A production scheduling method and device for laser processing tasks of hard materials

Abstract

This application proposes a scheduling method and device for laser processing tasks of hard materials, including the following steps: obtaining the ingot data and equipment data to be processed; taking the processing sequence decision, processing start time decision, processing end time decision and equipment allocation decision as decision variables, minimizing the total processing time as the optimization goal, and using the wafer processing sequence constraints, equipment occupancy constraints, processing end time constraints and relationship constraints between decision variables as constraints to construct a laser processing process scheduling model; solving the laser processing process scheduling model to obtain a scheduling plan and deploying the scheduling plan. This plan is based on the particularity of the laser processing process to formulate wafer processing sequence constraints that meet the characteristics of repeated processing of ingots and equipment allocation decisions that meet resource sharing and heterogeneous characteristics, so as to obtain a scheduling plan to improve the efficiency of laser processing.

Description

Technical Field

[0001] The present application relates to the field of intelligent manufacturing and production scheduling, and in particular to a method and device for scheduling hard material processing tasks. Background Art

[0002] With the continuous development of intelligent manufacturing technology in the semiconductor manufacturing industry, intelligent scheduling technology is gradually becoming an important means to improve production efficiency and management level. Intelligent scheduling technology combines actual production goals, reduces non-production time and shortens production cycle through accurate prediction and flexible scheduling; at the same time, intelligent scheduling technology provides comprehensive production data, enabling managers to have in-depth insights into the entire production process, provide strong support for decision-making, and conduct refined management of the entire production plan. However, the existing intelligent scheduling technology still has the following problems in the actual implementation of laser processing scenarios of hard materials such as semiconductor wafers:

[0003] 1. Since the laser processing of hard materials such as ingots needs to go through a series of processing stages such as laser modification, peeling and grinding, there is one or more processing machines in each stage and all hard materials must go through each processing stage in the same order. Therefore, production scheduling during the laser processing process involves a series of stages such as processing sequence and resource selection at different stages. Moreover, due to the mutual coupling between the stages, it is impossible to make reasonable scheduling decisions.

[0004] 2. Since a crystal ingot produces a round wafer during one laser processing, each crystal ingot needs to be processed repeatedly during the laser processing until the entire round wafer is consumed. Therefore, due to the characteristics of repeated processing of crystal ingots, the round wafers produced on the same crystal ingot have a sequence, which is different from conventional manufacturing industries, so production scheduling cannot be carried out in a conventional manner.

[0005] 3. In the laser processing process, since different machines share resources in each laser processing stage but the operating efficiency of each machine is different, the processing time of different machines will be different. Therefore, the sharing and heterogeneity of resources further aggravate the difficulty of optimizing production scheduling.

[0006] In summary, the existing intelligent production scheduling methods cannot improve the processing efficiency of the laser processing production line of crystal ingots. How to improve the applicability of intelligent production scheduling in the laser processing scenario of hard materials is a difficult problem that needs to be solved urgently. Summary of the invention

[0007] The embodiments of the present application provide a production scheduling method and device for laser processing tasks of hard materials. Based on the particularities of the laser processing process, wafer processing sequence constraints that meet the characteristics of repeated processing of ingots and equipment allocation decisions that meet resource sharing and heterogeneous characteristics are formulated, thereby obtaining a production scheduling plan to improve the efficiency of laser processing.

[0008] In a first aspect, an embodiment of the present application provides a production scheduling method for laser processing tasks of hard materials, the method comprising:

[0009] Obtaining data on the ingots to be processed and equipment data, wherein the data on the ingots to be processed includes the number of ingots, the number of wafers that can be processed by each ingot, and the total number of stages required for laser processing; the equipment data includes the number of equipment in each laser processing stage, and the processing time for each equipment to complete a processing task;

[0010] A laser processing process scheduling model is constructed with processing sequence decision, processing start time decision, processing end time decision and equipment allocation decision as decision variables, minimizing total processing time as optimization goal, and wafer processing sequence constraint, equipment occupancy constraint, processing end time constraint and relationship constraint between decision variables as constraint conditions, wherein processing sequence decision, processing start time decision, processing end time decision and equipment allocation decision are formulated according to the ingot data to be processed and the equipment data, the wafer processing sequence constraint is used to ensure that each wafer completes all processing stages, the equipment occupancy constraint is used to ensure that each device can only process one wafer in a single thread, the processing end time constraint is used to ensure that the processing end time is the sum of the processing start time and the corresponding processing time, and the relationship constraint between decision variables is used to ensure that in the same processing stage, when the round wafers on different ingots are allocated to the same device for processing, there must be a sequence;

[0011] The production scheduling model of the laser machining process is solved to obtain the production scheduling plan and then the production scheduling plan is deployed.

