Process machine evaluation method and device, electronic equipment and storage medium

Abstract

The invention provides a process machine evaluation method and device, electronic equipment and a storage medium, and the method comprises the steps: carrying out the analysis of a received evaluation instruction, and determining a current target evaluation strategy; wherein the target evaluation strategy comprises a parameter acquisition strategy and an identity label of the to-be-evaluated process machine; and according to the parameter acquisition strategy, performing data extraction from a database by using the identity label of the to-be-evaluated process machine to obtain target original data for determining the state of the to-be-evaluated process machine, and according to the target evaluation strategy, performing analysis processing on the target original data to obtain the state of the to-be-evaluated process machine. Obtaining a state evaluation result of the to-be-evaluated process machine; wherein the target original data comprises product node data and machine data. Therefore, by means of the scheme, the specific operation state of each machine can be known timely and accurately.

Description

Technical Field

[0001] The present application relates to the field of semiconductor technology, and more particularly to an evaluation method, device, electronic device and storage medium for a process machine. Background Art

[0002] In the semiconductor manufacturing process, the process machine is an important device for completing wafer processing, testing and handling. Its performance and status directly affect production efficiency and product quality. However, the operating status of the process machine is complex and changeable, and is easily affected by many factors, including equipment aging, environmental conditions, and fluctuations in process parameters. Therefore, in order to ensure the stability and efficiency of the production process, process machine control has become one of the core issues of concern to semiconductor manufacturers.

[0003] However, in most current statistical process control (SPC) systems, the status monitoring and control of process machines is mainly achieved through regular preventive maintenance (PM) or periodic monitoring. Although this traditional method can reduce the occurrence of machine failures to a certain extent, due to the fixed maintenance and monitoring cycles, it is often unable to capture the real-time changes in machine performance in a timely manner, which may cause some abnormal situations to be delayed or even ignored. In addition, with the increase in process complexity and the diversification of process parameters, the performance differences between different machines are gradually emerging. In this case, how to timely and accurately understand the specific operating status of each machine, so as to make targeted adjustments to process parameters, production plans and maintenance strategies, is a technical problem that needs to be solved urgently, but the traditional SPC system cannot support this demand. Summary of the invention

[0004] In view of this, the purpose of the present application is to provide a method, device, electronic equipment and storage medium for evaluating a process machine, which can timely and accurately understand the specific operating status of each machine.

[0005] The embodiment of the present application provides an evaluation method for a process tool, the evaluation method comprising:

[0006] Parsing the received evaluation instruction to determine the target evaluation strategy for this time; wherein the target evaluation strategy includes a parameter acquisition strategy and an identity of the process machine to be evaluated;

[0007] According to the parameter acquisition strategy, data is extracted from the database using the identity identifier of the process machine to be evaluated to obtain target original data for determining the status of the process machine to be evaluated, and the target original data is analyzed and processed according to the target evaluation strategy to obtain a status evaluation result of the process machine to be evaluated; wherein the target original data includes: product node data and machine data.

[0008] Optionally, when the target evaluation strategy is a node evaluation strategy, the status evaluation result of the process tool to be evaluated is determined by the following steps:

[0009] Identify the process node to which the process tool to be evaluated belongs;

[0010] According to the identity identifier of the process tool to be evaluated, extracting from the database a plurality of first product node data for producing semiconductor products using the process tool to be evaluated at the process node and first machine data of the process tool to be evaluated at the process node;

[0011] An evaluation analysis is performed based on the extracted data of the plurality of first product nodes, the first machine data of the process machine to be evaluated, and the standard data of the process node to determine a status evaluation result of the process machine to be evaluated.

[0012] Optionally, extracting from the database a plurality of first product node data for producing semiconductor products at the process node using the process tool to be evaluated includes:

[0013] Extracting from the database a plurality of original product parameters for producing semiconductor products using the process tool to be evaluated at the process node;

[0014] Parameters meeting at least one data screening requirement are screened out from the multiple original product parameters to obtain multiple first product node data.

