An intelligent scheduling method and system for a fast-response semiconductor packaging and testing workshop

Abstract

The present invention relates to an intelligent scheduling method and system for a fast-response semiconductor packaging and testing workshop. The workshop operation data is transmitted to the bottleneck identification module through the GUI module; the bottleneck identification module identifies all bottleneck processes through the "buffer-bottleneck index" bottleneck identification method; the GUI module and the bottleneck identification module respectively transmit the workshop scheduling data and the bottleneck process identification results to the scheduling module; the intelligent scheduling sub-module establishes a bottleneck process scheduling model for the semiconductor packaging and testing workshop and inputs the bottleneck process scheduling plan into the rule-based scheduling sub-module; the rule-based scheduling sub-module generates a global scheduling plan and transmits it to the GUI module to arrange production. It solves the problems of redundant work and low efficiency in the scheduling work of semiconductor packaging and testing factories that mostly focus on the scheduling of a certain process, can be used for the scheduling of semiconductor packaging and testing workshops, and can improve the production efficiency of the workshop. It can alleviate and overcome the "bottleneck drift" phenomenon that occurs in semiconductor packaging and testing workshops and improve production benefits.

Description

Technical Field

[0001] The present invention relates to a workshop scheduling technology, and particularly to an intelligent scheduling method and system for a fast-response semiconductor packaging and testing workshop. Background Art

[0002] Semiconductor packaging and testing is an important part of China's semiconductor industry and has great economic value. Semiconductor packaging and testing production is often regarded as a mixed-flow workshop. The semiconductor packaging and testing workshop has the characteristics of diverse product types, complex processes, a large number of equipment units, and a long processing cycle, making it difficult to efficiently complete the scheduling of the semiconductor packaging and testing workshop. Therefore, researching the scheduling problem of the semiconductor packaging and testing workshop has high practical value and theoretical significance. The semiconductor packaging and testing process includes wafer inspection, back thinning, wafer dicing, chip mounting, wire bonding, plastic encapsulation, laser printing, trim and form, electroplating, final testing, etc. Among them, chip mounting, wire bonding, and plastic encapsulation are often regarded as the most important processes in the packaging and testing workshop, and their processing efficiency has an important impact on the production efficiency of the semiconductor packaging and testing workshop.

[0003] Chinese Patent CN109085803A, Chinese Patent CN105320105A, and Chinese Patent CN103246240A mainly focus on the scheduling work of a certain process in the semiconductor packaging and testing field. Although they can improve the processing efficiency of some process production lines, shorten the production cycle, and thus improve production benefits by reducing the machine change time, they do not consider the overall scheduling of the semiconductor packaging and testing workshop and cannot fully study the scheduling problem of the semiconductor packaging and testing workshop. Because the orders obtained by semiconductor packaging and testing enterprises do not match the production capacity of each process in the workshop, a "bottleneck drift" phenomenon occurs in the workshop, that is, the process where the production bottleneck is located constantly changes, resulting in changes in the processes of "blockage" and "starvation". Only considering the scheduling of a single process cannot solve the bottleneck drift and cannot achieve truly effective scheduling. Summary of the Invention

[0004] Aiming at the problems of redundant work and low efficiency in the scheduling work of semiconductor packaging and testing factories mainly focusing on a certain process, a fast-response intelligent scheduling method and system for semiconductor packaging and testing workshops are proposed, in order to be used for the scheduling of semiconductor packaging and testing workshops, thereby improving the production efficiency of the workshop. Considering the overall scheduling of semiconductor packaging and testing factories, it can alleviate and overcome the "bottleneck drift" phenomenon in semiconductor packaging and testing workshops and improve production benefits. Therefore, it is of great research significance to conduct an overall study on the scheduling of semiconductor packaging and testing production workshops.

[0005] The technical solution of the present invention is as follows:

[0006] A fast-response intelligent scheduling method for a semiconductor packaging test workshop, including three modules: a graphical user interface (GUI) module, a bottleneck identification module, and a scheduling module; the scheduling module includes an intelligent scheduling sub-module and a rule-based scheduling sub-module, and the following steps are completed through these modules:

[0007] Step 1: The user transmits the workshop operation data to the bottleneck identification module through the GUI module;

[0008] Step 2: The bottleneck identification module identifies all bottleneck processes through the "buffer-bottleneck index" bottleneck identification method;

[0009] Step 3: The GUI module and the bottleneck identification module respectively transmit the workshop scheduling data and the bottleneck process identification results to the scheduling module;

[0010] Step 4: The intelligent scheduling sub-module establishes a bottleneck process scheduling model for the semiconductor packaging test workshop, uses IAHA for bottleneck process scheduling, and inputs the obtained bottleneck process scheduling plan into the rule-based scheduling sub-module;

[0011] Step 5: According to the rule library, the user selects rules for each process in advance. Combining with the bottleneck process scheduling plan obtained by the intelligent scheduling sub-module, the rule-based scheduling sub-module generates a global scheduling plan and transmits it to the GUI module to arrange production.

[0012] Further, the specific operation of Step 2 is as follows:

[0013] Step 2.1: Judge whether there is a backlog of work-in-progress in the buffer of process s, s ∈ 1...S, where S is the total number of processes. If so, this process is a bottleneck process and enter Step 2.3; otherwise, enter Step 2.2;

[0014] Step 2.2: Use formula (1) and formula (2) to calculate the bottleneck index of each process, and judge whether this process is the process with the highest bottleneck index. If so, enter Step 2.3; otherwise, enter Step 2.4;

[0015]

[0016] c s =T s -F s (t) (2)

[0017] where I BN is the bottleneck index, w t , w b , w q are the influence weights of the number of products produced by the process, the process buffer, and the product quality on the bottleneck degree respectively, and satisfy w t +w b +w q =1, cs and l s are the production capacity and production load respectively, and T s is the assumed available processing capacity, and F s (t) is the quantity by which the production capacity of process s changes due to changes in actual production conditions. and are the maximum bearable quantity of the buffer and the newly added product quantity of the buffer respectively. is the influence function of the quality assurance ability (q ac ) on the bottleneck degree, and (q ac ) is a comprehensive reflection of the quality ability and quality requirements.