[0012] In a second aspect, an embodiment of the present application provides a production scheduling device for laser processing tasks of hard materials, including:

[0013] A data acquisition module is used to obtain the ingot data and equipment data to be processed, wherein the ingot data to be processed includes the number of ingots, the number of wafers that can be processed by each ingot, and the total number of stages required for laser processing; the equipment data includes the number of equipment in each laser processing stage and the processing time for each equipment to complete a processing task;

[0014] The laser processing process scheduling model construction module takes the processing sequence decision, processing start time decision, processing end time decision and equipment allocation decision as decision variables, takes minimizing the total processing time as the optimization goal, and takes the wafer processing sequence constraint, equipment occupancy constraint, processing end time constraint and the relationship constraint between decision variables as constraint conditions to construct the laser processing process scheduling model, wherein the processing sequence decision, processing start time decision, processing end time decision and equipment allocation decision are formulated according to the ingot data to be processed and the equipment data, the wafer processing sequence constraint is used to ensure that each wafer completes all processing stages, the equipment occupancy constraint is used to ensure that each device can only process one wafer in a single thread, the processing end time constraint is used to ensure that the processing end time is the sum of the processing start time and the corresponding processing time, and the relationship constraint between the decision variables is used to ensure that in the same processing stage, when the round wafers on different ingots are allocated to the same device for processing, there must be a sequence;

[0015] The model solving module is used to solve the production scheduling model of the laser processing process to obtain the production scheduling plan and deploy the production scheduling plan.

[0016] In a third aspect, an embodiment of the present application provides an electronic device, including a memory and a processor, wherein the memory stores a computer program, and the processor is configured to run the computer program to execute a production scheduling method for hard material laser processing tasks.

[0017] In a fourth aspect, an embodiment of the present application provides a readable storage medium, in which a computer program is stored. The computer program includes a program code for controlling a process to execute a process, and the process includes a production scheduling method for hard material laser processing tasks.

[0018] The main contributions and innovations of the present invention are as follows:

[0019] The embodiment of the present application uses processing sequence decision, processing start time decision, processing end time decision and equipment allocation decision as decision variables according to the particularity of repeated processing and resource sharing in the ingot laser processing process, and constructs a laser processing process scheduling model with wafer processing sequence constraints, equipment occupancy constraints, processing end time constraints and relationship constraints between decision variables as constraints, so as to reasonably arrange the sequence of processing tasks and allocate resources, thereby improving the efficiency of the laser processing process.

[0020] Details of one or more embodiments of the present application are set forth in the following drawings and description to make other features, objects, and advantages of the present application more readily apparent. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] The drawings described herein are used to provide a further understanding of the present application and constitute a part of the present application. The illustrative embodiments of the present application and their descriptions are used to explain the present application and do not constitute an improper limitation on the present application. In the drawings:

[0022] Figure 1 is a flow chart of a production scheduling method for hard material laser processing tasks according to an embodiment of the present application;

[0023] Figure 2 It is a structural block diagram of a production scheduling device for hard material laser processing tasks according to an embodiment of the present application;

[0024] Figure 3 It is a schematic diagram of the hardware structure of an electronic device according to an embodiment of the present application. DETAILED DESCRIPTION

[0025] Exemplary embodiments will be described in detail herein, examples of which are shown in the accompanying drawings. When the following description refers to the drawings, the same numbers in different drawings represent the same or similar elements unless otherwise indicated. The implementations described in the following exemplary embodiments do not represent all implementations consistent with one or more embodiments of this specification. Instead, they are merely examples of devices and methods consistent with some aspects of one or more embodiments of this specification as detailed in the appended claims.

[0026] It should be noted that: in other embodiments, the steps of the corresponding method are not necessarily performed in the order shown and described in this specification. In some other embodiments, the steps included in the method may be more or less than those described in this specification. In addition, a single step described in this specification may be decomposed into multiple steps for description in other embodiments; and multiple steps described in this specification may be combined into a single step for description in other embodiments.

