Semiconductor device package production line scheduling method, apparatus, and device

Abstract

The application relates to a semiconductor device packaging production line scheduling method, device and equipment, relates to the production scheduling technical field, and the method comprises the following steps: obtaining multiple order information containing product models, packaging process requirements, order quantities, delivery deadlines and emergency grades, splitting each order into multiple process tasks with device requirements, working hour requirements and process parameter requirements according to preset process route rules, and determining order priority tasks containing process requirements, working hours and priorities; meanwhile, monitoring information of each device on the packaging production line is obtained, the current state of each device is determined based on a set threshold value and a fault diagnosis model; based on the current state of the device and the order priority task, a multi-objective optimization algorithm is used to allocate resources to each order, and a scheduling scheme is generated; finally, the corresponding device is controlled to perform production according to the scheduling scheme. The method realizes dynamic collaborative optimization of orders, devices and resources, and improves the scheduling efficiency of the packaging production line.

Description

Technical Field

[0001] This invention relates to the field of production scheduling technology, and in particular to a method, apparatus and equipment for scheduling a semiconductor equipment packaging production line. Background Technology

[0002] As advanced semiconductor packaging technologies continue to advance towards more complex directions such as multi-chip integration and system-in-package (SoC), production lines are expanding in scale and involving more types of packaging equipment, including bonding machines, injection molding machines, and testing equipment. The processes are also becoming more complex, including chip picking, bonding, packaging, and finished product testing. Simultaneously, order types are becoming increasingly diverse. All these changes make production line management more challenging and place higher demands on production efficiency and order fulfillment capabilities.

[0003] In the scheduling of traditional advanced semiconductor packaging production lines, several common methods are typically employed. One method relies on manual experience, where staff use their accumulated experience to arrange production tasks, allocate equipment resources, and handle various problems encountered during production. Another method uses static planning scheduling, where a fixed production plan is pre-defined, and production is organized according to preset processes and timelines.

[0004] However, in actual production, the original scheduling arrangements are greatly affected when unexpected equipment failures, urgent order changes, or material shortages occur. Manual adjustments in such situations have long response times, easily leading to production interruptions or order delays. Furthermore, uneven resource allocation and the inability of static scheduling to monitor the load status of each piece of equipment in real time result in significant differences in equipment utilization, preventing the overall production line capacity from being fully utilized. In addition, poor data collaboration exists; equipment status, order progress, and material inventory data are scattered across various subsystems, forming "data silos." This makes it difficult for the scheduling system to obtain complete data to support decision-making, leading to gaps in process connections. Finally, anomaly handling is passive; traditional systems only issue alarms after equipment failures or order delays occur, lacking early warning mechanisms. Moreover, anomaly handling requires manual coordination of each piece of equipment and order, which is inefficient and further amplifies production losses. Summary of the Invention

[0005] The technical problem to be solved by the present invention is to provide a method, apparatus and equipment for scheduling a semiconductor equipment packaging production line, which aims to solve at least one of the above-mentioned technical problems.

[0006] The technical solution of the present invention to solve the above-mentioned technical problems is as follows: In a first aspect, this application provides a method for scheduling a semiconductor equipment packaging production line, which adopts the following technical solution: A method for scheduling a semiconductor equipment packaging production line, comprising: The system acquires multiple order information entries, including product model, packaging process requirements, order quantity, delivery deadline, and urgency level. Based on these multiple order information entries and preset process route rules, each order information entry is broken down into multiple process tasks. Each process task represents a production step with equipment requirements, time requirements, and process parameter requirements. Based on the multiple process tasks corresponding to each order information entry, an order priority task is determined, which includes the process requirements, time requirements, and priority of each order. The monitoring information of each device on the packaging production line is obtained. Based on the monitoring information of each device, the set threshold information and the fault diagnosis model, the current status of each device on the packaging production line is determined. The monitoring information includes temperature data, vibration data, load data, process data and operation data. The current status is normal operation, low load, high load, fault warning or fault shutdown. Based on the current status of each device on the packaging production line, the order priority task, and the preset multi-objective optimization algorithm, resource allocation is performed on each order information to obtain a scheduling scheme, which includes the scheduling task for each order information. The scheduling scheme controls the equipment on the corresponding production line so that the equipment on the corresponding production line can produce the product corresponding to each order information.

[0007] The beneficial effects of this invention are as follows: By automatically breaking down orders into process tasks with time, equipment, and process constraints, and dynamically prioritizing them by integrating multiple dimensions such as delivery deadlines and urgency levels, high-value and urgent orders are ensured to be processed first. Based on various sensors and fault diagnosis models distributed at key equipment nodes, real-time monitoring and early warning of equipment health status are achieved, providing a precise on-site data foundation for dynamic scheduling. By using a multi-objective optimization algorithm for resource allocation based on real-time equipment status and order priorities, an optimal scheduling scheme can be generated, thereby improving overall equipment utilization and capacity, and shortening the average order delivery cycle.

[0008] Based on the above technical solution, the present invention can be further improved as follows.

[0009] Furthermore, for any of the aforementioned order information, the process of executing the scheduling scheme also includes: Obtain the production progress information sent by the corresponding device in the scheduling scheme, the production progress information including the completed quantity and the remaining working hours; Based on the order information and the production progress information, the current progress value is determined, and based on the priority, working hours and scheduling scheme corresponding to the order information, the planned progress threshold corresponding to the order information is calculated. If the current progress value is less than the planned progress threshold corresponding to the order information, a prompt message is sent to the management personnel of the equipment corresponding to the order information, and the cause of the abnormality is determined based on the monitoring information, material inventory information and order queue information of the corresponding equipment in the scheduling scheme. The cause of the abnormality includes any one of equipment abnormality, order abnormality and material abnormality. Based on the aforementioned causes of the anomaly, the preset anomaly policy rules, and the scheduling scheme, a new scheduling scheme is determined, and the equipment on the corresponding production line is controlled based on the new scheduling scheme. After all the process tasks of the order information are completed, the order information is marked as completed, and the production data related to the order information is stored. The production data includes total time, equipment utilization rate, and number of anomalies.