[0018] Step 2.3: Record this process as a bottleneck process and enter Step 2.5.

[0019] Step 2.4: Record this process as a non-bottleneck process and enter Step 2.5.

[0020] Step 2.5: Judge whether this process is the last process. If so, enter Step 2.6; otherwise, enter Step 2.1.

[0021] Step 2.6: End the judgment and output all bottleneck processes to the scheduling module.

[0022] Furthermore, the specific operation of Step 4 is as follows:

[0023] Step 4.1: Establish a bottleneck process scheduling model for the semiconductor packaging and testing workshop, which includes all bottleneck processes and the processes between bottlenecks.

[0024] Step 4.2: Optimize the bottleneck process workshop scheduling model by improving the artificial hummingbird algorithm.

[0025] Step 4.2.1: Initialize the population by improving the NEH heuristic rule instead of randomly generating the initial population.

[0026] Step 4.2.2: Initialize the food source access table.

[0027] Step 4.2.3: Set the number of iterations as Iteration and use IAHA for optimization search.

[0028] Step 4.2.4: Improve the foraging method through the foraging judgment formula to replace the foraging method of guiding foraging or local foraging with a 50% probability each.

[0029] Step 4.2.5: Judge the number of iterations. If the number of iterations is a multiple of the preset value n of the migration coefficient, perform enhanced tabu local foraging.

[0030] Step 4.2.6: Determine the number of iterations. If the number of iterations exceeds the predetermined value \(2n\) of the migration coefficient, perform migratory foraging;

[0031] Step 4.2.7: Output the individual with the optimal fitness value, that is, the optimal solution for the bottleneck process scheduling;

[0032] Step 4.3: The intelligent scheduling sub-module outputs the bottleneck process scheduling plan.

[0033] Furthermore, the specific operation of Step 4.2.1 is as follows:

[0034] Step 4.2.1.1: Calculate the total processing time of all orders For \(j\in2...n\), that is, the sum of the ratios of the processing times of each stage of the order to the sum of the processing speeds of each stage; Arrange the orders in non-increasing order of \(TP\) j to obtain the initial arrangement \(\pi\) 0 =\(\{\pi\) 0 (1), \(\pi\) 0 (2),..., \(\pi\) 0 (n)\};

[0035] Step 4.2.1.2: Take out the first two orders \(\pi\) 0 of \(\pi\) 0 (1) and \(\pi\) 0 (2), and sort them to obtain the two possible schedules \(\{\pi\) 0 (1), \(\pi\) 0 (2)\} and \(\{\pi\) 0 (2), \(\pi\) 0 (1)\}; Evaluate these two partial schedules, and take the one with the smaller maximum completion time as the current schedule, denoted as \(\pi = \{\pi(1), \pi(2)\}\);

[0036] Step 4.2.1.3: Take out the \(j\)th order \(\pi\) 0 of \(\pi\) 0 (j), and insert it into all possible positions of \(\pi\) to obtain a total of \(j\) partial permutations; Evaluate the obtained partial permutations, and take the partial permutation with the smallest maximum completion time as the current schedule \(\pi\);

[0037] Step 4.2.1.4: Let \(j = j + 1\); If \(j\leq n - 1\), then go to Step 2.1.3; Otherwise, output the current schedule \(\pi\);

[0038] Step 4.2.1.5: Based on the current schedule \(\pi=\{\pi(1), \pi(2),..., \pi(n)\}\), randomly select integers \(l\), \(m\) that satisfy the condition \(l\lt m\leq n\), and exchange the order of the \(l\)th order and the \(m\)th order in the current schedule \(\pi\) to obtain a new individual;

[0039] Step 4.2.1.6: Repeat Step 4.1.5 until a set of Hummingbird individuals with a quantity of Popsize / 2 is generated;

[0040] Step 4.2.1.7: Directly generate Popsize / 2 duplicate Hummingbird individuals using the result of the improved NFH heuristic algorithm, and merge them with the set of individuals generated in Step 4.1.6 to obtain the initial population.

[0041] Furthermore, the specific operation of Step 4.2.4 is as follows:

[0042] Step 4.2.4.1: Select the foraging method through the foraging judgment formula;

[0043] Replace the original 50% probability in AHA with the foraging judgment formula to make the selection between guided foraging and territorial foraging; it is beneficial to increase the selection ratio of guided foraging in the early stage of iteration to enhance the ability to explore other spaces, and increase the selection of territorial foraging in the later stage of iteration to enhance the ability to find local optimal solutions;

[0044]

[0045]

[0046] where GT is the foraging judgment coefficient; GT max and GT min are the maximum foraging judgment coefficient and the minimum foraging judgment coefficient respectively, with values Popsize is the population size, Popsize ≥mean is the number of individuals in the population whose fitness value is greater than or equal to the average fitness value of the population; it is the iteration number; Iteration is the maximum iteration number; v i (t + 1) is the position of the candidate food source of the i-th hummingbird at time t + 1; f Guided (v i，tar (t), x i (t)) is the guided foraging search, x i (t) is the position of the food source of the i-th hummingbird at time t, v i，tar (t) is the position of the target food source that the i-th hummingbird intends to visit; f Territorial (x i (t)) is the territorial foraging search;