[0027] Embodiment 1

[0028] The embodiment of the present application provides a scheduling method for laser processing tasks of hard materials. Based on the particularity of the laser processing process, a wafer processing sequence constraint that meets the characteristics of repeated processing of ingots and an equipment allocation decision that meets the characteristics of resource sharing and heterogeneity are formulated, thereby obtaining a scheduling plan to improve the efficiency of laser processing. Specifically, reference is made to Figure 1 , the method comprising:

[0029] Obtaining data on the ingots to be processed and equipment data, wherein the data on the ingots to be processed includes the number of ingots, the number of wafers that can be processed by each ingot, and the total number of stages required for laser processing; the equipment data includes the number of equipment in each laser processing stage, and the processing time for each equipment to complete a processing task;

[0030] A laser processing process scheduling model is constructed with processing sequence decision, processing start time decision, processing end time decision and equipment allocation decision as decision variables, minimizing total processing time as optimization goal, and wafer processing sequence constraint, equipment occupancy constraint, processing end time constraint and relationship constraint between decision variables as constraint conditions, wherein processing sequence decision, processing start time decision, processing end time decision and equipment allocation decision are formulated according to the ingot data to be processed and the equipment data, the wafer processing sequence constraint is used to ensure that each wafer completes all processing stages, the equipment occupancy constraint is used to ensure that each device can only process one wafer in a single thread, the processing end time constraint is used to ensure that the processing end time is the sum of the processing start time and the corresponding processing time, and the relationship constraint between decision variables is used to ensure that in the same processing stage, when the round wafers on different ingots are allocated to the same device for processing, there must be a sequence;

[0031] The production scheduling model of the laser machining process is solved to obtain the production scheduling plan and then the production scheduling plan is deployed.

[0032] In this solution, the number of round wafers that can be processed from each crystal ingot is obtained by dividing the thickness of the crystal ingot by the wafer consumption required to process each round wafer.

[0033] In some specific embodiments, the laser processing task includes three stages: laser modification of the ingot, separation of the ingot from the wafer, and grinding of the ingot from the wafer. The wafer consumption required for grinding each round wafer refers to the loss of the ingot to generate a wafer after going through the above three stages.

[0034] In this solution, the processing time for each machine to complete a processing task is the average value of the historical processing time of the machine executing the corresponding processing task.

[0035] The processing scheduling model used in this scheme is a mathematical model, so the processing sequence decision, processing start time decision, processing end time decision and equipment allocation decision are made after the ingot data to be processed and the equipment data are mathematically expressed.

[0036] Specifically, the mathematical expression is to abstract the data of the ingot to be processed and the equipment data into parameters, calculate the function in the form of parameters, and make various decisions.

[0037] In some specific embodiments, i represents the number of the ingot to be processed, l represents the number of the wafer, k represents the processing stage, j represents the equipment number, N represents the number of ingots to be processed, represents the number of wafers that can be processed from ingot i, S represents the total number of stages required for laser processing, represents the number of devices in the kth stage, It represents the processing time of the lth wafer of ingot i on the kth stage processing equipment j.

[0038] Furthermore, the processing sequence decision is used to determine the processing sequence between wafers on different ingots in the same processing stage, and the processing sequence decision is a 0 / 1 variable. For example, the processing sequence decision variable Ingot No. Whether the wafer precedes the ingot at stage k No. Wafer processing, if yes, then 1, if not, 0.

[0039] Furthermore, the processing start time decision and the processing end time decision respectively represent the processing start time and processing end time of each wafer at different stages, and the processing start time decision and the processing end time decision are continuous variables. For example, the processing start time decision It is used to indicate the processing start time and processing end time decision of the lth wafer of ingot i in stage k. Used to indicate the processing completion time of the lth wafer of ingot i in stage k.

[0040] Furthermore, the equipment allocation decision is used to determine which equipment each wafer on different ingots is allocated to in different processing stages, and the equipment allocation decision is a 0 / 1 variable. For example, the equipment allocation decision Used to indicate whether the lth wafer of ingot i is processed on equipment j at stage k. If so, it is 1, otherwise it is 0.