[0010] The beneficial effects of adopting the above-mentioned further solutions are as follows: By acquiring equipment production progress information in real time and comparing it with planned progress thresholds, production delays can be detected promptly, and alerts can be sent to management personnel for timely intervention. Determining the cause of anomalies based on equipment monitoring information, material inventory information, and order queue information allows for precise problem localization. Based on the cause of the anomaly and preset rules, a new scheduling plan can be determined and the equipment controlled, enabling dynamic adjustments and rapid responses to equipment, order, and material anomalies, avoiding production interruptions and improving order delivery rates. Marking order completion status and storing production data provides data support for subsequent production optimization decisions.

[0011] Furthermore, for any of the aforementioned order information, determining the cause of the anomaly based on the monitoring information of the corresponding device, material inventory information, and order queue information in the scheduling scheme includes: Based on the monitoring information, set threshold information and fault diagnosis model of the corresponding device in the scheduling scheme, it is determined whether at least one parameter in the monitoring information exceeds the corresponding threshold range, or whether the fault diagnosis model outputs an abnormality. If at least one parameter in the monitoring information exceeds the corresponding threshold range, or if the fault diagnosis model outputs an abnormality, then the cause of the abnormality is determined to be equipment malfunction. Based on the material inventory information and the preset safety stock threshold, determine whether the material inventory information is less than the preset safety stock threshold. If the material inventory information is less than the preset safety stock threshold, the cause of the abnormality is determined to be a material abnormality. Based on the order queue information, determine whether a new order with a higher priority than the current order information has been inserted into the order queue information; If a new order with a higher priority than the current order is inserted into the order queue, the cause of the abnormality is determined to be an order abnormality.

[0012] The beneficial effects of adopting the above-mentioned further solutions are as follows: By integrating equipment sensor data with intelligent diagnostic models, early warnings of potential equipment failures are achieved, effectively avoiding production interruptions and work-in-process inventory buildup caused by sudden equipment shutdowns. Simultaneously, combined with real-time monitoring of material inventory, early warnings can be proactively triggered before materials reach safety thresholds, allowing buffer time for procurement and distribution. Through dynamic monitoring of the order queue, high-priority order change events such as emergency order insertions are identified and responded to, ensuring the production plan's rapid adaptability to changes in external demand.

[0013] Furthermore, for any of the aforementioned order information, determining a new scheduling scheme based on the cause of the anomaly, preset anomaly policy rules, and scheduling scheme includes: If the cause of the anomaly is a device malfunction, then devices of the same type as the malfunctioning device in the packaging production line and currently in a normal or low-load state are selected as candidate backup devices; based on preset device matching rules, a target backup device is determined from at least one candidate backup device; the scheduling task corresponding to the order information is reassigned to the target backup device, and a new scheduling scheme is generated based on the current available time window of the target backup device and the assigned scheduling task; If the cause of the anomaly is an order anomaly, then based on the order queue information, the current status of each device on the packaging production line, and the progress of the tasks in production, resources will be reallocated for all unfinished orders to generate a new scheduling scheme. If the cause of the anomaly is a material anomaly, the estimated replenishment time of the abnormal material is obtained from the material management system; based on the priority, planned start time, and estimated replenishment time of the order information, a deferred execution time window for the order information is determined; if the deferred execution time window is greater than the estimated replenishment time, the planned start time of the order information is postponed to after the estimated replenishment time to obtain a new scheduling scheme; if the deferred execution time window is not greater than the estimated replenishment time, a prompt message is sent to the management personnel of the material corresponding to the order information.

[0014] The beneficial effects of adopting the above-mentioned further solutions are as follows: When equipment malfunctions, a target backup device is selected from the same type of normal or low-load equipment, tasks are reallocated, and a new scheduling plan is generated to ensure that production continues unaffected by equipment malfunctions; when order malfunctions occur, resources can be reallocated and new plans generated based on the order queue, equipment status, and progress of in-production tasks to adapt to order changes and ensure orderly production; when material malfunctions occur, the order plan start time can be flexibly adjusted by obtaining the estimated replenishment time and determining the time window for delayed execution to generate a new plan. If adjustment is not possible, management personnel are notified in a timely manner to effectively address material supply issues and improve production stability and order delivery rate.

[0015] Furthermore, the step of determining the order priority task based on multiple process tasks corresponding to each order information includes: Based on the complexity of each process task, the number of equipment, the current status, and the material inventory information corresponding to each order information, a first weight is determined for each order information. A second weight is determined for each order based on its delivery deadline. Based on the urgency level of each order, a third weight is determined for each order. A fourth weight is determined for each order based on the product quantity and value of each order. Based on the customer importance corresponding to each order information, a fifth weight is determined for each order information; The priority of order information is determined based on the first, second, third, fourth, and fifth weights of each order information. Based on the corresponding tasks of each order and the preset process requirement mapping table, the process requirements and working hours are determined. Based on the priority, process requirements, and working hours of each order, priority tasks for each order are determined.

[0016] The beneficial effects of adopting the above-mentioned further solutions are: by comprehensively considering the complexity of the corresponding process tasks, the number and status of equipment, material inventory information, delivery deadline, urgency level, product quantity and value, and the importance of the corresponding customer, the priority of orders is determined, and the importance and urgency of orders are assessed. This provides a more accurate basis for subsequent resource allocation and scheduling, helps to improve equipment utilization, shorten order delivery cycle, reduce process connection time, realize dynamic optimization of production resources, and improve the overall operating efficiency and order delivery capability of the production line.

[0017] Furthermore, based on the current status of each device on the packaging production line, order priority tasks, and a preset multi-objective optimization algorithm, resource allocation is performed on each order information to obtain a scheduling scheme, including: Based on the current status of each device on the packaging production line and the order priority task, with the objective functions of maximizing equipment utilization, minimizing order delivery cycle, and minimizing process connection time, and with the constraints of ensuring that the equipment load rate does not exceed the corresponding load threshold of the device and that the equipment accuracy matches the process requirements, a multi-objective optimization algorithm is used to generate a Pareto optimal solution set. The Pareto optimal solution set includes multiple scheduling schemes, and each scheduling scheme includes the equipment allocated to each process corresponding to each order information and the planned start and end times. Obtain the set decision strategy, and determine the weight vector information of each scheme in the Pareto front solution set for evaluation within the current scheduling period based on the set decision strategy; Based on the weight vector information, the weight value of each scheduling scheme in the Pareto optimal solution set is determined, and the scheduling scheme with the optimal weight value is taken as the final scheduling scheme.

