Material blending method and device of semiconductor workshop based on EMS system, and medium

Abstract

The invention discloses a semiconductor workshop material allocation method and device based on an EMS system, and a medium. The method comprises the steps of obtaining actual operation parameters and historical operation data of a production workshop work station; according to the actual operation parameters and the historical operation data, a work station association value set and a work-in-process association value set between different work stations are obtained through calculation; based on the work-in-process association value set and the pre-acquired idle time of the electric vehicle, determining the allocation priority of the electric vehicle; calculating according to pre-acquired position information of the work station equipment and a preset path optimization model to obtain a path number set; calculating the material dispatching time according to the path number set and the pre-acquired running speed of the electric transport vehicle, and taking the material dispatching time as a material dispatching priority; and distributing the to-be-allocated material with the highest material allocation priority to the electric transport vehicle with the highest electric transport vehicle allocation priority. The method can reasonably dispatch materials and improve the production efficiency.

Description

Technical Field

[0001] The present invention relates to the technical field of workshop scheduling, and in particular to a material allocation method, device and medium for a semiconductor workshop based on an EMS system. Background Art

[0002] With the increasing application of technologies such as the Internet of Things and artificial intelligence, the demand for semiconductor products continues to increase. From smartphones to smart cars to wearable devices, the semiconductor industry is facing unprecedented development opportunities. The demand for various semiconductor production materials such as hard mask materials, soft mask materials, photoresists, and wafers has also increased accordingly, and the production plan of the semiconductor manufacturing industry is becoming increasingly large.

[0003] At present, manual scheduling is still used in semiconductor manufacturing workshop management. The material handling path and material handling sequence are arranged according to the specific needs of each production. Equipment status, location information, and the quantity of various materials are manually counted. Although it can meet the needs of small-batch production, it is difficult to play a role in modern, large-scale production methods.

[0004] Due to the uneven distribution of workshop capacity and the lack of a reasonable scheduling plan, the waiting time at workstations with surplus capacity is too long, while the processing cycle at workstations with tight capacity is prolonged. With limited manpower, as the production plan increases, it is difficult to optimize manual material handling scheduling, resulting in low production efficiency. Summary of the invention

[0005] The present invention provides a material allocation method, device, electronic equipment and storage medium for a semiconductor workshop based on an EMS system, so as to realize reasonable material allocation and improve production efficiency.

[0006] In a first aspect, in order to solve the above technical problems, the present invention provides a material allocation method for a semiconductor workshop based on an EMS system, comprising: Acquire actual operating parameters, production plan data and historical operating data of production workshop stations, wherein the actual operating parameters include transmission efficiency and current working time; Calculating a set of workstation association values ​​between different workstations according to the actual operation parameters, the production plan data and the historical operation data; Determine a work-in-process associated value set according to the current working time and the workstation associated value set; Determine the priority of allocating electric vehicles in the production workshop based on the WIP association value set and the pre-acquired idle time of the electric vehicles in the workstation, and sort the electric vehicles from high to low according to the priority of allocating electric vehicles; Calculate according to the pre-acquired location information of the workstation equipment and the preset path optimization model to obtain a set of path numbers; Calculating the material dispatching time according to the set of path numbers and the pre-acquired electric vehicle travel speed, and sorting the materials to be dispatched according to the material dispatching time; The material to be allocated corresponding to the material scheduling time with the lowest ranking is allocated to the electric transport vehicle ranked first in the electric transport vehicle allocation priority.

[0007] As an optional implementation manner, the calculating, according to the actual operation parameters, the production plan data and the historical operation data, a set of workstation association values ​​between different workstations includes: Calculating the total efficiency difference between the different workstations according to the current working time; According to the total efficiency difference, a correlation function curve between different workstations is obtained; Using a second-order derivative formula to determine the local extreme value points of the correlation function curve between the workstations, and determining all the local extreme value points as the maximum value points of the correlation value; All the maximum values ​​of the correlation values ​​are standardized to obtain a set of station correlation values ​​between different stations, which is recorded as ,in, Indicates the station-related value, These are workstation numbers.

[0008] As an optional implementation manner, the calculating the total efficiency difference between the different workstations according to the current working time includes: The efficiency difference is calculated by the following formula:

[0009] in, For the n The efficiency difference at each workstation, The completion time of a workstation at the end of a pre-acquired MES production task. The completion time of a workstation at the start time of a pre-acquired MES production task. To preset the standard work order production cycle; The overall efficiency difference between different stations is calculated by the following formula:

[0010] in S(t) represents the overall efficiency difference, N ( t ) means at the current time t The number of all previously completed production tasks, For the i The efficiency difference at each workstation.

[0011] As an optional implementation manner, obtaining the correlation function curve between different workstations according to the total efficiency difference includes: The average efficiency difference is calculated by the following formula:

[0012] in is the average efficiency difference, W is the preset sliding window size, t is the current time; The average efficiency difference is used as a correlation value between the two stations; The least square method is used to fit the correlation values ​​between the two stations of all workstations into a function curve as they change over time, so as to obtain the correlation function curves between different workstations.

[0013] As an optional implementation manner, determining the WIP associated value set according to the current working time and the workstation associated value set includes: Get the material type and number of pieces of each work-in-progress material at each workstation , ,...... ,in k is the material type, k =1,2,3...... m, Work in Process i, j From the place , Processing at each workstation , Time of each workstation , ; The WIP associated value set is calculated using the following formula:

[0014] Among them, the WIP association value set between different workstations is expressed as , is the work-in-process associated value.