[0041] In this solution, the minimum total processing time is calculated by the following formula:

[0042]

[0043] in, It is used to select the latest processing end time among all processing stages. It is used to ensure The value of is the smallest, thus minimizing the total processing time.

[0044] In this scheme, wafer processing sequence constraints are constructed by constraining that any wafer can only start the processing task of the current processing stage after completing the processing task of the previous processing stage, and constraining that any wafer can only start the processing of the next wafer after completing the processing task of the last processing stage.

[0045] Specifically, the formula for the wafer processing sequence constraint is expressed as:

[0046]

[0047] This formula indicates that when the lth wafer of ingot i is processed on equipment j at stage k, , then the start time of stage k for the first wafer of ingot i is Must be later than the end time of stage (k-1) of wafer l of ingot i .

[0048]

[0049] This formula indicates that when the final stage S of the (l-1)th wafer of ingot i is processed on equipment j, , then the start time of phase 1 of the first wafer of ingot i is must be later than the end time of stage S of the (l-1)th wafer of ingot i .

[0050] In this solution, the formula for the equipment occupancy constraint is as follows:

[0051]

[0052] This formula indicates that because the ingot No. The wafer only exists before or after the ingot at stage k. No. Wafer processing, so and At most one of them is 1.

[0053] Wherein, L is a sufficiently large constant. In some specific embodiments, L is 1,000,000, because , , are all 0 / 1 variables, so The value of = 0 or >= 1, when When the value is >= 1, The value of is extremely large. Not constraining, when When the value of is 0, The value of is also 0, and the constraint takes effect, that is, 0>= .

[0054] In this solution, the formula of the processing end time constraint is expressed as follows:

[0055]

[0056] This formula indicates that when the lth wafer of ingot i is processed on equipment j at stage k, , then the end time of stage k of wafer l of ingot i is Start time Add the time that the first wafer of ingot i spends on equipment j at stage k .

[0057] In this solution, the formula for the relationship constraint between the decision variables is expressed as follows:

[0058]

[0059] This formula is only valid for It works when the crystal ingot No. Bulk Wafers and Ingots No. Each wafer is processed on equipment j at stage k, because equipment j can only process one wafer at a time, so the ingot No. Bulk Wafers and Ingots No. The block must have one 1 and the other 0, so the wafer .

[0060] The specific parameters in the above formula have been explained in detail in the previous text and will not be repeated here.

[0061] In this solution, a solver is used to solve the constructed laser machining process scheduling model, and a solution time threshold is set. If the optimal solution cannot be obtained within the set solution time threshold, the optimal solution is selected from the solutions that have been obtained for output.

[0062] Exemplarily, solvers such as CLPEX and Gurobi are used to compile the formulated decision variables, objective functions, and constraints, and the ingot data to be processed and the equipment data are input into the solver to obtain a production scheduling plan.

[0063] Embodiment 2

[0064] Based on the same idea, refer to Figure 2 , the present application also proposes a production scheduling device for laser processing tasks of hard materials, including:

[0065] A data acquisition module is used to obtain the ingot data and equipment data to be processed, wherein the ingot data to be processed includes the number of ingots, the number of wafers that can be processed by each ingot, and the total number of stages required for laser processing; the equipment data includes the number of equipment in each laser processing stage and the processing time for each equipment to complete a processing task;

[0066] The laser processing process scheduling model construction module takes the processing sequence decision, processing start time decision, processing end time decision and equipment allocation decision as decision variables, takes minimizing the total processing time as the optimization goal, and takes the wafer processing sequence constraint, equipment occupancy constraint, processing end time constraint and the relationship constraint between decision variables as constraint conditions to construct the laser processing process scheduling model, wherein the processing sequence decision, processing start time decision, processing end time decision and equipment allocation decision are formulated according to the ingot data to be processed and the equipment data, the wafer processing sequence constraint is used to ensure that each wafer completes all processing stages, the equipment occupancy constraint is used to ensure that each device can only process one wafer in a single thread, the processing end time constraint is used to ensure that the processing end time is the sum of the processing start time and the corresponding processing time, and the relationship constraint between the decision variables is used to ensure that in the same processing stage, when the round wafers on different ingots are allocated to the same device for processing, there must be a sequence;

[0067] The model solving module is used to solve the production scheduling model of the laser processing process to obtain the production scheduling plan and deploy the production scheduling plan.