[0015] Optionally, when the target evaluation strategy is an equipment evaluation strategy, the status evaluation result of the process tool to be evaluated is determined by the following steps:

[0016] Identify the process node to which the process tool to be evaluated belongs, and determine at least one candidate process tool of the same type and belonging to the same process node as the process tool to be evaluated;

[0017] According to the identity of the process machine to be evaluated, extract from the database a plurality of first product node data for producing semiconductor products using the process machine to be evaluated at the process node and first machine data of the process machine to be evaluated at the process node, and at the same time, according to the identity of each candidate process machine, extract from the database a plurality of second product node data for producing semiconductor products using each candidate process machine at the process node and second machine data of each candidate process machine at the process node;

[0018] An evaluation analysis is performed based on the plurality of first product node data, the plurality of second product node data, the first machine data of the process machine to be evaluated, and the second machine data of each candidate process machine to determine a status evaluation result of the process machine to be evaluated.

[0019] Optionally, before performing evaluation and analysis based on the plurality of first product node data, the plurality of second product node data, the first machine data of the process machine to be evaluated, the second machine data of each candidate process machine, and the standard data of the process node, the evaluation method further includes:

[0020] At least one filtering method is used to perform abnormal data filtering processing on the plurality of first product node data and the plurality of second product node data.

[0021] Optionally, the step of performing evaluation and analysis based on the extracted data of the plurality of first product nodes, the first machine data of the process machine to be evaluated, and the standard data of the process node to determine the state evaluation result of the process machine to be evaluated includes:

[0022] For each first product node data, a performance indicator calculation is performed according to the first product node data and the standard data to determine a plurality of first performance indicator values;

[0023] Determining a first process performance evaluation result according to the plurality of first performance indicator values;

[0024] Calculating equipment indicators according to the first equipment data of the process equipment to be evaluated, and determining a first equipment performance evaluation result according to the calculated equipment indicator value;

[0025] A comprehensive analysis is performed on the first process performance evaluation result and the first equipment performance evaluation result to determine a status evaluation result of the process tool to be evaluated.

[0026] Optionally, performing evaluation and analysis based on the plurality of first product node data, the plurality of second product node data, the first machine data of the process machine to be evaluated, and the second machine data of each candidate process machine to determine the status evaluation result of the process machine to be evaluated includes:

[0027] Determining standard verification data according to the plurality of first product node data and the plurality of second product node data;

[0028] For each first product node data, a performance indicator calculation is performed according to the first product node data and the standard verification data to determine a plurality of second performance indicator values;

[0029] Determining a second process performance evaluation result according to the plurality of second performance indicator values;

[0030] Calculating equipment indicators according to the first equipment data of the process equipment to be evaluated and the second equipment data of each candidate process equipment, and determining a second equipment performance evaluation result according to the calculated equipment indicator value;

[0031] A comprehensive analysis is performed on the second process performance evaluation result and the second equipment performance evaluation result to determine a status evaluation result of the process tool to be evaluated.

[0032] The present application also provides an evaluation device for a process machine, the evaluation device comprising:

[0033] A determination module, used to parse the received evaluation instruction and determine the target evaluation strategy for this time; wherein the target evaluation strategy includes a parameter acquisition strategy and an identity of the process machine to be evaluated;

[0034] An evaluation module is used to extract data from a database using the identity identifier of the process machine to be evaluated according to the parameter acquisition strategy, obtain target original data for determining the state of the process machine to be evaluated, and analyze and process the target original data according to the target evaluation strategy to obtain a state evaluation result of the process machine to be evaluated; wherein the target original data includes: product node data and machine data.

[0035] Optionally, the evaluation module is further used for:

[0036] When the target evaluation strategy is a node evaluation strategy, identifying the process node to which the process tool to be evaluated belongs;

[0037] According to the identity identifier of the process tool to be evaluated, extracting from the database a plurality of first product node data for producing semiconductor products using the process tool to be evaluated at the process node and first machine data of the process tool to be evaluated at the process node;

[0038] An evaluation analysis is performed based on the extracted data of the plurality of first product nodes, the first machine data of the process machine to be evaluated, and the standard data of the process node to determine a status evaluation result of the process machine to be evaluated.

[0039] Optionally, when the evaluation module is used to extract a plurality of first product node data for producing a semiconductor product using the process tool to be evaluated under the process node from the database, the evaluation module is used to:

[0040] Extracting from the database a plurality of original product parameters for producing semiconductor products using the process tool to be evaluated at the process node;

[0041] Parameters meeting at least one data screening requirement are screened out from the multiple original product parameters to obtain multiple first product node data.

