Work order data analysis system and method for wafer production

Abstract

The invention relates to the technical field of work order data analysis, in particular to a work order data analysis system and method for wafer production, and the method comprises the steps: obtaining the operation data of each piece of production equipment and the corresponding production function data of corresponding equipment, and carrying out the operation efficiency evaluation of the production equipment; the production time prediction method comprises the steps of performing production time prediction on work order data, performing future production equipment operation efficiency prediction on the basis of the work order data and a production equipment operation efficiency evaluation result, performing work order production time prediction on the basis of the work order data and a future production equipment operation efficiency prediction result, and performing production early warning on the basis of a work order production time prediction result. According to the method, the equipment production efficiency reduction caused by the production process is accurately analyzed and predicted, and the work order production time is accurately predicted through the influence on the production line and the production quality in the operation process and the production process, so that the accuracy of production time prediction is improved.

Description

Technical Field

[0001] The present application relates to the technical field of work order data analysis, and in particular to a work order data analysis system and method for wafer production. Background Art

[0002] In the existing work order data analysis process, when determining whether the work order can be completed on time, the completion amount and the speed of task completion are usually simply analyzed to conduct a time analysis. When conducting production, the existing technology does not take into account the reduction in equipment production efficiency caused by the production process, and thus cannot accurately analyze and predict the reduction in equipment production efficiency caused by the production process. The accuracy of the production time prediction is reduced by taking into account the impact on the production line and production quality during operation while taking into account the production process.

[0003] For example, in the prior art (Chinese patent application number CN118821981A), a work order prediction method, a work order prediction device and a storage medium are provided, the method comprising: the work order prediction device determines first incoming work order data based on preset time granularity data, and obtains first prediction data corresponding to the first incoming work order data based on the first incoming work order data and a first prediction model; wherein the first prediction model comprises an LSTM model; first target prediction data is determined based on the first prediction data and the second incoming work order data; N corresponding target time granularity data are determined based on the preset time granularity data; wherein N is a positive integer; a work order prediction result is obtained based on the N target time granularity data, the first target prediction data and the second prediction model; wherein the second prediction model comprises N Conv-Transformers models, thereby reducing the model prediction complexity while improving the model prediction accuracy; the prior art has the problems raised by this application; In view of this, a work order data analysis system and method for wafer production are proposed. Summary of the Invention

[0004] In order to overcome the defects and shortcomings of the existing technology, the present application provides a work order data analysis system and method for chip production, which obtains the operating data of each production equipment and the corresponding production function data of the corresponding equipment to evaluate the operating efficiency of the production equipment, predicts the future operating efficiency of the production equipment based on the work order data and the evaluation results of the operating efficiency of the production equipment, predicts the work order production time based on the work order data and the future production equipment operating efficiency prediction results, and performs production early warning based on the work order production time prediction results. When making production time predictions, the present application accurately analyzes and predicts the reduction in equipment production efficiency caused by the production process, accurately predicts the work order production time by taking into account the impact on the production line and production quality during operation while taking into account the production process, thereby improving the accuracy of production time predictions.

[0005] In order to achieve the above objectives, this application adopts the following technical solutions: In a first aspect, the present application provides a method for analyzing work order data for wafer production, comprising the following steps: S1: Obtain the received work order data, the operation data of each production equipment and the corresponding production function data of the corresponding equipment; S2: Obtain the operating data of each production equipment and the corresponding production function data of the corresponding equipment to evaluate the operating efficiency of the production equipment; S3: Predict future production equipment operating efficiency based on work order data and production equipment operating efficiency evaluation results; S4: Predict work order production time based on work order data and future production equipment operating efficiency prediction results; S5: Provide production warning based on the work order production time prediction results.

[0006] Optionally, the work order data includes quality data, quantity data and production deadline data of the products required on the work order, and the operation data of the production equipment includes the operation data of each equipment on the production line, and the operation data includes production standard speed data, real-time production speed data, real-time production product quality data and various information data of the equipment, wherein the information data includes the operating voltage, current and other types of data reflecting the operating quality of the corresponding equipment, and the corresponding production function data of the corresponding equipment includes various production function data of the corresponding equipment.