[0047] Step 4.2.4.2: The hummingbird searches for a better food source through three flight methods;

[0048] When the generated random number rand ≥ GT, the hummingbird chooses to conduct guided foraging. There are three flight patterns for guided foraging: selecting a processing location to swap two orders, selecting multiple consecutive locations to swap two order blocks, and selecting multiple locations to swap multiple orders;

[0049] When the generated random number rand < GT, the hummingbird chooses to conduct area foraging. There are three flight patterns for area foraging: selecting two locations to swap two orders, selecting multiple consecutive locations to swap orders within a block, and selecting multiple locations to swap multiple orders;

[0050] Step 4.2.4.3: Update the population and the access table;

[0051] The position of the i-th food source is updated as follows:

[0052]

[0053] In formula fitness(*), it is the fitness value represented by the hummingbird. If the fitness of the candidate food source is lower than that of the current food source, the hummingbird abandons the current food source and stays at the newly generated candidate food source v i (t + 1) feeds; update the access table.

[0054] Furthermore, the specific operation of Step 4.2.5 is as follows:

[0055] Step 4.2.5.1: By taking a random integer randint as the number of hummingbirds for intensified foraging, randint ∈ [2, 3, 4, 5], calculate the number of times RS for each hummingbird to conduct intensified foraging num , RS num = Popsize / randint;

[0056] Step 4.2.5.2: Screen the area-foraging hummingbirds for intensification through roulette wheel selection;

[0057] Step 4.2.5.3: Conduct RS num times of intensified taboo area foraging for each selected hummingbird in turn. The intensified taboo area foraging method adds a taboo table on the basis of area foraging to ensure that the search results of each intensified taboo area foraging are different;

[0058] Step 4.2.5.4: Update the population and the access table.

[0059] Furthermore, the specific operation of Step 4.2.6 is as follows:

[0060] Step 4.2.6.1: Select the hummingbird with the worst fitness value and let this hummingbird migrate to the position of the hummingbird with the best discovered fitness value;

[0061] Step 4.2.6.2: Update the access table.

[0062] Furthermore, the specific operations of Step 5 are as follows:

[0063] The heuristic rule library of the rule scheduling sub-module includes a heuristic processing order rule library and a heuristic equipment unit selection rule library;

[0064] The processing order rule library includes rules such as the rule of giving priority to shorter processing time, the rule of giving priority to longer processing time, the first-come-first-served rule, and the rule of giving priority to earlier operation due dates;

[0065] The equipment unit selection rule library includes rules such as the rule of selecting the equipment unit with the shortest processing time, the rule of selecting the equipment unit with the shortest machine change time, the rule of selecting the equipment unit with the highest precision, the rule of selecting the equipment unit with the lowest precision, the rule of selecting the equipment unit that can process the most order types, and the rule of selecting the equipment unit that can process the fewest order types;

[0066] Step 5.1: The user selects the corresponding heuristic rules for each operation in advance;

[0067] Step 5.2: Use the processing order rules and equipment unit selection rules selected for each operation to generate an overall scheduling plan;

[0068] Step 5.3: Transmit the generated global scheduling plan to the GUI module to arrange production.

[0069] A fast-response intelligent scheduling system for a semiconductor packaging and testing workshop includes three parts: a graphical user interface GUI module, a bottleneck identification module, and a scheduling module; it is used to complete the fast-response intelligent scheduling method for the semiconductor packaging and testing workshop;

[0070] The GUI module is used to transmit workshop operation data, workshop scheduling data, and receive the global scheduling plan;

[0071] The bottleneck identification module identifies all bottleneck operations through the "buffer-bottleneck index" bottleneck identification method;

[0072] The scheduling module includes an intelligent scheduling sub-module and a rule scheduling sub-module. The intelligent scheduling sub-module solves the bottleneck operation scheduling problem through an improved artificial hummingbird algorithm to generate a bottleneck scheduling plan. The rule scheduling sub-module generates a global scheduling plan by the user independently selecting rules from the rule library and combining the bottleneck scheduling plan.

[0073] Further, it further includes a memory and a processor. The memory stores a computer program. When the processor executes the computer program, it executes the "buffer-bottleneck index" bottleneck identification method of the bottleneck identification module, the IAHA algorithm of the intelligent scheduling sub-module in the scheduling module, and the rule scheduling sub-module in the scheduling module.

[0074] The beneficial effects of the present invention are as follows:

[0075] The present invention discloses an intelligent scheduling method and system for a fast-response semiconductor packaging and testing workshop, which relates to the field of workshop scheduling. The present invention considers the overall scheduling of a semiconductor packaging and testing factory, establishes a scheduling model for a semiconductor packaging and testing workshop, screens out bottleneck processes through the "buffer-bottleneck index" bottleneck identification method, constructs a bottleneck process workshop scheduling model, and uses the Improved Artificial Hummingbird Algorithm (IAHA) to solve the bottleneck processes, and complements the scheduling of missing processes using a heuristic rule library, thereby completing the scheduling of the entire workshop, solving the problem of scheduling optimization in a semiconductor packaging and testing production workshop, shortening the production cycle of bottleneck processes, improving the processing efficiency of the production line, and thus improving the production efficiency of the semiconductor packaging and testing production workshop. BRIEF DESCRIPTION OF THE DRAWINGS

[0076] Figure 1 It is the overall design diagram of the intelligent scheduling method and system for the fast-response semiconductor packaging and testing workshop of the present invention;

[0077] Figure 2 It is the flow chart of the "buffer-bottleneck index" bottleneck identification method of the present invention;

[0078] Figure 3 It is the flow chart of the IAHA of the present invention;

[0079] Figure 4 It is the demonstration diagram of the implementation of three flight modes for improved guided foraging of the present invention;

[0080] Figure 5 It is the demonstration diagram of the implementation of three flight modes for improved area foraging of the present invention;

[0081] Figure 6 It is the Gantt chart of four scheduling methods for the bottleneck processes of the present invention. a) is FIFO, b) is NEH, c) is IAHA, and d) is GA. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0082] The present invention will be described in detail below with reference to the accompanying drawings and specific embodiments. This embodiment is implemented on the premise of the technical solution of the present invention, and gives a detailed implementation manner and specific operation process, but the protection scope of the present invention is not limited to the following embodiments.