[0018] The beneficial effects of adopting the above-mentioned further scheme are as follows: by taking the maximization of equipment utilization, the shortest order delivery cycle, and the minimization of process connection time as objective functions, and setting constraints on equipment load rate and equipment accuracy, a Pareto optimal solution set is generated by using a multi-objective optimization algorithm. This can comprehensively consider multiple key factors in production scheduling and achieve optimal resource allocation. By obtaining the decision strategy to determine the weight vector information, and then determining the scheduling scheme with the optimal weighted value, it is helpful to select the most suitable scheduling scheme according to actual needs.

[0019] Furthermore, based on the current state of each device on the packaging production line and the order priority task, with the objective functions of maximizing equipment utilization, minimizing order delivery cycle, and minimizing process connection time, and with the constraints of ensuring that the equipment load rate does not exceed the corresponding load threshold and that the equipment accuracy matches the process requirements, a multi-objective optimization algorithm is used to generate a Pareto optimal solution set, including: S21. Within the solution space consisting of all order information, process tasks, available equipment, and time windows of available equipment, a population is randomly initialized. The population includes multiple individuals, each of which represents a complete scheduling scheme. The scheduling scheme defines the equipment allocated to each process task and the planned start time of the equipment. S22, based on the sequential constraints between processes, decodes the code of each individual to generate multiple scheduling schemes; S23, based on a pre-constructed multi-objective function, calculate the fitness of the scheduling scheme represented by each individual in the current iteration; S24. Based on the fitness of all individuals in the current iteration, perform non-dominated sorting on the population and divide the population into Pareto fronts at different levels to obtain the divided population. S25, For all individuals at the same Pareto front level, calculate the crowding distance for each individual in the current iteration, whereby the crowding distance is used to quantify the distribution density of other individuals around that individual; S26, Based on each Pareto front level and crowding distance of the divided population, perform tournament selection, simulated binary crossover and polynomial mutation operations on the divided population to generate the offspring population of the current iteration; S27, determine whether any individual in the current iteration of the offspring population violates the constraints. If there is an individual that violates the constraints, then apply a penalty term to the fitness of the individual that violates the constraints to obtain the offspring population after constraint processing. S28. Merge the constrained offspring population and the divided population to obtain a merged population. Select a predetermined number of individuals from the merged population based on non-dominated sorting and crowding distance to obtain a new generation population. S29, take the new generation of population as the population for the next iteration of the current iteration, and execute S23 to S28 until the preset algorithm termination condition is met. Take the scheduling scheme represented by all individuals in the population at the first Pareto front in the current iteration period as the Pareto optimal solution set.

[0020] The beneficial effects of adopting the above-mentioned further scheme are as follows: Through non-dominated sorting, the algorithm can identify a set of non-dominated solutions that perform well in multiple conflicting objectives such as equipment utilization, delivery cycle, and process connection time in each generation of the population, effectively solving the limitation of traditional single-objective optimization that cannot take into account the interests of multiple parties. The introduction of a crowding distance mechanism ensures the distribution and diversity of solutions during the iteration process, avoiding getting trapped in local optima, thus enabling the exploration of a broader and more balanced set of Pareto optimal scheduling schemes. Combining operations such as tournament selection, simulated binary crossover, and polynomial mutation improves the algorithm's global search capability and convergence speed. Simultaneously, by imposing fitness penalties on individuals that violate constraints, it ensures that the final set of generated scheduling schemes not only has superior performance but also strictly conforms to the physical and technological constraints in actual production, possessing extremely high feasibility and engineering practical value.

[0021] Furthermore, it also includes: Based on a set periodic statistical analysis, the equipment utilization rate, order delivery rate, anomaly rate, and production efficiency of the packaging production line are measured. The equipment utilization rate includes the average utilization rate and multiple single equipment utilization rates, and the order delivery rate represents the proportion of orders delivered on time. Based on a preset time series model, the equipment utilization rate, order delivery rate, anomaly rate, and production efficiency of the packaging production line, the trend of indicator changes, capacity, and equipment failure risk of the packaging production line are predicted. The trends in the indicators, capacity, and equipment failure risks of the packaging production line are sent to the terminal of the management personnel of the packaging production line. The beneficial effects of adopting the above-mentioned further solution are: based on the set periodic statistics of equipment utilization rate, order delivery rate, anomaly rate and production efficiency of the packaging production line, the trend of production line indicator changes, capacity and equipment failure risk are predicted based on the preset time series model, and the prediction results are sent to the management personnel to realize the statistical analysis and trend prediction of production line data, provide accurate decision-making suggestions for management personnel, reduce trial and error costs, and help the production line to be continuously optimized.

[0022] Secondly, this application provides a semiconductor equipment packaging production line scheduling device, which adopts the following technical solution: A semiconductor equipment packaging production line scheduling device, comprising: The order management module is used to acquire multiple order information, including product model, packaging process requirements, order quantity, delivery deadline, and urgency level. Based on the multiple order information and preset process route rules, each order information is broken down into multiple process tasks. Each process task represents a production step with equipment requirements, time requirements, and process parameter requirements. Based on the multiple process tasks corresponding to each order information, order priority tasks are determined. The order priority tasks include the process requirements, time requirements, and priority of each order. The equipment status monitoring module is used to acquire monitoring information of each piece of equipment on the packaging production line. Based on the monitoring information of each piece of equipment, the set threshold information and the fault diagnosis model, the current status of each piece of equipment on the packaging production line is determined. The monitoring information includes temperature data, vibration data, load data, process data and operating data. The current status is normal operation, low load, high load, fault warning or fault shutdown. The resource optimization and allocation module is used to allocate resources for each order based on the current status of each device on the packaging production line, order priority tasks, and a preset multi-objective optimization algorithm, thereby obtaining a scheduling scheme. The control module controls the equipment on the corresponding production line based on the scheduling scheme, so that the equipment on the corresponding production line produces the product corresponding to each order information.

[0023] Thirdly, this application provides an electronic device that adopts the following technical solution: An electronic device includes a memory and a processor, wherein the memory stores a computer program that can be loaded by the processor and executed as described in any of the first aspects of a semiconductor device packaging production line scheduling method.