[0015] As an optional implementation manner, the determining the electric vehicle allocation priority in the production workshop based on the WIP association value set and the pre-acquired electric vehicle idle time in the workstation includes: The priority of electric vehicle dispatch is calculated by the following formula:

[0016] in, for l The dispatch priority of the electric vehicle No. is the quantity of work in process to be allocated, for l The idle time of the electric vehicle.

[0017] As an optional implementation, the calculation is performed based on the pre-acquired location information of the workstation equipment and the preset path optimization model to obtain a set of path numbers, including: Use the shortest path algorithm to calculate the number of shortest paths between any two workstations, expressed as Q ( i , j ),in i and j Represent the starting and ending stations respectively; The following formula is used to calculate the i To the work station j Total number of paths: Q ( i , j )= Q (1, i )+ Q ( i +1, j )+ Q ( n 3, n 4)+…+( Q (2 r −1, n 3)− Q (2 r , i +1))+…+ Q ( i 1, n 4)− Q ( i 2, j )+…+( Q ( p −1, n 4)− Q ( p , n )− Q ( n 3, n 4)+ Q ( n 2, n )+… Q ( n −1, n )), The set of path numbers is represented as .

[0018] As an optional implementation manner, the calculating of the material scheduling time according to the set of path numbers and the pre-acquired electric vehicle travel speed includes: The material scheduling time is calculated by the following formula: = ; in, It represents the time required for materials to travel from station i to station j. is the set of path numbers, is the travel speed of the electric vehicle.

[0019] In a second aspect, the present invention provides a material allocation device for a semiconductor workshop based on an EMS system, comprising: A data acquisition module is used to acquire actual operating parameters, production plan data and historical operating data of a production workshop station, wherein the actual operating parameters include transmission efficiency and current working time; A workstation association value module, used to calculate a set of workstation association values ​​between different workstations according to the actual operation parameters, the production plan data and the historical operation data; A work-in-progress associated value module, used to determine a work-in-progress associated value set according to the current working time and the workstation associated value set; a priority calculation module, for determining the priority of allocating electric vehicles in the production workshop based on the WIP association value set and the pre-acquired idle time of electric vehicles in the workstation, and sorting the electric vehicles from high to low according to the priority of allocating electric vehicles; A path number calculation module is used to calculate according to the pre-acquired location information of the workstation equipment and the preset path optimization model to obtain a path number set; A time calculation module, used to calculate the material dispatching time according to the set of path numbers and the pre-acquired electric vehicle travel speed, and sort the materials to be dispatched according to the material dispatching time; The material allocation module is used to allocate the to-be-allocated materials corresponding to the material scheduling time with the lowest ranking to the electric transport vehicle ranked first in the electric transport vehicle allocation priority.

[0020] In a third aspect, the present invention further provides an electronic device comprising a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, wherein when the processor executes the computer program, the material allocation method for a semiconductor workshop based on the EMS system as described above is implemented.

[0021] In a fourth aspect, the present invention further provides a computer-readable storage medium, which includes a stored computer program, wherein when the computer program is running, the device where the computer-readable storage medium is located is controlled to execute any one of the above-mentioned material allocation methods for a semiconductor workshop based on the EMS system.

[0022] Compared with the prior art, the present invention has the following beneficial effects: The present invention discloses a material allocation method, device and medium for a semiconductor workshop based on an EMS system, the method comprising: obtaining actual operating parameters, production plan data and historical operating data of a production workshop workstation, the actual operating parameters comprising transmission efficiency and current working time; calculating a workstation association value set between different workstations according to the actual operating parameters, the production plan data and the historical operating data; determining a work-in-progress association value set according to the current working time and the workstation association value set; determining the allocation priority of electric vehicles in the production workshop based on the work-in-progress association value set and the idle time of electric vehicles in the workstations obtained in advance, and sorting the electric vehicle allocation priorities from high to low; calculating according to the location information of the workstation equipment obtained in advance and a preset path optimization model to obtain a path number set; calculating the material scheduling time according to the path number set and the driving speed of the electric vehicle obtained in advance, and sorting the materials to be allocated according to the material scheduling time; allocating the materials to be allocated corresponding to the material scheduling time with the lowest ranking to the electric vehicle ranked first in the electric vehicle allocation priority. The present invention can significantly improve the production efficiency of semiconductor workshops, reduce material waiting time, optimize workshop layout and capacity allocation, and realize real-time perception and dynamic optimization adjustment by using the EMS system for material allocation, thereby improving the accuracy of the overall production plan and the responsiveness of the workshop. Compared with the traditional manual allocation method, this method can better adapt to the needs of modern large-scale production, bring substantial improvements to the semiconductor manufacturing industry, realize reasonable material scheduling, and improve production efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] Figure 1 It is a schematic flow chart of a material allocation method for a semiconductor workshop based on an EMS system provided by a first embodiment of the present invention; Figure 2 It is a structural schematic diagram of a material allocation device for a semiconductor workshop based on an EMS system provided by the second embodiment of the present invention. DETAILED DESCRIPTION

[0024] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0025] With the increasing application of technologies such as the Internet of Things and artificial intelligence, the demand for semiconductor products continues to increase. From smartphones to smart cars to wearable devices, the semiconductor industry is facing unprecedented development opportunities. The demand for various semiconductor production materials such as hard mask materials, soft mask materials, photoresists, and wafers has also increased accordingly, and the production plan of the semiconductor manufacturing industry is becoming increasingly large.