[0083] The present invention provides an intelligent scheduling method and system for a fast-response semiconductor packaging and testing workshop, which includes three parts: a Graphical User Interface (GUI) module, a bottleneck identification module, and a scheduling module (including an intelligent scheduling sub-module and a rule-based scheduling sub-module), as Figure 1 shown.

[0084] The GUI module is used to transmit workshop operation data, workshop scheduling data, and receive a global scheduling plan. The bottleneck identification module uses the "buffer-bottleneck index" bottleneck identification method. The scheduling module includes an intelligent scheduling sub-module and a rule-based scheduling sub-module. The intelligent scheduling sub-module solves the bottleneck process scheduling problem through the IAHA algorithm to generate a bottleneck scheduling plan. The rule-based scheduling sub-module generates a global scheduling plan by the user independently selecting rules from the rule library and combining the bottleneck scheduling plan.

[0085] The scheduling process of the system includes the following steps:

[0086] Step 1: The user transmits the workshop operation data to the bottleneck identification module through the GUI module;

[0087] Step 2: The bottleneck identification module identifies all bottleneck processes through the "buffer-bottleneck index" bottleneck identification method. The bottleneck identification process is as Figure 2 shown. The relevant data of the bottleneck index of each process in the semiconductor packaging and testing workshop involved in the present invention is shown in Table 1. The specific steps are as follows;

[0088] Table 1 Relevant data of process bottleneck index

[0089]

[0090] Step 2.1: Determine whether there is a backlog of work-in-process (new addition in the buffer) in the buffer of process s (s ∈ 1...S, where S is the total number of processes). If so, this process is a bottleneck process, and go to Step 2.3; otherwise, go to Step 2.4;

[0091] Step 2.2: Calculate the bottleneck index of each process using Formula (1) and Formula (2), and determine whether this process is the process with the highest bottleneck index. If so, go to Step 2.3; otherwise, go to Step 2.4;

[0092]

[0093] c s = T s - F s (t) (2)

[0094] where I BN is the bottleneck index, w t , wb , w q are the influence weights of the number of products produced in the process, the process buffer, and product quality on the bottleneck degree respectively, and satisfy w t + w b + w q = 1, c s and l s are production capacity and production load (actual processed product quantity) respectively, T s is the assumed available processing capacity, F s (t) is the quantity by which the production capacity of process s changes due to changes in actual production conditions, and are the maximum load capacity of the buffer and the number of newly added products in the buffer respectively, is the influence function of quality assurance ability (q ac ) on the bottleneck degree, (q ac ) is a comprehensive reflection of quality ability and quality requirements.

[0095] Step 2.3: Record this process as a bottleneck process and enter Step 2.5;

[0096] Step 2.4: Record this process as a non-bottleneck process and enter Step 2.5;

[0097] Step 2.5: Determine whether this process is the last process. If so, enter Step 2.6; otherwise, enter Step 2.1;

[0098] Step 2.6: End the determination and output all bottleneck processes to the scheduling module;

[0099] Through the "buffer-bottleneck index" bottleneck identification method, the three connected processes of chip mounting, wire bonding, and plastic encapsulation are defined as bottlenecks due to buffer accumulation; take the weight values w t = 0.5, w b = 0.5, w q = 0, and the maximum value in the bottleneck index I BN is the chip mounting process, which is 0.443. Therefore, the bottleneck processes in this case are the three processes of chip mounting, wire bonding, and plastic encapsulation.

[0100] Step 3: The GUI module and the bottleneck identification module respectively transmit the workshop scheduling data and the bottleneck process identification results to the scheduling module;

[0101] Step 4: The intelligent scheduling sub-module establishes a bottleneck process scheduling model for the semiconductor packaging and testing workshop, uses IAHA to schedule the bottleneck processes, and inputs the obtained bottleneck process scheduling plan to the rule scheduling sub-module. The specific steps are as follows;

[0102] Step 4.1: Establish a bottleneck process scheduling model for the semiconductor packaging and testing workshop, which includes all bottleneck processes and the processes between bottlenecks;

[0103] The parameter symbols of the mathematical model are defined as follows:

[0104] N--Total number of orders, i is the order index;

[0105] K--Total number of processing stages, k is the stage index;

[0106] m k --Total number of equipment units in the stage;

[0107] W i，k --Processing time of order i in stage k;

[0108] P i，k，j --Processing time of order i on equipment unit j in stage k;

[0109] v k，j --Speed of the equipment unit in the stage;

[0110] B i，k，j --Start time of the order on equipment unit j in stage k;

[0111] E i，k，j --Completion time of the order on equipment unit j in stage k;

[0112] C i --Completion time of order i; C max Makespan;

[0113] X i，k，j --Is a 0-1 variable. If order i is scheduled to be processed on the jth equipment unit in the stage, the value is 1, otherwise it is 0.