[0024] Additional aspects and advantages of this application will be set forth in part in the description which follows, and will become apparent from the description or may be learned by practice of this application. Attached Figure Description

[0025] Figure 1 This is a flowchart illustrating a semiconductor equipment packaging production line scheduling method according to an embodiment of the present invention. Figure 2 This is a schematic diagram of the structure of a semiconductor equipment packaging production line scheduling device according to an embodiment of the present invention; Figure 3 This is a schematic diagram of the structure of an electronic device provided in one embodiment of the present invention. Detailed Implementation

[0026] To make the objectives, technical solutions, and advantages of the embodiments of this application clearer, the technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0027] Furthermore, the term "and / or" in this article is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A existing alone, A and B existing simultaneously, or B existing alone. Additionally, the character " / " in this article, unless otherwise specified, generally indicates that the preceding and following related objects have an "or" relationship.

[0028] This application provides a method for scheduling a semiconductor equipment packaging production line. This method can be executed by an electronic device, which can be a server or a mobile terminal device. The server can be an independent physical server, a server cluster or distributed system composed of multiple physical servers, or a cloud server that provides cloud computing services. The mobile terminal device can be a laptop computer, a desktop computer, etc., but is not limited to these.

[0029] like Figure 1 As shown, a semiconductor equipment packaging production line scheduling method mainly includes: S1. Obtain multiple order information, including product model, packaging process requirements, order quantity, delivery deadline, and urgency level; based on the multiple order information and preset process route rules, break down each order information into multiple process tasks, each process task representing a production step with equipment requirements, time requirements, and process parameter requirements; based on the multiple process tasks corresponding to each order information, determine the order priority task, the order priority task including the process requirements, time requirements, and priority of each order; In this embodiment, order information can be obtained through manual input, importing from an Excel spreadsheet, or integration with an enterprise ERP system to adapt to different enterprise order management models. Each order is broken down into multiple process tasks, each with specific equipment, time, and process parameter requirements. For example, a SiP packaging order can be broken down into processes such as "chip pickup → bonding → packaging → electrical performance testing → appearance inspection," each with corresponding equipment requirements, time requirements, and process parameters.

[0030] In this embodiment of the application, the order priority task is determined based on multiple process tasks corresponding to each order information, including: Based on the complexity of each process task, the number of equipment, the current status, and the material inventory information corresponding to each order information, a first weight is determined for each order information. A second weight is determined for each order based on its delivery deadline. Based on the urgency level of each order, a third weight is determined for each order. A fourth weight is determined for each order based on the product quantity and value of each order. Based on the customer importance corresponding to each order information, a fifth weight is determined for each order information; The priority of order information is determined based on the first, second, third, fourth, and fifth weights of each order information. Based on the corresponding tasks of each order and the preset process requirement mapping table, the process requirements and working hours are determined. Based on the priority, process requirements, and working hours of each order, priority tasks for each order are determined.

[0031] In the above implementation, the first weight is determined based on process complexity, resource (equipment) scarcity, and material availability. For example: process complexity is determined by setting a basic complexity coefficient for different processes based on historical data or process standards. Resource (equipment) scarcity: if the equipment required for an order is currently under high load or in short supply, it is given a higher weight for priority scheduling. Material availability: it checks whether all materials required for the order are at or above the safety stock level. If they are available, the weight is normal; if there is a shortage, the weight is reduced.

[0032] The second weight is usually calculated as a reciprocal, with the weight increasing dramatically as the delivery date approaches.

[0033] The third weight can be obtained through direct mapping.

[0034] The fourth weight can be set based on order amount, profit margin, or whether the product is a new process or a strategic product.

[0035] The fifth weight can be assigned a fixed coefficient based on the customer classification system (such as VIP, strategic, and ordinary customers).

[0036] S2, acquire monitoring information of each device on the packaging production line, and determine the current status of each device on the packaging production line based on the monitoring information of each device, the set threshold information and the fault diagnosis model. The monitoring information includes temperature data, vibration data, load data, process data and operating data. The current status is normal operation, low load, high load, fault warning or fault shutdown. In this embodiment, the temperature sensor can monitor the temperature of the device in real time, the vibration sensor can detect the vibration of the device, and the load sensor can acquire the load data of the device, etc.

[0037] Temperature sensors (measuring range -20~300℃, accuracy ±0.5℃), vibration sensors (measuring range 0~500Hz, accuracy ±0.1Hz), load sensors (measuring range 0~100% load rate, accuracy ±1%), and process parameter sensors (such as bonding temperature and injection pressure) are installed on key equipment (bonding machines, injection molding machines, testing equipment, etc.) on each production line to collect equipment operation data in real time. An edge computing gateway (supporting Modbus and Profinet communication protocols) is used to convert the analog signals collected by the sensors into digital signals, with a sampling frequency of 1 time / second to ensure real-time data transmission.

[0038] Based on monitoring information, set threshold information, and fault diagnosis models for each device, the current status of each device on the packaging production line is determined, such as normal operation, low load, high load, fault warning, or fault shutdown. For example, when vibration data exceeds the set vibration threshold and the fault diagnosis model determines that the device is abnormal, it can be determined that the device is in a fault warning state.

[0039] The fault diagnosis model can be a machine learning-based fault classification model trained based on historical monitoring data.

[0040] S3, based on the current status of each device on the packaging production line, the order priority task, and the preset multi-objective optimization algorithm, resource allocation is performed on each order information to obtain a scheduling scheme; In this embodiment of the application, specifically, the step of allocating resources to each order information based on the current status of each device on the packaging production line, order priority tasks, and a preset multi-objective optimization algorithm to obtain a scheduling scheme includes: Based on the current status of each device on the packaging production line and the order priority task, with the objective functions of maximizing equipment utilization, minimizing order delivery cycle, and minimizing process connection time, and with the constraints of ensuring that the equipment load rate does not exceed the corresponding load threshold of the device and that the equipment accuracy matches the process requirements, a multi-objective optimization algorithm is used to generate a Pareto optimal solution set. The Pareto optimal solution set includes multiple scheduling schemes, and each scheduling scheme includes the equipment allocated to each process corresponding to each order information and the planned start and end times. Obtain the set decision strategy, and determine the weight vector information of each scheme in the Pareto front solution set for evaluation within the current scheduling period based on the set decision strategy; Based on the weight vector information, the weight value of each scheduling scheme in the Pareto optimal solution set is determined, and the scheduling scheme with the optimal weight value is taken as the final scheduling scheme.