[0026] At present, manual scheduling is still used in semiconductor manufacturing workshop management. The material handling path and material handling sequence are arranged according to the specific needs of each production. Equipment status, location information, and the quantity of various materials are manually counted. Although it can meet the needs of small-batch production, it is difficult to play a role in modern, large-scale production methods.

[0027] Due to the uneven distribution of workshop capacity and the lack of a reasonable scheduling plan, the waiting time at workstations with surplus capacity is too long, while the processing cycle at workstations with tight capacity is prolonged. With limited manpower, as the production plan increases, it is difficult to optimize manual material handling scheduling, resulting in low production efficiency.

[0028] To solve the above problems, refer to Figure 1 The first embodiment of the present invention provides a material allocation method for a semiconductor workshop based on an EMS system, comprising the following steps: S11, obtaining actual operating parameters, production plan data and historical operating data of the production workshop station, wherein the actual operating parameters include transmission efficiency and current working time; S12, calculating a set of workstation association values ​​between different workstations according to the actual operation parameters, the production plan data and the historical operation data; S13, determining a work-in-process associated value set according to the current working time and the workstation associated value set; S14, determining the priority of allocating electric vehicles in the production workshop based on the WIP association value set and the pre-acquired idle time of the electric vehicles in the workstation, and sorting the electric vehicles from high to low according to the priority of allocating electric vehicles; S15, performing calculations based on the pre-acquired location information of the workstation equipment and a preset path optimization model to obtain a set of path numbers; S16, calculating the material dispatching time according to the set of path numbers and the pre-acquired electric vehicle travel speed, and sorting the materials to be dispatched according to the material dispatching time; S17, allocating the to-be-allocated material corresponding to the material scheduling time with the lowest ranking to the electric transport vehicle ranked first in the electric transport vehicle allocation priority.

[0029] It is worth noting that the innovation of the present invention lies in the application of the EMS system for material allocation. Explanation of the EMS system. The EMS system generally refers to a material handling system used in the manufacturing industry, and its full name is Elevated Monorail System. It is an automated material handling system that uses electric monorail cranes (monorails) to transport materials in factories or warehouses. These monorail cranes can move on predetermined tracks, which are usually installed under the ceiling of the building to save ground space and increase the layout flexibility of the factory. It consists of a monorail crane, a track system, a control system, a loading and unloading station, etc. All data and calculations of the present invention are performed by the control system in the EMS system.

[0030] In step S11, these basic information are obtained through the EMS system to collect various working parameters of the semiconductor workshop in real time, including transmission efficiency, current working time, the total number of workstations, the shortest number of wafers that can be processed at each workstation, the time required for the production line to process one wafer, and the expected production capacity of each workstation, which can provide important data support for subsequent material allocation.

[0031] In steps S12 and S13, the work in progress The correlation value is an indicator to measure the efficiency difference when the work-in-process moves from one station to another. It takes into account factors such as the transfer time of the work-in-process between different stations and the number of work-in-process, and is used to evaluate the degree of correlation between the work-in-process at different stations. Station correlation value It is an indicator to measure the degree of mutual influence between two workstations. It reflects the degree of mutual dependence between the two workstations in the production process and can help optimize the material allocation plan.

[0032] In one implementation, calculating a workstation association value set and a work-in-process association value set between different workstations according to the actual operation parameters and the historical operation data includes: S121, calculating the total efficiency difference between the different workstations according to the current working time; S122, obtaining a correlation function curve between different workstations according to the total efficiency difference; S123, using a second-order derivative formula to determine the local extreme value points of the correlation function curve between the workstations, and determining all the local extreme value points as the maximum value points of the correlation value; S124, standardize all the maximum values ​​of the correlation values ​​to obtain a set of station correlation values ​​between different stations, recorded as ,in, Indicates the station-related value, These are workstation numbers.

[0033] In step S121, the efficiency difference can be calculated by the following formula:

[0034] in, For the n The efficiency difference at each workstation, The completion time of a workstation at the end of a pre-acquired MES production task. The completion time of a workstation at the start time of a pre-acquired MES production task. To preset the standard work order production cycle; Then, the overall efficiency difference between different stations is calculated by the following formula:

[0035] in S(t) represents the overall efficiency difference, N ( t ) means at the current time t The number of all previously completed production tasks, For the i The efficiency difference at each workstation.

[0036] In step S122, obtaining the correlation function curve between different workstations according to the total efficiency difference includes: The average efficiency difference is calculated by the following formula:

[0037] in is the average efficiency difference, W is the preset sliding window size, t is the current time; The average efficiency difference is used as the correlation value between the two stations, and the correlation values ​​between the two stations of all workstations are fitted into a function curve with the least square method over time to obtain the correlation function curves between different workstations.

[0038] In step S123, the local extreme value points of the correlation function curve between the workstations are determined using a second-order derivative formula, and all the local extreme value points are determined to be the correlation value maximum value points.