[0007] Optionally, the production equipment operating efficiency evaluation includes the following specific steps: S21. Obtain information data from the operating data of the production equipment during the production cycle, and perform production anomaly analysis based on the information data from the operating data of the production equipment during the production cycle. The production anomaly analysis calculation formula is: , where M is the data type of information data, aj is the influence coefficient of the data type of the j-th information data on the equipment operation, xjh is the specific value of the j-th information data in the end time period of the production cycle, xjm is the median value of the safety range of the j-th information data, and xjq is the specific value of the j-th information data in the starting time period of the production cycle; S22. Acquire real-time quality data of products produced by the production equipment during the production cycle, and perform product quality anomaly analysis based on the product quality data. The calculation formula for the product quality anomaly analysis is: , where exp() is the power of the natural constant e, Zq is the average quality data of the products produced by the production equipment in real time at the start of the production cycle, Zh is the average quality data of the products produced by the production equipment in real time at the end of the production cycle, and Zm is the qualified quality data of the products produced; in this step, the impact of the production process on production aging is analyzed through the product production conditions at the start and end of the production cycle; here, in order to avoid the calculated data being too small or negative, exp() is used to quantify the data; S23. Obtain production standard speed data, real-time production speed data, product quality anomaly analysis results, and production anomaly analysis results of the production process to evaluate the production equipment operation efficiency. The production equipment operation efficiency evaluation formula is: , where Xlm is the standard efficiency of the production equipment, T is the production cycle length, and dt is the time integral constant. is the weight of production abnormality proportion, is the weight of the product abnormality ratio, vt is the production speed at time t, and vm is the production standard speed data. In this step, the degree of influence of equipment operation on equipment aging is quantitatively analyzed.

[0008] Optionally, the future production equipment operating efficiency prediction includes the following specific steps: S31. Obtaining quality, quantity, and production deadline data of the product required on the work order, and determining the production speed required for producing the product based on the quantity and production deadline data; Here, we need to analyze the production speed required for the products on the production order, so as to analyze the impact of long-term high-speed operation on equipment and products, and thus analyze the efficiency of product production; S32. Based on the production speed required for the production of the product and the evaluation results of the production equipment operating efficiency, the future production equipment operating efficiency prediction formula is: , where Vs is the production speed required to produce the product, Tm is the future production time, Zs is the quality data of the product required on the work order, and Zm is the qualified quality data of the produced product; Optionally, the work order production time prediction based on the work order data and the future production equipment operation efficiency prediction results includes: Obtain the maximum production speed of the production line, and predict the work order production time based on the maximum production speed and the predicted results of the future production equipment operating efficiency. The calculation formula for the work order production time prediction is: , where Xz is the quantity of products required on the work order, ty is the production time of the work order, Xlt is the predicted value of the production equipment operating efficiency at time t in the future, and Vx is the maximum production speed of the largest production line. Solve the equation to get ,in, ,In this step, the production time of the work order is accurately predicted by taking into ,account the impact on the production line during operation and the ,production process into consideration, which improves the accuracy of the production time ,prediction.

[0009] Optionally, the production warning based on the work order production time prediction result includes: The obtained work order production time prediction result is compared with the production deadline data. If the work order production time prediction result is greater than or equal to the production deadline data, it means that the production can be met. If the work order production time prediction result is less than the production deadline data, it means that the production cannot be met, and a work order production warning is issued.

[0010] In a second aspect, the present application provides a work order data analysis system for wafer production, comprising: The data acquisition module is used to obtain the received work order data, the operation data of each production equipment and the corresponding production function data of the corresponding equipment; The equipment operation efficiency evaluation module obtains the operation data of each production equipment and the corresponding production function data of the corresponding equipment to evaluate the operation efficiency of the production equipment; The operation efficiency prediction module predicts the future operation efficiency of production equipment based on work order data and production equipment operation efficiency evaluation results; The work order production time prediction module predicts the work order production time based on the work order data and the future production equipment operation efficiency prediction results; The production early warning module provides production early warning based on the work order production time prediction results.

[0011] In a third aspect, the present application provides an electronic device comprising: a processor and a memory, wherein the memory stores a computer program that can be called by the processor, and the processor executes a work order data analysis method for chip production by calling the computer program stored in the memory.

[0012] In a fourth aspect, the present application provides a computer-readable storage medium storing instructions, which, when executed on a computer, enable the computer to execute a work order data analysis method for wafer production.