[0068] Embodiment 3

[0069] This embodiment also provides an electronic device, referring to Figure 3 , comprises a memory 404 and a processor 402, wherein the memory 404 stores a computer program, and the processor 402 is configured to run the computer program to execute the steps in any of the above method embodiments.

[0070] Specifically, the processor 402 may include a central processing unit (CPU), or an application specific integrated circuit (ASIC), or may be configured to implement one or more integrated circuits of the embodiments of the present application.

[0071] Among them, the memory 404 may include a large capacity memory 404 for data or instructions. For example, but not limitation, the memory 404 may include a hard disk drive (HDD), a floppy disk drive, a solid state drive (SSD), a flash memory, an optical disk, a magneto-optical disk, a tape, or a universal serial bus (USB) drive, or a combination of two or more of these. In appropriate cases, the memory 404 may include a removable or non-removable (or fixed) medium. In appropriate cases, the memory 404 may be inside or outside the data processing device. In a specific embodiment, the memory 404 is a non-volatile memory. In a specific embodiment, the memory 404 includes a read-only memory (ROM) and a random access memory (RAM). Where appropriate, the ROM may be a mask-programmed ROM, a programmable ROM (Programmable Read-Only Memory, PROM for short), an erasable PROM (Erasable Programmable Read-Only Memory, EPROM for short), an electrically erasable PROM (Electrically Erasable Programmable Read-Only Memory, EEPROM for short), an electrically alterable ROM (Electrically Alterable Read-Only Memory, EAROM for short) or a flash memory (FLASH) or a combination of two or more of these. In appropriate circumstances, the RAM may be a static random access memory (SRAM) or a dynamic random access memory (DRAM), wherein the DRAM may be a fast page mode dynamic random access memory 404 (FPMDRAM), an extended data output dynamic random access memory (EDODRAM), a synchronous dynamic random access memory (SDRAM), etc.

[0072] The memory 404 may be used to store or cache various data files required for processing and / or communication, as well as possible computer program instructions executed by the processor 402 .

[0073] The processor 402 reads and executes the computer program instructions stored in the memory 404 to implement any one of the scheduling methods for hard material laser processing tasks in the above embodiments.

[0074] Optionally, the electronic device may further include a transmission device 406 and an input / output device 408 , wherein the transmission device 406 is connected to the processor 402 , and the input / output device 408 is connected to the processor 402 .

[0075] The transmission device 406 can be used to receive or send data via a network. Specific examples of the above-mentioned network may include a wired or wireless network provided by a communication provider of the electronic device. In one example, the transmission device includes a network adapter (Network Interface Controller, referred to as NIC), which can be connected to other network devices through a base station so as to communicate with the Internet. In one example, the transmission device 406 can be a radio frequency (Radio Frequency, referred to as RF) module, which is used to communicate with the Internet wirelessly.

[0076] The input / output device 408 is used to input or output information. In this embodiment, the input information may be the ingot data to be processed and the equipment data, etc., and the output information may be the production scheduling plan, etc.

[0077] Optionally, in this embodiment, the processor 402 may be configured to perform the following steps through a computer program:

[0078] Obtaining data on the ingots to be processed and equipment data, wherein the data on the ingots to be processed includes the number of ingots, the number of wafers that can be processed by each ingot, and the total number of stages required for laser processing; the equipment data includes the number of equipment in each laser processing stage, and the processing time for each equipment to complete a processing task;

[0079] A laser processing process scheduling model is constructed with processing sequence decision, processing start time decision, processing end time decision and equipment allocation decision as decision variables, minimizing total processing time as optimization goal, and wafer processing sequence constraint, equipment occupancy constraint, processing end time constraint and relationship constraint between decision variables as constraint conditions, wherein processing sequence decision, processing start time decision, processing end time decision and equipment allocation decision are formulated according to the ingot data to be processed and the equipment data, the wafer processing sequence constraint is used to ensure that each wafer completes all processing stages, the equipment occupancy constraint is used to ensure that each device can only process one wafer in a single thread, the processing end time constraint is used to ensure that the processing end time is the sum of the processing start time and the corresponding processing time, and the relationship constraint between decision variables is used to ensure that in the same processing stage, when the round wafers on different ingots are allocated to the same device for processing, there must be a sequence;

[0080] The production scheduling model of the laser machining process is solved to obtain the production scheduling plan and then the production scheduling plan is deployed.