[0114] Furthermore, the objective function of the workshop scheduling model is formula (3):

[0115] fitness = w1 * C max + w2 * Cost + w3 * Tardiness (3)

[0116] Where fitness is the weighted scheduling objective, w1, w2, and w3 are the weight values of the maximum processing time, processing cost, and average tardiness time respectively, and w1 + w2 + w3 = 1. The specific values of the weight times are determined according to the actual situation of different semiconductor packaging and testing production workshops. In this case, the weight values are taken as w1 = 1, w2 = 0, w3 = 0, C max , Cost, and Tardiness are the maximum processing time, processing cost, and average tardiness time respectively.

[0117] The constraint conditions of the mathematical model are as follows:

[0118] E i，k，j ≤B i，k+1，j (4)

[0119]

[0120]

[0121] E i2，k，j =B i2，k，j +P i2，k，j (7)

[0122] C i =E i，K，j (8)

[0123] Equation (3) is the objective function; Equation (4) indicates that the start time of each order's current process is after the previous process; Equation (5) indicates that each order's process is processed on only one equipment unit; Equation (6) indicates the completion time of the order in each process; Equation (7) indicates that the completion time of a certain order is the sum of the start processing time of the order and the processing time of this stage; Equation (8) indicates that the final completion time of the order is the processing end time of stage K.

[0124] The relevant data for the bottleneck process shop scheduling are shown in Tables 2 and 3. Table 2 is the data of the order processing time for the bottleneck process shop scheduling, which records the production volume of 20 orders, and the basic processing time of the corresponding type of chip in the three bottleneck processes of chip mounting, wire bonding, and plastic encapsulation, that is, the time consumed for processing every 10,000 chips; Table 3 is the data of the processing rate of the equipment units in the bottleneck process shop, which records the number of scheduled equipment units and the number of equipment units with different processing rates.

[0125] Table 2 Data of Order Processing Time for Bottleneck Process Shop Scheduling

[0126]

[0127] Table 3 Data of Processing Rate of Equipment Units in Bottleneck Process Shop

[0128]

[0129] Step 4.2: Optimize the bottleneck process shop scheduling model through IAHA, and the algorithm flow is as Figure 3 shown;

[0130] Step 4.2.1: Initialize by improving the NEH (the Nawaz, Enscore, and Ham, NEH) heuristic rule instead of randomly generating;

[0131] Step 4.2.1.1: Calculate the total processing time of all orders For \(j\in2...n\), that is, the sum of the ratios of the processing times of each stage of the order to the processing speeds of each stage. Arrange the orders in non-increasing order of \(TP\) j Arrange all orders to obtain the initial permutation \(\pi\) 0 =\(\{\pi\) 0 (1), \(\pi\) 0 (2),..., \(\pi\) 0 (n)\}

[0132] Step 4.2.1.2: Take out the first two orders \(\pi\) 0 of \(\pi\), namely \(\pi\) 0 (1) and \(\pi\) 0 (2). Sort them to obtain the two possible schedules \(\{\pi\) 0 (1), \(\pi\) 0 (2)\} and \(\{\pi\) 0 (2), \(\pi\) 0 (1)\}. Evaluate these two partial schedules, and take the one with the smaller maximum completion time as the current schedule, denoted as \(\pi=\{\pi(1),\pi(2)\}\). Let \(j = 3\).

[0133] Step 4.2.1.3: Take out the \(j\)-th order \(\pi\) 0 of \(\pi\), namely \(\pi\) 0 (j). Insert it into all possible positions of \(\pi\) to obtain \(j\) partial permutations. Evaluate the obtained partial permutations, and take the partial permutation with the smallest maximum completion time as the current schedule \(\pi\).

[0134] Step 4.2.1.4: Let \(j=j + 1\). If \(j\leq n - 1\), then go to Step 2.1.3; otherwise, output the current schedule \(\pi\).

[0135] Step 4.2.1.5: Based on the current schedule \(\pi=\{\pi(1),\pi(2),...,\pi(n)\}\), randomly take integers \(l\), \(m\) satisfying the condition \(l\lt m\leq n\), and swap the order of the \(l\)-th order and the \(m\)-th order in the current schedule \(\pi\) to obtain a new individual.

[0136] Step 4.2.1.6: Repeat Step 4.1.5 until a set of \(Popsize / 2\) hummingbird individuals is generated;

[0137] Step 4.2.1.7: Directly generate \(Popsize / 2\) duplicate hummingbird individuals using the result of the improved NEH heuristic algorithm, and merge them with the set of individuals generated in Step 4.1.6 to obtain the initial population.

[0138] Step 4.2.2: Initialize the food source access table;

[0139]

[0140] where i = j, VT i，j = null indicates that the hummingbird feeds on a specific food source; for i ≠ j, VT i，j = 0 indicates that in the current iteration, the i-th hummingbird has just visited the j-th food source.

[0141] Step 4.2.3: Set the number of iterations as Iteration, and use IAHA for optimization search;

[0142] Step 4.2.4: Improve the foraging method through the foraging judgment formula, replacing the 50% probability for each of the guided foraging method or the territorial foraging method;

[0143] Step 4.2.4.1: Select the foraging method through the foraging judgment formula;

[0144] Replace the original 50% probability in AHA through the foraging judgment formula to make the selection between guided foraging and territorial foraging. This is beneficial to increasing the selection ratio of guided foraging in the early stage of iteration to enhance the ability to explore other spaces, and increasing the selection of territorial foraging in the later stage of iteration to enhance the ability to find local optimal solutions;

[0145]

[0146]

[0147] where GT is the foraging judgment coefficient; GT max and GT min are the maximum foraging judgment coefficient and the minimum foraging judgment coefficient respectively, with values of Popsize is the population size, Popsize ≥mean is the number of individuals in the population whose fitness value is greater than or equal to the average fitness value of the population; it is the number of iterations; Iteration is the maximum number of iterations; v i (t + 1) is the position of the candidate food source of the i-th hummingbird at time t + 1; f Guided (v i，tar (t), x i (t)) is the guided foraging search, x i (t) is the position of the food source of the i-th hummingbird at time t, v i，tar (t) is the position of the target food source that the i-th hummingbird intends to visit; f Territorial (x i (t)) is the territorial foraging search.