[0013] Compared with the prior art, this application has the following advantages and beneficial effects: This application obtains the operating data of each production equipment and the corresponding production function data of the corresponding equipment to evaluate the operating efficiency of the production equipment, predicts the future operating efficiency of the production equipment based on the work order data and the evaluation results of the production equipment operating efficiency, predicts the work order production time based on the work order data and the future production equipment operating efficiency prediction results, and performs production early warning based on the work order production time prediction results. When making production time predictions, this application accurately analyzes and predicts the reduction in equipment production efficiency caused by the production process, and accurately predicts the work order production time by taking into account the impact on the production line during operation and the production process, thereby improving the accuracy of production time predictions. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] Other features, objects and advantages of the present application will become more apparent by reading the detailed description of non-limiting embodiments made with reference to the following drawings: Figure 1 This is a schematic diagram of the overall process of the work order data analysis method for wafer production provided by an embodiment of the present application; Figure 2 This is a flowchart of the steps for evaluating the operating efficiency of production equipment in a work order data analysis method for wafer production provided in an embodiment of the present application; Figure 3 It is a structural diagram of a work order data analysis system for wafer production provided in an embodiment of the present application. DETAILED DESCRIPTION

[0015] The technical solution of the present application is described in detail below through the accompanying drawings and specific embodiments. It should be understood that the embodiments of the present application and the specific features in the embodiments are detailed descriptions of the technical solution of the present application, rather than limitations on the technical solution of the present application. Unless there is a conflict, the embodiments of the present application and the technical features in the embodiments can be combined with each other.