[0041] In the above embodiments, based on the current state of each device on the packaging production line and the order priority task, with the objective functions of maximizing equipment utilization, minimizing order delivery cycle, and minimizing process connection time, and with the constraints of ensuring that the equipment load rate does not exceed the corresponding load threshold and that the equipment accuracy matches the process requirements, a multi-objective optimization algorithm is used to generate a Pareto optimal solution set, including: S21. Within the solution space consisting of all order information, process tasks, available equipment, and time windows of available equipment, a population is randomly initialized. The population includes multiple individuals, each of which represents a complete scheduling scheme. The scheduling scheme defines the equipment allocated to each process task and the planned start time of the equipment. S22, based on the sequential constraints between processes, decodes the code of each individual to generate multiple scheduling schemes; S23, based on a pre-constructed multi-objective function, calculate the fitness of the scheduling scheme represented by each individual in the current iteration; S24. Based on the fitness of all individuals in the current iteration, perform non-dominated sorting on the population and divide the population into Pareto fronts at different levels to obtain the divided population. S25, For all individuals at the same Pareto front level, calculate the crowding distance for each individual in the current iteration, whereby the crowding distance is used to quantify the distribution density of other individuals around that individual; S26, Based on each Pareto front level and crowding distance of the divided population, perform tournament selection, simulated binary crossover and polynomial mutation operations on the divided population to generate the offspring population of the current iteration; S27, determine whether any individual in the current iteration of the offspring population violates the constraints. If there is an individual that violates the constraints, then apply a penalty term to the fitness of the individual that violates the constraints to obtain the offspring population after constraint processing. S28. Merge the constrained offspring population and the divided population to obtain a merged population. Select a predetermined number of individuals from the merged population based on non-dominated sorting and crowding distance to obtain a new generation population. S29, take the new generation of population as the population for the next iteration of the current iteration, and execute S23 to S28 until the preset algorithm termination condition is met. Take the scheduling scheme represented by all individuals in the population at the first Pareto front in the current iteration period as the Pareto optimal solution set.

[0042] In the above embodiments, obtaining the set decision strategy and determining the weight vector information of each scheme in the Pareto front solution set for evaluation within the current scheduling period based on the set decision strategy includes: Real-time acquisition of production line dynamic data, including the overall average load rate of the production line, the length of the work-in-process queue, the number of backlog tasks in key bottleneck processes, and the proportion of high-priority orders to the total number of orders to be scheduled. The production line dynamic data is matched with a preset configuration strategy. The preset configuration strategy includes multiple strategies corresponding to different production scenarios. The configuration strategy defines the weight distribution relationship between the three optimization objectives of maximizing equipment utilization, minimizing the overall order delivery cycle, and minimizing the total waiting time between processes under different production scenarios. Based on the matching results, the corresponding dynamic weight vector is output.

[0043] S4, based on the scheduling scheme, control the equipment on the corresponding production line so that the equipment on the corresponding production line can produce the product corresponding to each order information.

[0044] In this embodiment of the application, the optimized solution is converted into specific task instructions, including equipment number, task content (e.g., "bonding machine #3: process 1000 chips of type A order, process parameters: temperature 220℃, pressure 30N"), start time, and completion time limit, and the equipment on the corresponding production line is scheduled according to the task instructions.

[0045] As a further optional implementation in the embodiments of this application, for any of the order information, the process of executing the scheduling scheme further includes: Obtain the production progress information sent by the corresponding device in the scheduling scheme, the production progress information including the completed quantity and the remaining working hours; Based on the order information and the production progress information, the current progress value is determined, and based on the priority, working hours and scheduling scheme corresponding to the order information, the planned progress threshold corresponding to the order information is calculated. If the current progress value is less than the planned progress threshold corresponding to the order information, a prompt message is sent to the management personnel of the equipment corresponding to the order information, and the cause of the abnormality is determined based on the monitoring information, material inventory information and order queue information of the corresponding equipment in the scheduling scheme. The cause of the abnormality includes any one of equipment abnormality, order abnormality and material abnormality. Based on the aforementioned causes of the anomaly, the preset anomaly policy rules, and the scheduling scheme, a new scheduling scheme is determined, and the equipment on the corresponding production line is controlled based on the new scheduling scheme. After all the process tasks of the order information are completed, the order information is marked as completed, and the production data related to the order information is stored. The production data includes total time, equipment utilization rate, and number of anomalies.

[0046] In the above embodiments, for any of the order information, determining the cause of the anomaly based on the monitoring information of the corresponding device, material inventory information, and order queue information in the scheduling scheme includes: Based on the monitoring information, set threshold information and fault diagnosis model of the corresponding device in the scheduling scheme, it is determined whether at least one parameter in the monitoring information exceeds the corresponding threshold range, or whether the fault diagnosis model outputs an abnormality. If at least one parameter in the monitoring information exceeds the corresponding threshold range, or if the fault diagnosis model outputs an abnormality, then the cause of the abnormality is determined to be equipment malfunction. Based on the material inventory information and the preset safety stock threshold, determine whether the material inventory information is less than the preset safety stock threshold. If the material inventory information is less than the preset safety stock threshold, the cause of the abnormality is determined to be a material abnormality. Based on the order queue information, determine whether a new order with a higher priority than the current order information has been inserted into the order queue information; If a new order with a higher priority than the current order is inserted into the order queue, the cause of the abnormality is determined to be an order abnormality.

[0047] In the above embodiments, for any of the order information, determining a new scheduling scheme based on the cause of the anomaly, preset anomaly policy rules, and scheduling scheme includes: If the cause of the anomaly is a device malfunction, then devices of the same type as the malfunctioning device in the packaging production line and currently in a normal or low-load state are selected as candidate backup devices; based on preset device matching rules, a target backup device is determined from at least one candidate backup device; the scheduling task corresponding to the order information is reassigned to the target backup device, and a new scheduling scheme is generated based on the current available time window of the target backup device and the assigned scheduling task; If the cause of the anomaly is an order anomaly, then based on the order queue information, the current status of each device on the packaging production line, and the progress of the tasks in production, resources will be reallocated for all unfinished orders to generate a new scheduling scheme. If the cause of the anomaly is a material anomaly, the estimated replenishment time of the abnormal material is obtained from the material management system; based on the priority, planned start time, and estimated replenishment time of the order information, a deferred execution time window for the order information is determined; if the deferred execution time window is greater than the estimated replenishment time, the planned start time of the order information is postponed to after the estimated replenishment time to obtain a new scheduling scheme; if the deferred execution time window is not greater than the estimated replenishment time, a prompt message is sent to the management personnel of the material corresponding to the order information.

