BIN tray management mechanisms, a transfer trolley, and a lifting mechanism; The chip detection unit includes five tray buffer mechanisms, a BIN separation robot, two shuttles, a material changing robot and multiple chip detection mechanisms.

4. The SLT equipment optimization design method based on discrete event simulation according to claim 3 is characterized in that: In S3, the following steps are included: S31. Use the Desmo-J open source discrete event simulation engine to build a discrete event simulation model, including: S311, extracting entities based on the mechanical structure of SLT equipment; S312, extracting events from each entity to form three event streams, namely, a chip detection event stream, a tray entry event stream, and a tray exit event stream; S313. Create the life cycle of each entity to manage events, that is, set the execution timing of the event; S32. Add counters and timers to the discrete event simulation model to set the output parameters, with the core design parameters as input and the maximum number of tests for the SLT equipment to work continuously for 8 hours, as well as the sum of the waiting time for the disk to be tested, the waiting time for the BIN disk, the waiting time for the BIN robot, and the waiting time for the material changing robot as output.

5. The SLT equipment optimization design method based on discrete event simulation according to claim 4 is characterized in that: In S4, the GA-PSO hybrid algorithm is used to perform particle encoding on the core design parameters and generate an initialized particle swarm, which specifically includes the following steps: S41. Using real number coding for the particle, define the particle gene length as 8 bits, where the first to eighth bits correspond to the eight core design parameters respectively; S42, set the size of the particle swarm to 20, and make the first three core design parameters of the particle gene length be arranged and combined within their respective value ranges in the optimization problem model to generate a set of integer gene segments; make the last five core design parameters of the particle gene length be divided into five equal points within their respective value ranges in the optimization problem model and make arrangements and combinations to generate a set of integer gene segments of size 5. 5 Floating point gene segment; S43. Randomly select 20 times from the groups of integer gene segments and floating-point gene segments respectively to form 20 complete codes and generate an initialized particle swarm.

6. The SLT equipment optimization design method based on discrete event simulation according to claim 5, characterized in that: In S4 and S5, the fitness function is as follows: in, is the number of tests for the current individual, is the maximum number of detections present in the current population, is the minimum number of detections that appear in the current population, It is the sum of the time for the current individual to wait for the disk to be tested, the time for waiting for the BIN disk, the time for waiting for the BIN robot, and the time for waiting for the material changing robot. is the maximum sum of waiting times in the current population, is the maximum sum of waiting times in the current population; right Normalize ,right Perform reverse normalization , each of the two items has a weight coefficient and , ,Pick ,Pick .

7. The SLT equipment optimization design method based on discrete event simulation according to claim 6 is characterized in that: In S5, the selection, crossover and mutation operations are performed on the initialized particle swarm to obtain a new particle swarm, which specifically includes the following steps: S51. Set the crossover probability to 0.6, that is, use the roulette wheel method to select 60% of the particles in the current population to perform genetic crossover to generate new individuals; Among them, the crossover method adopts a discrete recombination method suitable for real number coding, that is, the same bit of the two parent individuals is selected with the same probability as the gene value corresponding to the child individual; S52, set the mutation probability to variable value, obey the function , n is the number of particle swarm evolutions; Among them, the particle decides whether to mutate based on the mutation probability. If mutation is required, a gene is randomly selected for mutation. The mutation rule is to add or subtract one to integer data within the respective value ranges in the optimization problem model, and to multiply or divide floating-point data by 1.

1.

8. The SLT equipment optimization design method based on discrete event simulation according to claim 7 is characterized in that: In S6, the termination condition is that the fluctuation of the historical optimal fitness of the particle swarm in five consecutive generations does not exceed 5%.

9. An SLT equipment system based on discrete event simulation, characterized in that: The optimization design method for SLT equipment based on discrete event simulation according to any one of claims 1 to 8 is applied to SLT equipment based on discrete event simulation, comprising: The optimization problem definition module is used to determine the optimization problem definition corresponding to the detection efficiency problem of SLT equipment according to the optimization objectives and constraints; The core design parameter extraction module is used to quantitatively characterize resource allocation and action rhythm through parameter settings based on the mechanical structure and optimization problem of SLT equipment, extract the core design parameters required for parameter optimization, and establish an optimization problem model based on the optimization problem and core design parameters; Construct a simulation model module to establish a discrete event simulation model based on the mechanical structure of the SLT equipment and perform parameterization on the discrete event simulation model, including taking core design parameters as input and the optimization objectives corresponding to the core design parameters as output; The initialization particle swarm calculation module is used to use the GA-PSO hybrid algorithm to perform particle encoding on the core design parameters, generate the initialization particle swarm within the value range of the optimization problem model, input the discrete event simulation model for simulation analysis, and calculate the fitness of the initialization particle swarm and each particle in combination with the fitness function; Iterative particle swarm calculation module is used to perform selection, crossover and mutation operations on the initialized particle swarm to obtain a new particle swarm, input the discrete event simulation model for simulation analysis, and calculate the fitness of each new particle swarm and each particle in combination with the fitness function; Iterative optimization module is used to define the historical optimal fitness of a single particle as the local optimal value and the historical optimal fitness of a particle swarm as the global optimal value. After each generation of particle swarm completes fitness calculation, the local optimal value and the global optimal value are updated. The judgment module is used to judge whether the local optimal value output by the iteration meets the termination condition. If not, it returns to S5 to continue the iteration. If so, it jumps out of the loop and selects the particle with the largest historical optimal fitness as the optimal core design parameter; The verification and debugging module is used to use virtual debugging technology to apply the optimal core design parameters to the virtual SLT equipment for verification and debugging.

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