[0016] See Figure 1 , Figure 1 This is a schematic diagram of the overall process of the work order data analysis method for wafer production provided by an embodiment of the present application, which specifically includes the following steps: S1: Obtain the received work order data, the operation data of each production equipment and the corresponding production function data of the corresponding equipment; In this embodiment, the work order data includes the quality data, quantity data, and production deadline data of the product required on the work order; the operation data of the production equipment includes the operation data of each device on the production line; the operation data includes production standard speed data, real-time production speed data, real-time production product quality data, and various information data of the equipment; wherein the information data includes type data reflecting the operation quality of the equipment, such as the operating voltage and current of the corresponding equipment; the corresponding production function data of the corresponding equipment includes various production function data of the corresponding equipment. In this embodiment, the corresponding data types are collected by corresponding data collection terminals; Exemplarily, the information data of the device is collected by a corresponding device data collection terminal; For example, the standard polishing speed of a wafer polishing device on a production line is 2 wafers / min, while the real-time production speed is 1.5 wafers / min. The quality of the product is the polishing effect of the polished surface. The information data includes data reflecting the operating quality of the polishing device, such as the voltage, current, and polishing force of the polishing device. The function of the polishing device is polishing. For example, the quality data of the real-time production products can be obtained by comparing the size of the production products with the standard products, or by scoring them according to pre-set production standards; S2: Obtain the operating data of each production equipment and the corresponding production function data of the corresponding equipment to evaluate the operating efficiency of the production equipment; In this embodiment, if Figure 2 As shown in the figure, the production equipment operation efficiency evaluation includes the following specific steps: S21. Obtain information data from the operating data of the production equipment during the production cycle, and perform production anomaly analysis based on the information data from the operating data of the production equipment during the production cycle. The production anomaly analysis calculation formula is: , where M is the data type of information data, aj is the influence coefficient of the data type of the j-th information data on the equipment operation, xjh is the specific value of the j-th information data in the end time period of the production cycle, xjm is the median value of the safety range of the j-th information data, and xjq is the specific value of the j-th information data in the starting time period of the production cycle; Exemplarily, the influence coefficient of the data type of the jth information data on the operation of the equipment is obtained by obtaining the number of times that various data anomalies are outside the safe range when the equipment fails and needs maintenance, and obtaining the influence coefficient of the data type of the information data on the operation of the equipment by the ratio of the number of times that various data anomalies are outside the safe range to the total number of times; for example, when the polishing equipment needs maintenance after operation, the number of times the voltage is outside the safe range is 7 times, while the number of times all data types are outside the safe range is 30 times, so the influence coefficient of the voltage is 0.2333; S22. Acquire real-time quality data of products produced by the production equipment during the production cycle, and perform product quality anomaly analysis based on the product quality data. The calculation formula for the product quality anomaly analysis is: , where exp() is the power of the natural constant e, Zq is the average quality data of the products produced by the production equipment in real time at the start of the production cycle, Zh is the average quality data of the products produced by the production equipment in real time at the end of the production cycle, and Zm is the qualified quality data of the products produced; in this step, the impact of the production process on production aging is analyzed through the product production conditions at the start and end of the production cycle; here, in order to avoid the calculated data being too small or negative, exp() is used to quantify the data; For example, for example, the average quality data of the products produced at the start time is 8, the average quality data of the products produced by the production equipment in real time at the end time is 7.8, and the qualified quality data of the products is 6. The calculated result of the abnormal quality analysis of the products produced during the production cycle is: 1.03389; 23. Obtain standard production speed data, real-time production speed data, product quality anomaly analysis results, and production anomaly analysis results from the production process to evaluate the operating efficiency of production equipment. The production equipment operating efficiency evaluation formula is: , where Xlm is the standard efficiency of the production equipment, T is the production cycle length, and dt is the time integral constant. is the weight of production abnormality proportion, is the weight of the product abnormality ratio, vt is the production speed at time t, and vm is the standard production speed data. In this step, the degree of influence of equipment operation on equipment aging is quantitatively analyzed; For example, in this embodiment, the weight of production anomaly ratio is 0.65, and the weight of product anomaly ratio is 0.35; For example, in a specific embodiment, the calculated Sp is 1.03389, Sc is 0.345, the production abnormality ratio weight is 0.065, the product abnormality ratio weight is 0.035, vt is 1.5 pieces / min, vm is 2 pieces / min, and the duration is 30 minutes. Then, the calculated production equipment operating efficiency is 0.996: S3: Predict future production equipment operating efficiency based on work order data and production equipment operating efficiency evaluation results; In this embodiment, the future production equipment operating efficiency prediction includes the following specific steps: S31. Obtaining quality, quantity, and production deadline data of the product required on the work order, and determining the production speed required for producing the product based on the quantity and production deadline data; The benefits are: here we need to analyze the production speed required for the products on the production order, so as to analyze the impact of long-term high-speed operation on equipment and products, and thus analyze the efficiency of product production; S32. Based on the production speed required for the production of the product and the evaluation results of the production equipment operating efficiency, the future production equipment operating efficiency prediction formula is: , where Vs is the production speed required to produce the product, Tm is the future production time, Zs is the quality data of the product required on the work order, and Zm is the qualified quality data of the produced product; Exemplarily, the standard efficiency of the production equipment is preferably 1; S4: Predict work order production time based on work order data and future production equipment operating efficiency prediction results; In this embodiment, the work order production time prediction is performed based on the work order data and the future production equipment operation efficiency prediction results, including: Obtain the maximum production speed of the production line, and predict the work order production time based on the maximum production speed and the predicted results of the future production equipment operating efficiency. The calculation formula for the work order production time prediction is: , where Xz is the quantity of products required on the work order, ty is the production time of the work order, Xlt is the predicted value of the production equipment operating efficiency at time t in the future, and Vx is the maximum production speed of the largest production line. Solve the equation to get ,in, ,In this step, the production time of the work order is accurately predicted by taking into account the ,impact on the production line during operation and the production process, thus ,improving the accuracy of the production time prediction; S5: Provide production warning based on the work order production time prediction results; In this embodiment, production warning is performed based on the work order production time prediction result, including: Compare the obtained work order production time prediction result with the production deadline data. If the work order production time prediction result is greater than or equal to the production deadline data, it means that the production can be met. If the work order production time prediction result is less than the production deadline data, it means that the production cannot be met, and a work order production warning is issued; The above method can achieve the following: obtaining the operating data of each production equipment and the corresponding production function data of the corresponding equipment to evaluate the operating efficiency of the production equipment, predicting the future operating efficiency of the production equipment based on the work order data and the evaluation results of the operating efficiency of the production equipment, predicting the work order production time based on the work order data and the future production equipment operating efficiency prediction results, and performing production early warning based on the work order production time prediction results. When making production time predictions, this application accurately analyzes and predicts the reduction in equipment production efficiency caused by the production process, accurately predicts the work order production time by taking into account the impact on the production line during operation and the production process, thereby improving the accuracy of production time predictions.