[0048] In the above implementation, the process of screening candidate devices will take into account factors such as the device's load, maintenance records, and historical failure count to ensure the reliability and availability of the candidate backup devices.

[0049] Based on preset equipment matching rules, target backup equipment is determined from candidate backup equipment. Equipment matching rules may include factors such as equipment accuracy level, production capacity, and distance from the faulty equipment. For example, equipment with high accuracy level, large production capacity, and proximity to the faulty equipment is given priority as target backup equipment to reduce the time and transportation costs associated with equipment switchover.

[0050] As a further optional implementation method in the embodiments of this application, it also includes: Based on a set periodic statistical analysis, the equipment utilization rate, order delivery rate, anomaly rate, and production efficiency of the packaging production line are measured. The equipment utilization rate includes the average utilization rate and multiple single equipment utilization rates, and the order delivery rate represents the proportion of orders delivered on time. Based on a preset time series model, the equipment utilization rate, order delivery rate, anomaly rate, and production efficiency of the packaging production line, the trend of indicator changes, capacity, and equipment failure risk of the packaging production line are predicted. The trends in the indicators, capacity, and equipment failure risks of the packaging production line are sent to the terminal of the management personnel of the packaging production line. In this embodiment of the application, the preset time series analysis model can adopt the classic differential integrated moving average autoregressive model or its variants. The model learns the trend, seasonality and periodicity of historical data, and then makes rolling predictions of the KPI values ​​for the next period. The prediction results include: Indicator trends, such as the prediction that the average utilization rate of a certain type of key equipment will rise or fall in the coming week; Capacity forecasting is the process of estimating the total output that can be completed within a specified future time period (such as next week) based on equipment utilization trends and scheduling plans. Equipment failure risk is mainly identified and warned by analyzing the trends of historical anomaly rates, vibration, or temperature data of specific equipment and extrapolating them.

[0051] This method automatically breaks down orders into process tasks with time, equipment, and process constraints, and dynamically prioritizes them based on multiple dimensions such as delivery deadlines and urgency levels, ensuring that high-value and urgent orders are processed first. By utilizing various sensors and fault diagnosis models distributed across key equipment nodes, real-time monitoring and early warning of equipment health status are achieved, providing a precise on-site data foundation for dynamic scheduling. Through resource allocation using a multi-objective optimization algorithm based on real-time equipment status and order priorities, an optimal scheduling scheme can be generated, thereby improving overall equipment utilization and capacity, and shortening the average order delivery cycle.

[0052] Figure 2 A schematic diagram of a semiconductor equipment packaging production line scheduling device 200 is shown.

[0053] like Figure 2 As shown, a semiconductor equipment packaging production line scheduling device 200 mainly includes: The order management module 201 is used to acquire multiple order information, including product model, packaging process requirements, order quantity, delivery deadline, and urgency level; based on the multiple order information and preset process route rules, each order information is divided into multiple process tasks, each process task representing a production step with equipment requirements, time requirements, and process parameter requirements; based on the multiple process tasks corresponding to each order information, order priority tasks are determined, the order priority tasks including the process requirements, time requirements, and priority of each order; The equipment status monitoring module 202 is used to acquire monitoring information of each piece of equipment on the packaging production line. Based on the monitoring information of each piece of equipment, the set threshold information and the fault diagnosis model, the current status of each piece of equipment on the packaging production line is determined. The monitoring information includes temperature data, vibration data, load data, process data and operating data. The current status is normal operation, low load, high load, fault warning or fault shutdown. Resource optimization and allocation module 203 is used to allocate resources to each order based on the current status of each device on the packaging production line, order priority tasks, and a preset multi-objective optimization algorithm, thereby obtaining a scheduling scheme; The control module 204 controls the equipment on the corresponding production line based on the scheduling scheme, so that the equipment on the corresponding production line produces the product corresponding to each order information.

[0054] In one example, the module in any of the above devices may be one or more integrated circuits configured to implement the above methods, such as one or more application-specific integrated circuits (ASICs), or one or more digital signal processors (DSPs), or one or more field-programmable gate arrays (FPGAs), or a combination of at least two of these integrated circuit forms.

[0055] For example, when modules in a device can be implemented via a processing element scheduler, the processing element can be a general-purpose processor, such as a central processing unit (CPU) or other processor capable of calling programs. Alternatively, these modules can be integrated together as a system-on-a-chip (SOC).

[0056] In this application, various objects such as messages / information / devices / network elements / systems / apparatus / actions / operations / processes / concepts may be named. It is understood that these specific names do not constitute a limitation on the relevant objects. The names may be changed depending on the scenario, context, or usage habits. The understanding of the technical meaning of the technical terms in this application should be mainly determined from their functions and technical effects embodied / performed in the technical solution.

[0057] Those skilled in the art will understand that, for the sake of convenience and brevity, the specific working processes of the systems, devices, and modules described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0058] Those skilled in the art will recognize that the modules and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0059] Figure 3 This is a structural block diagram of an electronic device 300 according to an embodiment of this application.

[0060] like Figure 3 As shown, the electronic device 300 includes a processor 301 and a memory 302, and may further include one or more of an information input / output (I / O) interface 303, a communication component 304, and a communication bus 305.

[0061] The processor 301 controls the overall operation of the electronic device 300 to complete all or part of the steps in the semiconductor device packaging production line scheduling method described above. The memory 302 stores various types of data to support the operation of the electronic device 300. This data may include, for example, instructions for any application or method operating on the electronic device 300, as well as application-related data. The memory 302 can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as one or more of Static Random Access Memory (SRAM), Electrically Erasable Programmable Read-Only Memory (EEPROM), Erasable Programmable Read-Only Memory (EPROM), Programmable Read-Only Memory (PROM), Read-Only Memory (ROM), magnetic storage, flash memory, magnetic disk, or optical disk.