[0148] Step 4.2.4.2: The hummingbird finds a better food source through three flight methods;

[0149] When the generated random number rand ≥ GT, the hummingbird chooses to conduct guided foraging, such asFigure 4 As shown. Three flight modes for guiding foraging: extracting one processing location to swap two orders, extracting multiple consecutive locations to swap two order blocks, and extracting multiple locations to swap multiple orders.

[0150] When the generated random number rand < GT, the hummingbird chooses to perform area foraging, as Figure 5 shown. Three flight modes for area foraging: extracting two locations to swap two orders, extracting multiple consecutive locations to swap orders within a block, and extracting multiple locations to swap multiple orders.

[0151] Step 4.2.4.3: Update the population and the access table.

[0152] The position of the i-th food source is updated as follows:

[0153]

[0154] In formula fitness(*), it is the fitness value represented by the hummingbird. If the fitness of the candidate food source is lower than that of the current food source, the hummingbird abandons the current food source and stays at the newly generated candidate food source v i (t + 1) to feed; update the access table;

[0155] Step 4.2.5: Judge the number of iterations. If the number of iterations is a multiple of the preset value n of the migration coefficient, perform enhanced taboo area foraging;

[0156] Step 4.2.5.1: By taking a random integer randint (randint ∈ [2, 3, 4, 5]) as the number of hummingbirds for enhanced foraging, calculate the number of times RS num (RS num = Popsize / randint);

[0157] Step 4.2.5.2: Screen the area foraging hummingbirds for enhancement through roulette;

[0158] Step 4.2.5.3: Perform RS num times of enhanced taboo area foraging on each selected hummingbird in turn. The enhanced taboo area foraging method adds a taboo table on the basis of area foraging to ensure that the search results of each enhanced taboo area foraging are different;

[0159] Step 4.2.5.4: Update the population and the access table;

[0160] Step 4.2.6: Judge the number of iterations. If the number of iterations exceeds the preset value 2n of the migration coefficient, perform migratory foraging;

[0161] Step 4.2.6.1: Select the hummingbird with the worst fitness value and let it migrate to the position of the hummingbird with the best fitness value that has been discovered;

[0162] Step 4.2.6.2: Update the access table;

[0163] Step 4.2.7: Output the individual with the best fitness value, that is, the optimal solution for bottleneck process scheduling;

[0164] Table 4 is the algorithm performance comparison table of the present invention for the above case. The improved artificial hummingbird algorithm (IAHA) and the genetic algorithm (GA) are used 20 times. It can be seen that IAHA is significantly superior to the traditional genetic algorithm in terms of time and algorithm performance, and is significantly superior to the FIFO (First in First out) and NEH (the Nawaz, Enscore, and Ham) heuristic rules in terms of performance. As Figure 6 a-d in it are the Gantt charts for the scheduling of bottleneck processes in four ways: FIFO, NEH heuristic rule, IAHA algorithm, and genetic algorithm of the present invention.

[0165] Table 4 Algorithm Performance Comparison Table

[0166]

[0167] Step 4.3: The intelligent scheduling sub-module outputs the bottleneck process scheduling plan;

[0168] Step 5: According to the rule library, the user selects rules for each process in advance. Combining with the bottleneck process scheduling plan obtained by the intelligent scheduling sub-module, the rule scheduling sub-module generates a global scheduling plan and transmits it to the GUI module to arrange production. The specific steps are as follows;

[0169] The heuristic rule library of the rule scheduling sub-module includes a heuristic processing sequence rule library and a heuristic equipment unit selection rule library. The order processing sequence rule library includes rules such as the shorter processing time is more prioritized rule, the longer processing time is more prioritized rule, the first-come-first-processed rule, and the earlier the process delivery date is more prioritized rule; the equipment unit selection rule library includes rules such as the rule of selecting the equipment unit with the shortest processing time, the rule of selecting the equipment unit with the shortest machine change time, the rule of selecting the equipment unit with the highest accuracy, the rule of selecting the equipment unit with the lowest accuracy, the rule of selecting the equipment unit that can process the most order types, and the rule of selecting the equipment unit that can process the fewest order types.

[0170] Step 5.1: The user selects the corresponding heuristic rules for each process in advance;

[0171] Step 5.2: Generate an overall scheduling plan using the processing sequence rules and equipment unit selection rules selected for each process. In this case, since the production capacity of non-bottleneck processes is relatively sufficient, the FIFO rule is selected to choose the processing orders, and the FAM rule (Finite Available Machine rule) is selected to choose the equipment units.

[0172] Step 5.3: Transmit the generated global scheduling plan to the GUI module to arrange production.

[0173] It should be further noted that the above only describes the specific embodiments of the present invention in detail, but it does not limit the protection scope of the present invention. Equivalent modifications and substitutions made by those skilled in the art to the present invention are all included in the scope of the present invention. Therefore, without departing from the spirit and scope of the present invention, equal transformations and modifications made to the present invention are all covered within the scope of the present invention.