[0039] In step S124, all the maximum values ​​of the correlation values ​​are standardized to obtain a set of station correlation values ​​between different stations, which is recorded as ,in, Indicates the station-related value, These are workstation numbers.

[0040] Furthermore, in the embodiment of the present invention, the calculation formula for the normalization process is: ; in, represents the original data point; Represents the mean of the data set; Represents the standard deviation of the data set; Represents the normalized data points.

[0041] It should be noted that the standardization process is to calculate the mean and standard deviation of each parameter respectively. In this embodiment, the parameters of the standardization process are the correlation values ​​between workstations.

[0042] In step S13, determining the WIP associated value set according to the current working time and the workstation associated value set includes: Get the material type and number of pieces of each work-in-progress material at each workstation , ,...... ,in k is the material type, k =1,2,3...... m, Work in Process i, j From the place , Processing at each workstation , Time of each workstation , ; The WIP associated value set is calculated using the following formula:

[0043] Among them, the WIP association value set between different workstations is expressed as , is the WIP association value. As above, we get the station association value set and the WIP association value set between different stations. Let's make a specific analysis of this formula: Molecular part It consists of the product of two parts: Represents work in progress i and j The required k The number of pieces of material, Represents the time required to move a work-in-progress from one station to another compared to the standard time The relative increase compared to part is greater than , then this value will be greater than 0, indicating that the actual time is longer than the standard time; if The part equals , then this value will be equal to 0; if The part is less than , then this value will be less than 0. This part reflects the relative difference between the time required for the work-in-process to move between different workstations and the standard time, as well as the weighted contribution of different material quantities to this difference. The denominator part represents the total number of pieces of all materials required for the work-in-process 𝑖 or 𝑗, which helps to standardize the calculation results so that the associated value is not affected by the total amount of materials.

[0044] It is worth noting that the WIP association value focuses on evaluating the flow efficiency of WIP between different workstations, which can help manufacturers understand which WIP is closely related to specific workstations or equipment, so as to better arrange production plans and scheduling, and ensure that high-priority WIP can be processed first. By analyzing the set of WIP association values, it is possible to determine which resources (such as workstations, tools, and personnel) are critical to completing specific WIP, so as to reasonably allocate resources and avoid resource waste. Understanding the relationship between WIP and its production factors helps to implement more effective quality control measures to ensure product quality. WIP with high association values ​​is often related to bottleneck links in the production line. By monitoring and managing these WIP, bottleneck problems can be identified and alleviated, and overall production efficiency can be improved.

[0045] The station correlation value focuses on evaluating the degree of interdependence between different stations. By analyzing the station correlation value, we can better understand which stations are critical to specific products or works in progress, thereby optimizing production planning and scheduling. Stations with high correlation values ​​usually mean higher resource requirements or high dependence on specific types of resources. This helps to allocate resources reasonably and avoid resource waste. Stations with high correlation values ​​are often bottlenecks in the production line. By identifying these stations, measures can be taken to alleviate bottleneck problems and improve overall production efficiency. Understanding the relationship between stations and materials helps determine which stations need to prioritize material allocation to ensure that key production links are not delayed due to material shortages.

[0046] In step S14, based on the WIP association value set and the pre-acquired idle time of the electric vehicle in the workstation, the priority of the electric vehicle allocation in the production workshop is determined, and the electric vehicle allocation priority is sorted from high to low. It should be noted that the electric vehicle allocation priority refers to a sorting mechanism in a semiconductor workshop that determines which electric vehicles should be prioritized for material allocation based on the idle time of the electric vehicle and the amount of materials that can be allocated.

[0047] The priority of electric vehicle dispatch is calculated by the following formula:

[0048] in, for l The dispatch priority of the electric vehicle No. is the quantity of work in process to be allocated, for l The idle time of the electric vehicle.

[0049] According to the above formula, we can see and , is proportional to This means that the more work-in-progress is to be allocated, the higher the allocation priority of the electric transport vehicle; i arrive j The larger the correlation value is; the longer the idle time of the electric transport vehicle is, the lower the allocation priority of the electric transport vehicle is. Through this calculation method, it can be ensured that those electric transport vehicles that have a large number of work-in-progress to be transported and are critical to the production process are allocated first.

[0050] It is worth noting that by giving priority to the allocation of electric transport vehicles with shorter idle time, the transportation of materials can be completed faster, the waiting time for materials can be reduced, and production efficiency can be improved. Reasonable allocation of electric transport vehicles can avoid waste of resources, ensure that materials are delivered to designated workstations in a timely manner, and reduce the downtime of the production line. If the idle time of an electric transport vehicle is too long, its allocation priority will be reduced, which can avoid long-term idleness and reduce the time for work-in-progress to wait for transportation. In this way, electric transport vehicle resources can be allocated more reasonably to ensure smooth material flow in key links, thereby improving the efficiency of the overall production line.

[0051] In step S15, calculation is performed based on the pre-acquired location information of the workstation equipment and a preset path optimization model to obtain a set of path numbers.

[0052] Among them, the path number set From a workstation i To another workstation j The number of different paths that may exist between them. In the material allocation process of the semiconductor workshop, understanding The value can be used to help plan the best route for material transportation to reduce transportation time and cost.