[0081] It should be noted that the specific examples in this embodiment can refer to the examples described in the above embodiments and optional implementation modes, and this embodiment will not be described in detail here.

[0082] In general, various embodiments may be implemented in hardware or dedicated circuits, software, logic, or any combination thereof. Some aspects of the invention may be implemented in hardware, while other aspects may be implemented in firmware or software that may be executed by a controller, microprocessor, or other computing device, but the invention is not limited thereto. Although various aspects of the invention may be shown and described as block diagrams, flow charts, or using some other graphical representation, it should be understood that, as non-limiting examples, the boxes, devices, systems, techniques, or methods described herein may be implemented in hardware, software, firmware, dedicated circuits or logic, general-purpose hardware or controllers or other computing devices, or some combination thereof.

[0083] Embodiments of the present invention may be implemented by computer software that is executable by a data processor of a mobile device, such as in a processor entity, or by hardware, or by a combination of software and hardware. Computer software or programs (also referred to as program products) including software routines, applets and / or macros may be stored in any device-readable data storage medium, and they include program instructions for performing specific tasks. A computer program product may include one or more computer executable components configured to perform an embodiment when the program is run. One or more computer executable components may be at least one software code or a portion thereof. In addition, at this point, it should be noted that, for example, Figure 3Any block of the logic flow in the program may represent program steps, or interconnected logic circuits, blocks and functions, or a combination of program steps and logic circuits, blocks and functions. The software may be stored on physical media such as memory chips or storage blocks implemented within a processor, magnetic media such as hard disks or floppy disks, and optical media such as, for example, DVDs and their data variants, CDs, etc. Physical media are non-transitory media.

[0084] Those skilled in the art should understand that the technical features of the above embodiments may be arbitrarily combined. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0085] The above embodiments only express several implementation methods of the present application, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the scope of the present application. It should be pointed out that, for a person of ordinary skill in the art, several variations and improvements can be made without departing from the concept of the present application, and these all belong to the protection scope of the present application. Therefore, the protection scope of the present application shall be subject to the attached claims.

Claims

1. A production scheduling method for laser processing tasks of hard materials. It is characterized in that The following steps are involved: Obtaining data on the ingots to be processed and equipment data, wherein the data on the ingots to be processed includes the number of ingots, the number of wafers that can be processed by each ingot, and the total number of stages required for laser processing; the equipment data includes the number of equipment in each laser processing stage, and the processing time for each equipment to complete a processing task; A laser processing process scheduling model is constructed with processing sequence decision, processing start time decision, processing end time decision and equipment allocation decision as decision variables, minimizing total processing time as optimization goal, and wafer processing sequence constraint, equipment occupancy constraint, processing end time constraint and relationship constraint between decision variables as constraint conditions. The laser processing process scheduling model is a mathematical model, in which the ingot data to be processed and the equipment data are abstracted into parameters, i represents the ingot number to be processed, l represents the wafer number, k represents the processing stage, j represents the equipment number, N represents the number of ingots to be processed, represents the number of wafers that can be processed from ingot i, S represents the total number of stages required for laser processing, represents the number of devices in the kth stage, represents the processing time of the lth wafer of ingot i on processing equipment j in the kth stage. According to the ingot data to be processed and the equipment data, the processing sequence decision, processing start time decision, processing end time decision and equipment allocation decision are made. The processing sequence decision variable Ingot No. Whether the wafer precedes the ingot at stage k No. Wafer processing, processing start time decision It is used to indicate the processing start time and processing end time decision of the lth wafer of ingot i in stage k. It is used to indicate the processing end time of the first wafer of ingot i in stage k, and the equipment allocation decision It is used to indicate whether the first wafer of ingot i is processed on device j at stage k. The wafer processing sequence constraint is used to ensure that each wafer completes all processing stages. The device occupancy constraint is used to ensure that each device can only process one wafer in a single thread. The processing end time constraint is used to ensure that the processing end time is the sum of the processing start time and the corresponding processing time. The relationship constraint between decision variables is used to ensure that there must be a sequence when wafers on different ingots are assigned to the same device for processing in the same processing stage. The production scheduling model of the laser processing process is solved to obtain a production scheduling plan and deploy the production scheduling plan, wherein a solver is used to compile the formulated decision variables, objective functions and constraints, and the ingot data to be processed and the equipment data are input into the solver to obtain the production scheduling plan.