[0062] I / O interface 303 provides an interface between processor 301 and other interface modules, such as keyboards, mice, and buttons. These buttons can be virtual or physical. Communication component 304 is used to test wired or wireless communication between electronic device 300 and other devices. Wireless communication includes Wi-Fi, Bluetooth, Near Field Communication (NFC), 2G, 3G, or 4G, or a combination thereof. Therefore, the corresponding communication component 304 may include a Wi-Fi component, a Bluetooth component, and an NFC component.

[0063] The communication bus 305 may include a path for transmitting information between the aforementioned components. The communication bus 305 may be a PCI (Peripheral Component Interconnect) bus or an EISA (Extended Industry Standard Architecture) bus, etc. The communication bus 305 may be divided into an address bus, a data bus, a control bus, etc.

[0064] The electronic device 300 may be implemented by one or more application-specific integrated circuits (ASICs), digital signal processors (DSPs), digital signal processing devices (DSPDs), programmable logic devices (PLDs), field-programmable gate arrays (FPGAs), controllers, microcontrollers, microprocessors, or other electronic components to execute the semiconductor equipment packaging production line scheduling method given in the above embodiments.

[0065] The terms “comprising,” “including,” or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus.

[0066] The above description is merely a preferred embodiment of this application and an explanation of the technical principles employed. Those skilled in the art should understand that the scope of this application is not limited to technical solutions formed by specific combinations of the above-described technical features, but should also cover other technical solutions formed by arbitrary combinations of the above-described technical features or their equivalents without departing from the foregoing application concept. For example, technical solutions formed by substituting the above features with (but not limited to) technical features with similar functions claimed in this application.

Claims

1. A method for scheduling a semiconductor equipment packaging production line, characterized in that, include: The system acquires multiple order information entries, including product model, packaging process requirements, order quantity, delivery deadline, and urgency level. Based on these multiple order information entries and preset process route rules, each order information entry is broken down into multiple process tasks. Each process task represents a production step with equipment requirements, time requirements, and process parameter requirements. Based on the multiple process tasks corresponding to each order information entry, an order priority task is determined, which includes the process requirements, time requirements, and priority of each order. The monitoring information of each device on the packaging production line is obtained. Based on the monitoring information of each device, the set threshold information and the fault diagnosis model, the current status of each device on the packaging production line is determined. The monitoring information includes temperature data, vibration data, load data, process data and operation data. The current status is normal operation, low load, high load, fault warning or fault shutdown. Based on the current status of each device on the packaging production line, the order priority task, and the preset multi-objective optimization algorithm, resource allocation is performed on each order information to obtain a scheduling scheme, which includes the scheduling task for each order information. The scheduling scheme controls the equipment on the corresponding production line so that the equipment on the corresponding production line can produce the product corresponding to each order information.

2. The semiconductor equipment packaging production line scheduling method according to claim 1, characterized in that, For any of the order information, the process of executing the scheduling scheme also includes: Obtain the production progress information sent by the corresponding device in the scheduling scheme, the production progress information including the completed quantity and the remaining working hours; Based on the order information and the production progress information, the current progress value is determined, and based on the priority, working hours and scheduling scheme corresponding to the order information, the planned progress threshold corresponding to the order information is calculated. If the current progress value is less than the planned progress threshold corresponding to the order information, a prompt message is sent to the management personnel of the equipment corresponding to the order information, and the cause of the abnormality is determined based on the monitoring information, material inventory information and order queue information of the corresponding equipment in the scheduling scheme. The cause of the abnormality includes any one of equipment abnormality, order abnormality and material abnormality. Based on the aforementioned causes of the anomaly, the preset anomaly policy rules, and the scheduling scheme, a new scheduling scheme is determined, and the equipment on the corresponding production line is controlled based on the new scheduling scheme. After all the process tasks of the order information are completed, the order information is marked as completed, and the production data related to the order information is stored. The production data includes total time, equipment utilization rate, and number of anomalies.

3. The semiconductor equipment packaging production line scheduling method according to claim 2, characterized in that, For any of the aforementioned order information, the determination of the cause of the anomaly based on the monitoring information of the corresponding device, material inventory information, and order queue information in the scheduling scheme includes: Based on the monitoring information, set threshold information and fault diagnosis model of the corresponding device in the scheduling scheme, it is determined whether at least one parameter in the monitoring information exceeds the corresponding threshold range, or whether the fault diagnosis model outputs an abnormality. If at least one parameter in the monitoring information exceeds the corresponding threshold range, or if the fault diagnosis model outputs an abnormality, then the cause of the abnormality is determined to be equipment malfunction. Based on the material inventory information and the preset safety stock threshold, determine whether the material inventory information is less than the preset safety stock threshold. If the material inventory information is less than the preset safety stock threshold, the cause of the abnormality is determined to be a material abnormality. Based on the order queue information, determine whether a new order with a higher priority than the current order information has been inserted into the order queue information; If a new order with a higher priority than the current order is inserted into the order queue, the cause of the abnormality is determined to be an order abnormality.

4. The semiconductor equipment packaging production line scheduling method according to claim 3, characterized in that, For any of the aforementioned order information, determining a new scheduling scheme based on the cause of the anomaly, preset anomaly policy rules, and scheduling scheme includes: If the cause of the anomaly is a device malfunction, then devices of the same type as the malfunctioning device in the packaging production line and currently in a normal or low-load state are selected as candidate backup devices; based on preset device matching rules, a target backup device is determined from at least one candidate backup device; the scheduling task corresponding to the order information is reassigned to the target backup device, and a new scheduling scheme is generated based on the current available time window of the target backup device and the assigned scheduling task; If the cause of the anomaly is an order anomaly, then based on the order queue information, the current status of each device on the packaging production line, and the progress of the tasks in production, resources will be reallocated for all unfinished orders to generate a new scheduling scheme. If the cause of the anomaly is a material anomaly, the estimated replenishment time of the abnormal material is obtained from the material management system; based on the priority, planned start time, and estimated replenishment time of the order information, a deferred execution time window for the order information is determined; if the deferred execution time window is greater than the estimated replenishment time, the planned start time of the order information is postponed to after the estimated replenishment time to obtain a new scheduling scheme; if the deferred execution time window is not greater than the estimated replenishment time, a prompt message is sent to the management personnel of the material corresponding to the order information.