[0053] Specifically, the shortest path algorithm is used to calculate the number of shortest paths between any two workstations, expressed as Q ( i , j ),in i and j Represent the starting and ending stations respectively; The shortest path can store general path planning algorithms such as Dijkstra algorithm or Floyd algorithm in memory in advance and use them when calculating the number of paths.

[0054] The following formula is used to calculate the i To the work station j Total number of paths: Q ( i , j )= Q (1, i )+ Q ( i +1, j )+ Q ( n 3, n 4)+…+( Q (2 r −1, n 3)− Q (2 r , i +1))+…+ Q ( i 1, n 4)− Q ( i 2, j )+…+( Q ( p −1, n 4)− Q ( p , n )− Q ( n 3, n 4)+ Q ( n 2, n )+… Q ( n −1, n )), The set of path numbers is represented as .

[0055] For example, suppose we want to calculate the number of paths from station 1 to station 6, and we know that there are three different paths from station 1 to station 6, and the electric vehicle only needs to travel half the distance between station 3 and station 4. According to the formula: Q (1,6)= Q (1,1)+ Q (2,6)+ Q (3,4)+( Q (5,3) - Q (6,2))+ Q (1,4) - Q (2,6)+( Q (5,4) - Q (6,1); Known Q (1,1) = 1, Q (2,6) = 3, Q (3,4) = 2, Q (5,3) = 2, Q (6,2) = 1, Q (1,4) = 2, Q (5,4) = 1, Q (6,1) = 1, Q (2,1) = 1; So Q (1,6)=8.

[0056] In step S16, the material dispatching time is calculated according to the set of path numbers and the pre-acquired electric vehicle travel speed, and the materials to be dispatched are sorted according to the material dispatching time.

[0057] It should be noted that material dispatch priority refers to a sorting mechanism set in a production environment to optimize logistics efficiency. It determines the time sequence in which materials (such as parts, raw materials, etc.) are transported to various workstations. In a manufacturing system, the timely supply of materials is crucial to keep the production line running smoothly. By calculating the material dispatch time and setting priorities accordingly, it can be ensured that key workstations will not be shut down due to material shortages.

[0058] Next, the material scheduling time will be calculated based on these path number sets and the speed of the electric transport vehicle, and used as the priority basis for material allocation.

[0059] The material scheduling time is calculated by the following formula: = ; in, Indicates that the material is from the workstation iTo the work station j The time required, is the set of path numbers, is the travel speed of the electric vehicle.

[0060] It is worth noting that the priority is inversely proportional to the travel time, that is, the path with shorter travel time has higher priority. This means that materials will be dispatched along the shortest path first to reduce waiting time and improve overall production efficiency.

[0061] In step S17, the to-be-allocated material corresponding to the lowest-ranked material scheduling time is allocated to the electric transport vehicle ranked first in the electric transport vehicle allocation priority.

[0062] Assume that materials A, B, and C have priorities 1, 2, and 3, respectively (larger numbers indicate higher priorities). Trucks 1, 2, and 3 have priorities 3, 1, and 2, respectively (larger numbers indicate higher priorities). Then match material C with truck 1. Truck 1 is loaded with material C and sets off along the optimal path. In this way, it is ensured that important materials can be delivered to their destination quickly, thus ensuring the smooth operation of the production line. If there are multiple high-priority materials or trucks, similar matching operations can be continued until all high-priority materials are allocated.

[0063] It is worth mentioning that the allocation method is very important in the manufacturing environment because it involves how to effectively use resources (such as equipment, personnel, materials, etc.) to meet production needs. In different production systems, different allocation methods may be used depending on the specific situation and goals. There are also some common allocation methods that can be used, For example, the allocation method based on constraint theory focuses on identifying and optimizing the bottleneck links in the production process. It helps to maximize the utilization efficiency of bottleneck resources and improve the balance of the overall production process. However, it is difficult to implement and requires accurate identification of bottlenecks. It can also be done through artificial intelligence-assisted allocation, which uses machine learning and artificial intelligence technology to predict and optimize resource allocation. It can handle large amounts of data and complex decision-making processes, provide more accurate predictions and optimization suggestions, but the implementation cost is high. The choice of which allocation method depends on the characteristics of the production environment, resource availability, production goals, and other factors. For example, in a highly dynamic and uncertain production environment, dynamic allocation or artificial intelligence-assisted allocation may be more suitable. In the case of relatively stable production processes, static allocation or rule-based allocation may be a better choice.

[0064] It should be noted that the present invention introduces a loop optimization mechanism, which is a method for gradually improving the material allocation plan through continuous iteration and adjustment. This method can ensure that the allocation plan continues to improve over time and changes in the production environment to achieve the optimal state. Use a priority-based allocation method for material allocation, set the number of iterations and the target to minimize the efficiency difference. After each EMS task is completed, calculate the average efficiency difference of the current plan. After introducing the shortest operation time priority method for material allocation, calculate the average efficiency difference of the updated plan. If the average difference of the new plan is smaller, use it as the current plan; otherwise try other methods. When there is no obvious improvement in the average efficiency difference after several consecutive iterations, end the loop optimization process. Through the loop optimization mechanism, continuous iterative optimization can gradually improve the material allocation plan and improve production efficiency. As the production environment changes, the loop optimization mechanism can adjust the plan in time to ensure that it adapts to new needs, adjust the optimization strategy according to actual conditions, and make the plan more flexible. In order to facilitate the understanding of the present invention, some preferred embodiments of the present invention are further described below.