2. A production scheduling method for laser processing tasks of hard materials according to claim 1, It is characterized in that The number of round wafers that can be processed from each ingot is obtained by dividing the thickness of the ingot by the wafer consumption required to process each round wafer. The processing time for each machine to complete a processing task is the average of the historical processing time of the machine to perform the corresponding processing task.

3. A production scheduling method for laser processing tasks of hard materials according to claim 1, It is characterized in that The processing sequence decision is used to determine the processing sequence between wafers on different ingots in the same processing stage, and the processing sequence decision is a 0 / 1 variable.

4. A production scheduling method for laser processing tasks of hard materials according to claim 1, It is characterized in that The processing start time decision and the processing end time decision respectively represent the processing start time and the processing end time of each wafer at different stages, and the processing start time decision and the processing end time decision are continuous variables.

5. The method for scheduling hard material laser processing tasks according to claim 1, It is characterized in that The equipment allocation decision is used to determine to which equipment each wafer on different ingots is allocated in different processing stages, and the equipment allocation decision is a 0 / 1 variable.

6. A production scheduling method for laser processing tasks of hard materials according to claim 1, It is characterized in that Wafer processing sequence constraints are constructed by constraining that any wafer can only start the processing task of the current processing stage after completing the processing task of the previous processing stage, and constraining that any wafer can only start the processing of the next wafer after completing the processing task of the last processing stage.

7. A production scheduling device for laser processing tasks of hard materials, It is characterized in that include: A data acquisition module is used to obtain the ingot data and equipment data to be processed, wherein the ingot data to be processed includes the number of ingots, the number of wafers that can be processed by each ingot, and the total number of stages required for laser processing; the equipment data includes the number of equipment in each laser processing stage and the processing time for each equipment to complete a processing task; The laser processing process scheduling model construction module takes processing sequence decision, processing start time decision, processing end time decision and equipment allocation decision as decision variables, takes minimizing the total processing time as the optimization goal, and takes wafer processing sequence constraint, equipment occupancy constraint, processing end time constraint and relationship constraint between decision variables as constraint conditions to construct the laser processing process scheduling model. The laser processing process scheduling model is a mathematical model, in which the ingot data to be processed and the equipment data are abstracted into parameters, i represents the ingot number to be processed, l represents the wafer number, k represents the processing stage, j represents the equipment number, N represents the number of ingots to be processed, represents the number of wafers that can be processed from ingot i, S represents the total number of stages required for laser processing, represents the number of devices in the kth stage, represents the processing time of the lth wafer of ingot i on processing equipment j in the kth stage. According to the ingot data to be processed and the equipment data, the processing sequence decision, processing start time decision, processing end time decision and equipment allocation decision are made. The processing sequence decision variable Ingot No. Whether the wafer precedes the ingot at stage k No. Wafer processing, processing start time decision It is used to indicate the processing start time and processing end time decision of the lth wafer of ingot i in stage k. It is used to indicate the processing end time of the first wafer of ingot i in stage k, and the equipment allocation decision It is used to indicate whether the first wafer of ingot i is processed on device j at stage k. The wafer processing sequence constraint is used to ensure that each wafer completes all processing stages. The device occupancy constraint is used to ensure that each device can only process one wafer in a single thread. The processing end time constraint is used to ensure that the processing end time is the sum of the processing start time and the corresponding processing time. The relationship constraint between decision variables is used to ensure that there must be a sequence when wafers on different ingots are assigned to the same device for processing in the same processing stage. The model solving module is used to solve the laser processing scheduling model to obtain a scheduling plan and deploy the scheduling plan. The solver is used to compile the formulated decision variables, objective functions and constraints, and the ingot data and equipment data to be processed are input into the solver to obtain the scheduling plan.

8. An electronic device comprising a memory and a processor, It is characterized in that A computer program is stored in the memory, and the processor is configured to run the computer program to execute a production scheduling method for hard material laser processing tasks as described in any one of claims 1-6.

9. A readable storage medium, It is characterized in that A computer program is stored in the readable storage medium, and the computer program includes a program code for controlling a process to execute a process, and the process includes a production scheduling method for hard material laser processing tasks according to any one of claims 1-6.

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