[0017] See Figure 3 , Figure 3 : is a structural diagram of a work order data analysis system for wafer production provided in an embodiment of the present application. This embodiment provides a work order data analysis system for wafer production, including: The data acquisition module is used to obtain the received work order data, the operation data of each production equipment and the corresponding production function data of the corresponding equipment; The equipment operation efficiency evaluation module obtains the operation data of each production equipment and the corresponding production function data of the corresponding equipment to evaluate the operation efficiency of the production equipment; The operation efficiency prediction module predicts the future operation efficiency of production equipment based on work order data and production equipment operation efficiency evaluation results; The work order production time prediction module predicts the work order production time based on the work order data and the future production equipment operation efficiency prediction results; The production early warning module provides production early warning based on the work order production time prediction results.

[0018] The above-mentioned parameters and steps for each unit module to implement corresponding functions in the work order data analysis system for chip production of this application can refer to the parameters and steps in the embodiment of the work order data analysis method for chip production above, and will not be repeated here.

[0019] An embodiment of the present invention further provides an electronic device comprising a memory, a processor, and a communication bus; the memory and the processor are connected via the communication bus. The memory stores data that can be loaded and executed by the processor, such as the work order data analysis method for wafer production provided in the above embodiment.

[0020] The memory can be used to store instructions, programs, codes, code sets, or instruction sets. The memory can include a program storage area and a data storage area. The program storage area can store instructions for implementing an operating system, instructions for at least one function, and instructions for implementing the work order data analysis method for wafer production provided in the above embodiment. The data storage area can store data related to the work order data analysis method for wafer production provided in the above embodiment.

[0021] The processor may include one or more processing cores. The processor executes the various functions and processes data of the present application by running or executing instructions, programs, code sets or instruction sets stored in the memory, calling the data stored in the memory. The processor can be at least one of an application-specific integrated circuit (ASIC), a digital signal processor (DSP), a digital signal processing device (DSPD), a programmable logic device (PLD), a field programmable gate array (FPGA), a central processing unit (CPU), a controller, a microcontroller and a microprocessor. It is understandable that for different devices, the electronic device used to implement the above-mentioned processor functions can also be other, and the embodiments of the present application are not specifically limited.

[0022] A communication bus may include a pathway for transmitting information between the aforementioned components. Examples of communication buses include the PCI (Peripheral Component Interconnect) bus and the EISA (Extended Industry Standard Architecture) bus. Communication buses can be categorized as address buses, data buses, and control buses.

[0023] An embodiment of the present application provides a computer-readable storage medium storing a computer program that can be loaded by a processor and execute a work order data analysis method for wafer production as provided in the above embodiment.

[0024] In embodiments of the present application, a computer-readable storage medium may be a tangible device that holds and stores instructions used by an instruction execution device. The computer-readable storage medium may be, but is not limited to, an electrical storage device, a magnetic storage device, an optical storage device, an electromagnetic storage device, a semiconductor storage device, or any combination thereof. Specifically, the computer-readable storage medium may be a portable computer disk, a hard disk, a USB flash drive, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), a lectern random access memory (SRAM), a portable compact disc read-only memory (CD-ROM), a digital versatile disc (DVD), a memory stick, a floppy disk, an optical disc, a magnetic disk, a mechanical encoding device, or any combination thereof.

[0025] The terms "comprises," "comprising," 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 inherent to such process, method, article, or apparatus.

[0026] The above description is merely a preferred embodiment of the present application and an illustration of the technical principles employed. Those skilled in the art should understand that the scope of application of this application is not limited to technical solutions formed by a specific combination of the aforementioned technical features. It also encompasses other technical solutions formed by any combination of the aforementioned technical features or their equivalents, without departing from the aforementioned application concept. For example, a technical solution formed by replacing the aforementioned features with (but not limited to) technical features with similar functions claimed in this application.

Claims

1. A method for analyzing work order data for wafer production, characterized in that: The steps include: S1: Obtain the received work order data, the operation data of each production equipment and the corresponding production function data of the corresponding equipment; S2: Obtain the operating data of each production equipment and the corresponding production function data of the corresponding equipment to evaluate the operating efficiency of the production equipment; S3: Predict future production equipment operating efficiency based on work order data and production equipment operating efficiency evaluation results; S4: Predict work order production time based on work order data and future production equipment operating efficiency prediction results; S5: Provide production warning based on the work order production time prediction results.