Claims

1. An intelligent scheduling method for a fast-response semiconductor packaging test workshop, characterized in that It includes three parts: a graphical user interface (GUI) module, a bottleneck identification module, and a scheduling module. The scheduling module includes an intelligent scheduling sub-module and a rule-based scheduling sub-module. Through these modules, the following steps are completed: Step 1: The user transmits the workshop operation data to the bottleneck identification module through the GUI module; Step 2: The bottleneck identification module identifies all bottleneck processes through the "buffer-bottleneck index" bottleneck identification method; The specific operation of Step 2 is: Step 2.1: Determine whether there is a backlog of work-in-process in the buffer of process s, where s ∈ 1…S, and S is the total number of processes. If so, this process is a bottleneck process, and go to Step 2.3; otherwise, go to Step 2.2; Step 2.2: Use Formula (1) and Formula (2) to calculate the bottleneck index of each process, and determine whether this process is the process with the highest bottleneck index. If so, go to Step 2.3; otherwise, go to Step 2.4; c s = T s - F s (t) (2) Among which I BN is the bottleneck index, w t , w b , w q are the influence weights of the number of products produced in the process, the process buffer, and the product quality on the bottleneck degree respectively, and satisfy w t + w b + w q = 1, c s and l s are the production capacity and production load respectively, T s is the assumed available processing capacity, F s (t) is the quantity by which the production capacity of process s changes due to changes in actual production conditions, and are the maximum bearable quantity of the buffer and the newly added quantity of products in the buffer respectively, is the influence function of the quality assurance ability (q ac ) on the bottleneck degree, (q ac ) is a comprehensive reflection of the quality ability and quality requirements; Step 2.3: Record this process as a bottleneck process and go to Step 2.5; Step 2.4: Record this process as a non-bottleneck process and go to Step 2.5; Step 2.5: Determine whether this process is the last process. If so, go to Step 2.6; otherwise, go to Step 2.1; Step 2.6: End the judgment and output all bottleneck processes to the scheduling module; Step 3: The GUI module and the bottleneck identification module respectively transmit the workshop scheduling data and the bottleneck process identification result to the scheduling module; Step 4: The intelligent scheduling sub-module establishes a bottleneck process scheduling model for the semiconductor packaging and testing workshop, uses the improved artificial hummingbird algorithm (IAHA) to schedule the bottleneck processes, and inputs the obtained bottleneck process scheduling plan to the rule-based scheduling sub-module; Step 5: According to the rule library, the user selects rules for each process in advance. Combining the bottleneck process scheduling plan obtained by the intelligent scheduling sub-module, the rule-based scheduling sub-module generates a global scheduling plan and transmits it to the GUI module to arrange production.

2. The intelligent scheduling method for a fast-response semiconductor packaging test workshop according to claim 1, wherein The specific operation of Step 4 is: Step 4.1: Establish a bottleneck process scheduling model for the semiconductor packaging and testing workshop, which includes all bottleneck processes and the processes between bottlenecks; Step 4.2: Optimize the bottleneck process workshop scheduling model through the improved artificial hummingbird algorithm; Step 4.2.1: Initialize the population through the improved NEH heuristic rule instead of randomly generating the initial population; Step 4.2.2: Initialize the food source access table; Step 4.2.3: Set the maximum number of iterations as Iteration and use IAHA for optimization search; Step 4.2.4: Improve the foraging method through the foraging judgment formula, replacing the 50% probability for each of the guided foraging method or the neighborhood foraging method; Step 4.2.5: Judge the number of iterations. If the number of iterations is a multiple of the preset value n of the migration coefficient, perform enhanced tabu neighborhood foraging; Step 4.2.6: Judge the number of iterations. If the number of iterations exceeds the predetermined value 2n of the migration coefficient, perform migratory foraging; Step 4.2.7: Output the individual with the optimal fitness value, that is, the optimal plan for bottleneck process scheduling; Step 4.3: The intelligent scheduling sub-module outputs the bottleneck process scheduling plan.

3. The intelligent scheduling method for the fast-response semiconductor packaging test workshop according to claim 2, wherein, The specific operation of Step 4.2.1 is: Step 4.2.1.1: Calculate the total processing time of all orders j ∈ 2...n, that is, the sum of the ratios of the processing times of each stage of the order to the sum of the processing speeds of each stage; in accordance with TP j Arrange the orders in non-increasing order to obtain the initial arrangement π 0 ={π 0 (1), π 0 (2), …, π 0 (n)}; Step 4.2.1.2: Take out π 0 The first two orders of π 0 (1) and π 0 (2), sorting them can obtain these two possible schedules {π 0 (1), π 0 (2)} and {π 0 (2), π 0 (1)}; Evaluate these two partial schedules, and take the one with the smaller maximum completion time as the current schedule, denoted as π = {π(1), π(2)}; Step 4.2.1.3: Take out π 0 The j-th order π 0 (j), insert it into all possible positions of π, obtaining a total of j partial permutations; evaluate the obtained partial permutations, and take the partial permutation with the minimum maximum completion time as the current schedule π; Step 4.2.1.4: Let \(j = j + 1\); if \(j\leq n - 1\), go to Step 4.2.1.3; otherwise, output the current schedule \(\pi\). Step 4.2.1.5: Based on the current schedule \(\pi=\{\pi(1),\pi(2),\cdots,\pi(n)\}\), randomly select integers \(l\) and \(m\) such that \(l < m\leq n\), and swap the order of the \(l\)-th order and the \(m\)-th order in the current schedule \(\pi\) to obtain a new individual. Step 4.2.1.6: Repeat Step 4.2.1.5 until a set of Hummingbird individuals with a quantity of Popsize / 2 is generated. Step 4.2.1.7: Use the result of the improved NEH heuristic algorithm to directly generate Popsize / 2 duplicate Hummingbird individuals, and merge them with the individual set generated in Step 4.2.1.6 to obtain the initial population.