5. A semiconductor equipment packaging production line scheduling method according to claim 1, characterized in that, The step of determining the order priority task based on multiple process tasks corresponding to each order information includes: Based on the complexity of each process task, the number of equipment, the current status, and the material inventory information corresponding to each order information, a first weight is determined for each order information. A second weight is determined for each order based on its delivery deadline. Based on the urgency level of each order, a third weight is determined for each order. A fourth weight is determined for each order based on the product quantity and value of each order. Based on the customer importance corresponding to each order information, a fifth weight is determined for each order information; The priority of order information is determined based on the first, second, third, fourth, and fifth weights of each order information. Based on the corresponding tasks of each order and the preset process requirement mapping table, the process requirements and working hours are determined. Based on the priority, process requirements, and working hours of each order, priority tasks for each order are determined.

6. The semiconductor equipment packaging production line scheduling method according to claim 1, characterized in that, The process involves allocating resources for each order based on the current status of each device on the packaging production line, order priority tasks, and a preset multi-objective optimization algorithm, to obtain a scheduling scheme, including: Based on the current status of each device on the packaging production line and the order priority task, with the objective functions of maximizing equipment utilization, minimizing order delivery cycle, and minimizing process connection time, and with the constraints of ensuring that the equipment load rate does not exceed the corresponding load threshold of the device and that the equipment accuracy matches the process requirements, a multi-objective optimization algorithm is used to generate a Pareto optimal solution set. The Pareto optimal solution set includes multiple scheduling schemes, and each scheduling scheme includes the equipment allocated to each process corresponding to each order information and the planned start and end times. Obtain the set decision strategy, and determine the weight vector information of each scheme in the Pareto front solution set for evaluation within the current scheduling period based on the set decision strategy; Based on the weight vector information, the weight value of each scheduling scheme in the Pareto optimal solution set is determined, and the scheduling scheme with the optimal weight value is taken as the final scheduling scheme.

7. A semiconductor equipment packaging production line scheduling method according to claim 6, characterized in that, Based on the current state of each device on the packaging production line and the order priority tasks, the algorithm uses maximizing equipment utilization, minimizing order delivery cycle, and minimizing process connection time as objective functions, with constraints including ensuring that the equipment load rate does not exceed the corresponding load threshold and that the equipment accuracy matches the process requirements. A multi-objective optimization algorithm is then employed to generate a Pareto optimal solution set, including: S21. Within the solution space consisting of all order information, process tasks, available equipment, and time windows of available equipment, a population is randomly initialized. The population includes multiple individuals, each of which represents a complete scheduling scheme. The scheduling scheme defines the equipment allocated to each process task and the planned start time of the equipment. S22, based on the sequential constraints between processes, decodes the code of each individual to generate multiple scheduling schemes; S23, based on a pre-constructed multi-objective function, calculate the fitness of the scheduling scheme represented by each individual in the current iteration; S24. Based on the fitness of all individuals in the current iteration, perform non-dominated sorting on the population and divide the population into Pareto fronts at different levels to obtain the divided population. S25, For all individuals at the same Pareto front level, calculate the crowding distance for each individual in the current iteration, whereby the crowding distance is used to quantify the distribution density of other individuals around that individual; S26, Based on each Pareto front level and crowding distance of the divided population, perform tournament selection, simulated binary crossover and polynomial mutation operations on the divided population to generate the offspring population of the current iteration; S27, determine whether any individual in the current iteration of the offspring population violates the constraints. If there is an individual that violates the constraints, then apply a penalty term to the fitness of the individual that violates the constraints to obtain the offspring population after constraint processing. S28. Merge the constrained offspring population and the divided population to obtain a merged population. Select a predetermined number of individuals from the merged population based on non-dominated sorting and crowding distance to obtain a new generation population. S29, take the new generation of population as the population for the next iteration of the current iteration, and execute S23 to S28 until the preset algorithm termination condition is met. Take the scheduling scheme represented by all individuals in the population at the first Pareto front in the current iteration period as the Pareto optimal solution set.

8. A semiconductor equipment packaging production line scheduling method according to claim 1, characterized in that, Also includes: Based on a set periodic statistical analysis, the equipment utilization rate, order delivery rate, anomaly rate, and production efficiency of the packaging production line are measured. The equipment utilization rate includes the average utilization rate and multiple single equipment utilization rates, and the order delivery rate represents the proportion of orders delivered on time. Based on a preset time series model, the equipment utilization rate, order delivery rate, anomaly rate, and production efficiency of the packaging production line, the trend of indicator changes, capacity, and equipment failure risk of the packaging production line are predicted. The trends in the indicators, capacity, and equipment failure risks of the packaging production line are sent to the terminal of the management personnel of the packaging production line.

9. A semiconductor equipment packaging production line scheduling device, characterized in that, include: The order management module is used to obtain information on multiple orders, including product model, packaging process requirements, order quantity, delivery deadline, and urgency level. Based on multiple order information and preset process route rules, each order information is divided into multiple process tasks, and each process task represents a production step with equipment requirements, time requirements, and process parameter requirements; based on the multiple process tasks corresponding to each order information, order priority tasks are determined, and the order priority tasks include the process requirements, time requirements, and priority of each order; The equipment status monitoring module is used to acquire monitoring information of each piece of equipment on the packaging production line. Based on the monitoring information of each piece of equipment, the set threshold information and the fault diagnosis model, the current status of each piece of equipment on the packaging production line is determined. The monitoring information includes temperature data, vibration data, load data, process data and operating data. The current status is normal operation, low load, high load, fault warning or fault shutdown. The resource optimization and allocation module is used to allocate resources for each order based on the current status of each device on the packaging production line, order priority tasks, and a preset multi-objective optimization algorithm, thereby obtaining a scheduling scheme. The control module controls the equipment on the corresponding production line based on the scheduling scheme, so that the equipment on the corresponding production line produces the product corresponding to each order information.

10. An electronic device, characterized in that, Includes a processor, which is coupled to a memory; The processor is configured to execute a computer program stored in the memory to cause the electronic device to perform the method as described in any one of claims 1 to 8.

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