2. The work order data analysis method for wafer production according to claim 1, characterized in that: The production equipment operation efficiency evaluation includes the following specific steps: Acquire information data from the operating data of the production equipment during the production cycle, and perform production anomaly analysis based on the information data from the operating data of the production equipment during the production cycle; Obtain real-time quality data of products produced by production equipment during the production cycle, and perform quality anomaly analysis of products based on the quality data of the products produced; Obtain production standard speed data, real-time production speed data, product quality anomaly analysis results, and production anomaly analysis results of the production process to evaluate the production equipment operation efficiency. The production equipment operation efficiency evaluation formula is: , where Xlm is the standard efficiency of the production equipment, T is the production cycle length, and dt is the time integral constant. is the weight of production abnormality proportion, is the weight of product abnormality proportion, vt is the production speed at time t, vm is the production standard speed data, among which Sc is the production abnormality analysis result, and Sp is the production product quality abnormality analysis result.

3. The work order data analysis method for wafer production according to claim 1, characterized in that: The prediction of future production equipment operating efficiency includes the following specific steps: Obtain the quality, quantity, and production deadline data of the product required on the work order, and determine the production speed required to produce the product based on the quantity and production deadline data; Based on the production speed required to produce the product and the evaluation results of the production equipment operating efficiency, the production equipment operating efficiency in the future is predicted.

4. The work order data analysis method for wafer production according to claim 1, characterized in that: The work order production time prediction based on the work order data and the future production equipment operation efficiency prediction results includes: Obtain the maximum production speed of the production line, and predict the work order production time based on the maximum production speed and the predicted results of the future production equipment operating efficiency. The calculation formula for the work order production time prediction is: , where Xz is the quantity data of the products required on the work order, ty is the production time of the work order, Xlt is the predicted value of the production equipment operating efficiency at time t in the future, and Vx is the maximum production speed of the largest production line.

5. The work order data analysis method for wafer production according to claim 1, characterized in that: The production early warning based on the work order production time prediction result includes: The obtained work order production time prediction result is compared with the production deadline data. If the work order production time prediction result is greater than or equal to the production deadline data, it means that the production can be met. If the work order production time prediction result is less than the production deadline data, it means that the production cannot be met, and a work order production warning is issued.

6. The work order data analysis method for wafer production according to claim 1, characterized in that: The work order data includes the quality data, quantity data and production deadline data of the products required on the work order, and the operation data of the production equipment includes the operation data of each equipment on the production line. The operation data includes production standard speed data, real-time production speed data, real-time production product quality data and various information data of the equipment.

7. The work order data analysis method for wafer production according to claim 3, characterized in that: The formula for predicting the operating efficiency of the production equipment at the future time Tm is: , where Vs is the production speed required to produce the product, Tm is the future production time, Zs is the quality data of the product required on the work order, and Zm is the qualified quality data of the produced product.

8. A work order data analysis system for wafer production, implemented based on the work order data analysis method for wafer production according to any one of claims 1 to 7, characterized in that: The system comprises: The data acquisition module is used to obtain the received work order data, the operation data of each production equipment and the corresponding production function data of the corresponding equipment; The equipment operation efficiency evaluation module obtains the operation data of each production equipment and the corresponding production function data of the corresponding equipment to evaluate the operation efficiency of the production equipment; The operation efficiency prediction module predicts the future operation efficiency of production equipment based on work order data and production equipment operation efficiency evaluation results; The work order production time prediction module predicts the work order production time based on the work order data and the future production equipment operation efficiency prediction results; The production early warning module provides production early warning based on the work order production time prediction results.

9. An electronic device comprising: A processor and a memory, wherein the memory stores a computer program that can be called by the processor; characterized in that the processor executes the work order data analysis method for wafer production as described in any one of claims 1 to 7 by calling the computer program stored in the memory.

10. A computer-readable storage medium, characterized in that Instructions are stored, and when the instructions are run on a computer, the computer is caused to execute the work order data analysis method for wafer production according to any one of claims 1 to 7.

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