[0065] In step S12, efficiency difference, total efficiency difference and average efficiency difference are mentioned, which usually refer to the performance differences shown by different methods or solutions when performing the same task.

[0066] The efficiency difference refers to the difference in resources (such as time, cost, etc.) required to complete the same task by two or more different methods. In material allocation, this usually refers to the difference in time or cost spent on completing the material allocation task when different paths or strategies are adopted. Suppose there are two paths, path A and path B, used to transport materials from station 1 to station 6. Path A takes 50 minutes, while path B only takes 40 minutes. In this case, the efficiency difference of path B relative to path A is 10 minutes.

[0067] The total efficiency difference is the sum of the total efficiency differences when using different methods to complete multiple tasks within a period of time. This can be used to evaluate the overall performance of different strategies throughout the production cycle. If you consider the situation where multiple material allocation tasks are completed using different paths in a working day, the total efficiency difference will be the sum of the efficiency differences of each task. For example, if path B is used to complete 10 tasks in a day, and each time it is 10 minutes faster than path A, the total efficiency difference is 100 minutes.

[0068] The average efficiency difference is the ratio of the total efficiency difference to the number of completed tasks when multiple tasks are completed within a certain period of time. It can be used to measure the average performance difference between different methods or strategies. Continuing with the above example, if 10 tasks are completed using path B in one day, the total efficiency difference is 100 minutes, then the average efficiency difference is 10 minutes (100 minutes / 10 tasks).

[0069] It is worth further explaining that how to determine the dispatch priority of electric vehicles is described in step S13, and here is a way to improve efficiency. Specifically, the electric vehicles are divided into three groups: those with unused time of less than 5 minutes, 5 minutes to 5 and a half hours, and more than 5 and a half hours, and then sorted in ascending order according to the unused time. The shorter the time, the higher the priority. Within each group, the electric vehicles are sorted in ascending order according to the unused time. In this way, the electric vehicles with shorter unused time are ranked higher in the sorting and have higher priority. In this way, the electric vehicle with the shortest unused time has the highest priority, and the electric vehicle with the longest unused time has the lowest priority. This method helps to ensure that the electric vehicles are fully utilized and reduce waiting time, thereby improving overall logistics efficiency. At the same time, it can also be calculated separately for computers to improve computing efficiency.

[0070] In order to obtain the correlation value between workstations and the correlation value of work-in-progress, another implementation method can also be considered: Get the current number of WIPs, which directly affects the urgency and priority of material allocation; get the time WIPs wait for processing or transportation, which helps evaluate the efficiency and bottlenecks of the production line; get the distance of the transport action; Define a comprehensive scoring function ; in , is the weight coefficient, which indicates the relative importance of the total number of WIP, waiting time and handling distance in the comprehensive score. It can be determined through regression analysis based on historical data.

[0071] The smaller the waiting time and the handling distance, the higher the corresponding scores, indicating that these factors are more favorable to material allocation. =50, 10min, =50m. Assuming the given weight coefficient Then we can calculate S=25.034. It is also acceptable to use this as the WIP association value. By comprehensively considering multiple parameters, we can ensure that WIP with large quantity, long waiting time and short handling distance are allocated first, thereby improving overall production efficiency. Reducing the waiting time of WIP helps to improve the smoothness of the production line and reduce the stagnation of the production line. In this way, resources can be allocated more reasonably to ensure smooth material flow in key links.

[0072] In summary, the present invention can significantly improve the production efficiency of semiconductor workshops, reduce material waiting time, optimize workshop layout and capacity allocation by using the EMS system for material allocation, and can realize real-time perception and dynamic optimization and adjustment, thereby improving the accuracy of the overall production plan and the responsiveness of the workshop. Compared with the traditional manual allocation method, it is difficult to obtain detailed information on the production site in real time, such as material inventory, equipment status, number of work-in-progress, etc., which makes it impossible to accurately judge the current production status, making it difficult to make the best scheduling decision. This method can better adapt to the needs of modern large-scale production, bring substantial improvements to the semiconductor manufacturing industry, realize reasonable scheduling of materials, and improve production efficiency.

[0073] Reference Figure 2 The second embodiment of the present invention provides a material allocation device for a semiconductor workshop based on an EMS system, comprising: A data acquisition module is used to acquire actual operating parameters, production plan data and historical operating data of a production workshop station, wherein the actual operating parameters include transmission efficiency and current working time; A workstation association value module, used to calculate a set of workstation association values ​​between different workstations according to the actual operation parameters, the production plan data and the historical operation data; A work-in-progress associated value module, used to determine a work-in-progress associated value set according to the current working time and the workstation associated value set; a priority calculation module, for determining the priority of allocating electric vehicles in the production workshop based on the WIP association value set and the pre-acquired idle time of electric vehicles in the workstation, and sorting the electric vehicles from high to low according to the priority of allocating electric vehicles; A path number calculation module is used to calculate according to the pre-acquired location information of the workstation equipment and the preset path optimization model to obtain a path number set; A time calculation module, used to calculate the material dispatching time according to the set of path numbers and the pre-acquired electric vehicle travel speed, and sort the materials to be dispatched according to the material dispatching time; The material allocation module is used to allocate the to-be-allocated materials corresponding to the material scheduling time with the lowest ranking to the electric transport vehicle ranked first in the electric transport vehicle allocation priority.