4. The intelligent scheduling method for a fast-response semiconductor packaging test workshop according to claim 3, wherein The specific operation of Step 4.2.4 is as follows: Step 4.2.4.1: Select the foraging method through the foraging judgment formula. Replace the original 50% probability in AHA with the foraging judgment formula to select guided foraging and territorial foraging; it is beneficial to increase the selection ratio of guided foraging in the early stage of iteration to enhance the ability to explore other spaces, and increase the selection of territorial foraging in the later stage of iteration to enhance the ability to find local optimal solutions. where GT is the foraging judgment coefficient; GT max and GT min are the maximum foraging judgment coefficient and the minimum foraging judgment coefficient respectively, and the value range is Popsize is the population size, Popsize ≥mean is the number of individuals in the population whose fitness value is greater than or equal to the average fitness value of the population; it is the iteration number; Iteration is the maximum iteration number; v i (t + 1) is the candidate food source position of the i-th hummingbird at time t + 1; f Guided (v i,tar (t), x i (t)) is used to guide the foraging search, and x i (t) is the position of the food source of the i-th hummingbird at time t, and v i,tar (t) is the position of the target food source that the i-th hummingbird intends to visit; f Territorial (x i (t)) is the patch foraging search; Step 4.2.4.2: The hummingbird searches for a better food source through three flight methods. When the generated random number \(rand\geq GT\), the hummingbird chooses to conduct guided foraging. The three flight methods of guided foraging are: extracting a processing position to swap two orders, extracting multiple consecutive positions to swap two order blocks, and extracting multiple positions to swap multiple orders. When the generated random number \(rand < GT\), the hummingbird chooses to conduct territorial foraging. The three flight methods of territorial foraging are: extracting two positions to swap two orders, extracting multiple consecutive positions to swap orders within the block, and extracting multiple positions to swap multiple orders. Step 4.2.4.3: Update the population and the access table. The update of the \(i\)-th food source position is as follows: The fitness value represented by the hummingbird in the formula fitness(*). If the fitness of the candidate food source is lower than that of the current food source, the hummingbird abandons the current food source and stays at the newly generated candidate food source v i (t + 1) Feeding; update the access table.

5. The intelligent scheduling method for a fast-response semiconductor packaging test workshop according to claim 4, wherein The specific operation of Step 4.2.5 is as follows: Step 4.2.5.1: By taking a random integer randint as the number of hummingbirds for enhanced foraging, where randint ∈ [2, 3, 4, 5], calculate the number of times RS for each hummingbird to perform enhanced foraging num , RS num = Popsize / randint; Step 4.2.5.2: Screen the territorial foraging hummingbirds for enhancement through roulette wheel selection. Step 4.2.5.3: Perform RS on each selected hummingbird in sequence num times of enhanced taboo area foraging. The enhanced taboo area foraging method adds a taboo list on the basis of area foraging to ensure that the search results of each enhanced taboo area foraging are different; Step 4.2.5.4: Update the population and the access table.

6. The intelligent scheduling method for the fast-response semiconductor packaging test workshop according to claim 5, wherein The specific operation of Step 4.2.6 is as follows: Step 4.2.6.1: Select the hummingbird with the worst fitness value and let this hummingbird migrate to the position of the hummingbird with the best fitness value that has been discovered. Step 4.2.6.2: Update the access table.

7. The intelligent scheduling method for a fast-response semiconductor packaging test workshop according to claim 3, wherein The specific operation of Step 5 is as follows: The heuristic rule base of the rule scheduling sub-module includes a heuristic processing sequence rule base and a heuristic equipment unit selection rule base. The processing sequence rule base includes the rule of giving priority to the shorter processing time, the rule of giving priority to the longer processing time, the rule of processing the earliest arrived first, and the rule of giving priority to the earlier due date of the operation. The equipment unit selection rule base includes the rule of selecting the equipment unit with the shortest processing time, the rule of selecting the equipment unit with the shortest changeover time, the rule of selecting the equipment unit with the highest accuracy, the rule of selecting the equipment unit with the lowest accuracy, the rule of selecting the equipment unit with the most processable order types, and the rule of selecting the equipment unit with the fewest processable order types. Step 5.1: The user selects the corresponding heuristic rules for each process in advance; Step 5.2: Use the processing sequence rules and equipment unit selection rules selected for each process to generate an overall scheduling plan; Step 5.3: Transmit the generated global scheduling plan to the GUI module to arrange production.

8. An intelligent scheduling system for a fast-response semiconductor packaging and testing workshop, characterized in that, It includes three parts: a graphical user interface GUI module, a bottleneck identification module, and a scheduling module; It is used to complete the intelligent scheduling method for the fast-response semiconductor packaging and testing workshop described in any one of claims 1-7; The GUI module is used to transmit workshop operation data, workshop scheduling data, and receive the global scheduling plan; The bottleneck identification module identifies all bottleneck processes through the "buffer-bottleneck index" bottleneck identification method; The scheduling module includes an intelligent scheduling sub-module and a rule scheduling sub-module. The intelligent scheduling sub-module solves the bottleneck process scheduling problem through an improved artificial hummingbird algorithm to generate a bottleneck scheduling plan. The rule scheduling sub-module generates a global scheduling plan by the user independently selecting rules from the rule library and combining the bottleneck scheduling plan.

9. The intelligent scheduling system for a fast-response semiconductor packaging test workshop according to claim 8, wherein, It also includes a memory and a processor. The memory stores a computer program. When the processor executes the computer program, it executes the "buffer-bottleneck index" bottleneck identification method of the bottleneck identification module, the IAHA algorithm of the intelligent scheduling sub-module in the scheduling module, and the rule scheduling sub-module in the scheduling module.

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