[0074] It should be noted that the material allocation method device for a semiconductor workshop based on an EMS system provided in an embodiment of the present invention is used to execute all the process steps of the material allocation method for a semiconductor workshop based on an EMS system in the above-mentioned embodiment. The working principles and beneficial effects of the two correspond one to one, and therefore will not be repeated here.

[0075] The embodiment of the present invention further provides an electronic device. The electronic device includes: a processor, a memory, and a computer program stored in the memory and executable on the processor, such as an optimization path program. When the processor executes the computer program, the steps in the above-mentioned material allocation method for a semiconductor workshop based on an EMS system are implemented, such as Figure 1 Alternatively, when the processor executes the computer program, the functions of the modules / units in the above-mentioned device embodiments are realized, such as the data acquisition module.

[0076] Exemplarily, the computer program may be divided into one or more modules / units, which are stored in the memory and executed by the processor to implement the present invention. The one or more modules / units may be a series of computer program instruction segments capable of implementing specific functions, which are used to describe the execution process of the computer program in the electronic device.

[0077] The electronic device may be a computing device such as a desktop computer, a notebook, a PDA, and a smart tablet. The electronic device may include, but is not limited to, a processor and a memory. Those skilled in the art will appreciate that the above components are merely examples of electronic devices and do not constitute a limitation on the electronic device. The electronic device may include more or fewer components than the above components, or may combine certain components, or different components. For example, the electronic device may also include input and output devices, network access devices, buses, etc.

[0078] The processor may be a central processing unit (CPU), other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or any conventional processor, etc. The processor is the control center of the electronic device, and uses various interfaces and lines to connect various parts of the entire electronic device.

[0079] The memory can be used to store the computer program and / or module, and the processor realizes various functions of the electronic device by running or executing the computer program and / or module stored in the memory, and calling the data stored in the memory. The memory can mainly include a program storage area and a data storage area, wherein the program storage area can store an operating system, an application required for at least one function (such as a sound playback function, an image playback function, etc.), etc.; the data storage area can store data created according to the use of the mobile phone (such as audio data, a phone book, etc.), etc. In addition, the memory can include a high-speed random access memory, and can also include a non-volatile memory, such as a hard disk, a memory, a plug-in hard disk, a smart memory card (Smart Media Card, SMC), a secure digital (Secure Digital, SD) card, a flash card (Flash Card), at least one disk storage device, a flash memory device, or other volatile solid-state storage devices.

[0080] Wherein, if the module / unit integrated in the electronic device is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on such an understanding, the present invention implements all or part of the processes in the above-mentioned embodiment method, and can also be completed by instructing the relevant hardware through a computer program. The computer program can be stored in a computer-readable storage medium, and the computer program can implement the steps of the above-mentioned various method embodiments when executed by the processor. Wherein, the computer program includes computer program code, and the computer program code can be in source code form, object code form, executable file or some intermediate form. The computer-readable medium may include: any entity or device capable of carrying the computer program code, recording medium, U disk, mobile hard disk, disk, optical disk, computer memory, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), electric carrier signal, telecommunication signal and software distribution medium. It should be noted that the content contained in the computer-readable medium can be appropriately increased or decreased according to the requirements of legislation and patent practice in the jurisdiction. For example, in some jurisdictions, according to legislation and patent practice, computer-readable media do not include electric carrier signals and telecommunication signals.

[0081] It should be noted that the device embodiments described above are merely schematic, wherein the units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they may be located in one place, or they may be distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the scheme of this embodiment. In addition, in the accompanying drawings of the device embodiments provided by the present invention, the connection relationship between the modules indicates that there is a communication connection between them, which may be specifically implemented as one or more communication buses or signal lines. A person of ordinary skill in the art may understand and implement it without paying any creative effort.

[0082] The specific embodiments described above further illustrate the purpose, technical solutions and beneficial effects of the present invention. It should be understood that the above description is only a specific embodiment of the present invention and is not intended to limit the scope of protection of the present invention. It is particularly pointed out that for those skilled in the art, any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention should be included in the scope of protection of the present invention.

Claims

1. A material allocation method for a semiconductor workshop based on an EMS system, characterized in that: Executed by the EMS system, including: Acquire actual operating parameters, production plan data and historical operating data of production workshop stations, wherein the actual operating parameters include transmission efficiency and current working time; Calculating a set of workstation association values ​​between different workstations according to the actual operation parameters, the production plan data and the historical operation data; Determine a work-in-process associated value set according to the current working time and the workstation associated value set; Determine the priority of allocating electric vehicles in the production workshop based on the WIP association value set and the pre-acquired idle time of the electric vehicles in the workstation, and sort the electric vehicles from high to low according to the priority of allocating electric vehicles; Calculate according to the pre-acquired location information of the workstation equipment and the preset path optimization model to obtain a set of path numbers; Calculating the material dispatching time according to the set of path numbers and the pre-acquired electric vehicle travel speed, and sorting the materials to be dispatched according to the material dispatching time; The material to be allocated corresponding to the material scheduling time with the lowest ranking is allocated to the electric transport vehicle ranked first in the electric transport vehicle allocation priority.

2. The material allocation method for a semiconductor workshop based on an EMS system according to claim 1, characterized in that: The calculating, according to the actual operation parameters, the production plan data and the historical operation data, a set of workstation association values ​​between different workstations comprises: Calculating the total efficiency difference between the different workstations according to the current working time; According to the total efficiency difference, a correlation function curve between different workstations is obtained; Using a second-order derivative formula to determine the local extreme value points of the correlation function curve between the workstations, and determining all the local extreme value points as the maximum value points of the correlation value; All the maximum values ​​of the correlation values ​​are standardized to obtain a set of station correlation values ​​between different stations, which is recorded as ,in, Indicates the station-related value, These are workstation numbers.

3. The material allocation method for a semiconductor workshop based on an EMS system according to claim 2, characterized in that: The calculating the total efficiency difference between the different workstations according to the current working time includes: The efficiency difference is calculated by the following formula: in, For the n The efficiency difference at each workstation, The completion time of a workstation at the end of a pre-acquired MES production task. The completion time of a workstation at the start time of a pre-acquired MES production task. To preset the standard work order production cycle; The overall efficiency difference between different stations is calculated by the following formula: in S(t) represents the overall efficiency difference, N ( t ) means at the current time t The number of all previously completed production tasks, For the i The efficiency difference at each workstation.

4. The material allocation method for a semiconductor workshop based on an EMS system according to claim 2, characterized in that: The correlation function curves between different workstations are obtained according to the total efficiency difference, including: The average efficiency difference is calculated by the following formula: in is the average efficiency difference, W is the preset sliding window size, t is the current time; The average efficiency difference is used as a correlation value between the two stations; The least square method is used to fit the correlation values ​​between the two stations of all workstations into a function curve as they change over time, so as to obtain the correlation function curves between different workstations.

5. The material allocation method for a semiconductor workshop based on an EMS system according to claim 1, characterized in that: Determining the WIP associated value set according to the current working time and the workstation associated value set includes: Get the material type and number of pieces of each work-in-progress material at each workstation , ,...... ,in k is the material type, k =1,2,3...... m, Work in Process i, j From the place , Processing at each workstation , Time of each workstation , ; The WIP associated value set is calculated using the following formula: Among them, the WIP association value set between different workstations is expressed as , is the work-in-process associated value.

6. The material allocation method for a semiconductor workshop based on an EMS system according to claim 1, characterized in that: The step of determining the priority of allocating electric vehicles in a production workshop based on the WIP association value set and the pre-acquired idle time of electric vehicles in the workstations includes: The priority of electric vehicle dispatch is calculated by the following formula: in, for l The dispatch priority of the electric vehicle No. is the quantity of work in process to be allocated, for l The idle time of the electric vehicle.

7. The material allocation method for a semiconductor workshop based on an EMS system according to claim 1, characterized in that: The calculation is performed based on the pre-acquired location information of the workstation equipment and the preset path optimization model to obtain a set of path numbers, including: Use the shortest path algorithm to calculate the number of shortest paths between any two workstations, expressed as Q ( i , j ),in i and j Represent the starting and ending stations respectively; The following formula is used to calculate the i To the work station j Total number of paths: Q ( i , j )= Q (1, i )+ Q ( i +1, j )+ Q ( n 3, n 4)+…+( Q (2 r −1, n 3)− Q (2 r , i +1))+…+ Q ( i 1, n 4)− Q ( i 2, j )+…+( Q ( p −1, n 4)− Q ( p , n )− Q ( n 3, n 4)+ Q ( n 2, n )+… Q ( n −1, n ))， The set of path numbers is represented as .

8. The material allocation method for a semiconductor workshop based on an EMS system according to claim 1, characterized in that: The calculating of the material dispatching time according to the set of path numbers and the pre-acquired electric vehicle travel speed comprises: The material scheduling time is calculated by the following formula: ＝ ； in, It represents the time required for materials to travel from station i to station j. is the set of path numbers, is the travel speed of the electric vehicle.

9. A material allocation device for a semiconductor workshop based on an EMS system, comprising: A data acquisition module is used to acquire actual operating parameters, production plan data and historical operating data of a production workshop station, wherein the actual operating parameters include transmission efficiency and current working time; A workstation association value module, used to calculate a set of workstation association values ​​between different workstations according to the actual operation parameters, the production plan data and the historical operation data; A work-in-progress associated value module, used to determine a work-in-progress associated value set according to the current working time and the workstation associated value set; a priority calculation module, for determining the priority of allocating electric vehicles in the production workshop based on the WIP association value set and the pre-acquired idle time of electric vehicles in the workstation, and sorting the electric vehicles from high to low according to the priority of allocating electric vehicles; A path number calculation module is used to calculate according to the pre-acquired location information of the workstation equipment and the preset path optimization model to obtain a path number set; A time calculation module, used to calculate the material dispatching time according to the set of path numbers and the pre-acquired electric vehicle travel speed, and sort the materials to be dispatched according to the material dispatching time; The material allocation module is used to allocate the to-be-allocated materials corresponding to the material scheduling time with the lowest ranking to the electric transport vehicle ranked first in the electric transport vehicle allocation priority.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium includes a stored computer program, wherein when the computer program is executed, the device where the computer-readable storage medium is located is controlled to execute the material allocation method for a semiconductor workshop based on an EMS system as described in any one of claims 1 to 8.

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