[0042] Optionally, the evaluation module is further used for:

[0043] When the target evaluation strategy is an equipment evaluation strategy, identifying the process node to which the process tool to be evaluated belongs, and determining at least one candidate process tool of the same type as the process tool to be evaluated and belonging to the same process node;

[0044] According to the identity of the process machine to be evaluated, extract from the database a plurality of first product node data for producing semiconductor products using the process machine to be evaluated at the process node and first machine data of the process machine to be evaluated at the process node, and at the same time, according to the identity of each candidate process machine, extract from the database a plurality of second product node data for producing semiconductor products using each candidate process machine at the process node and second machine data of each candidate process machine at the process node;

[0045] An evaluation analysis is performed based on the plurality of first product node data, the plurality of second product node data, the first machine data of the process machine to be evaluated, and the second machine data of each candidate process machine to determine a status evaluation result of the process machine to be evaluated.

[0046] Optionally, the evaluation module is further used for:

[0047] At least one filtering method is used to perform abnormal data filtering processing on the plurality of first product node data and the plurality of second product node data.

[0048] Optionally, when the evaluation module is used to perform evaluation and analysis based on the extracted plurality of first product node data, the first machine data of the process machine to be evaluated, and the standard data of the process node to determine the status evaluation result of the process machine to be evaluated, the evaluation module is used to:

[0049] For each first product node data, a performance indicator calculation is performed according to the first product node data and the standard data to determine a plurality of first performance indicator values;

[0050] Determining a first process performance evaluation result according to the plurality of first performance indicator values;

[0051] Calculating equipment indicators according to the first equipment data of the process equipment to be evaluated, and determining a first equipment performance evaluation result according to the calculated equipment indicator value;

[0052] A comprehensive analysis is performed on the first process performance evaluation result and the first equipment performance evaluation result to determine a status evaluation result of the process tool to be evaluated.

[0053] Optionally, when the evaluation module is used to perform evaluation and analysis based on the plurality of first product node data, the plurality of second product node data, the first machine data of the process machine to be evaluated, and the second machine data of each candidate process machine to determine the status evaluation result of the process machine to be evaluated, the evaluation module is used to:

[0054] Determining standard verification data according to the plurality of first product node data and the plurality of second product node data;

[0055] For each first product node data, a performance indicator calculation is performed according to the first product node data and the standard verification data to determine a plurality of second performance indicator values;

[0056] Determining a second process performance evaluation result according to the plurality of second performance indicator values;

[0057] Calculating equipment indicators according to the first equipment data of the process equipment to be evaluated and the second equipment data of each candidate process equipment, and determining a second equipment performance evaluation result according to the calculated equipment indicator value;

[0058] A comprehensive analysis is performed on the second process performance evaluation result and the second equipment performance evaluation result to determine a status evaluation result of the process tool to be evaluated.

[0059] An embodiment of the present application also provides an electronic device, comprising: a processor, a memory and a bus, wherein the memory stores machine-readable instructions executable by the processor, and when the electronic device is running, the processor and the memory communicate via the bus, and when the machine-readable instructions are executed by the processor, the steps of the above-mentioned evaluation method are performed.

[0060] An embodiment of the present application further provides a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, the steps of the above-mentioned evaluation method are executed.

[0061] An embodiment of the present application provides an evaluation method, device, electronic device and storage medium for a process machine. The method includes: parsing a received evaluation instruction to determine a target evaluation strategy for this time; wherein the target evaluation strategy includes a parameter acquisition strategy and an identity identifier of the process machine to be evaluated; according to the parameter acquisition strategy, using the identity identifier of the process machine to be evaluated to extract data from a database to obtain target original data for determining the state of the process machine to be evaluated, and analyzing and processing the target original data according to the target evaluation strategy to obtain a state evaluation result of the process machine to be evaluated; wherein the target original data includes: product node data and machine data.

[0062] In this way, this application significantly improves the timeliness and accuracy of machine status determination through accurate data extraction, comprehensive data analysis, automated evaluation process and real-time result feedback. Therefore, this solution can not only help to quickly discover abnormalities and take measures, but also provide strong support for optimizing processes and equipment maintenance, ultimately improving production efficiency and product quality.

[0063] In order to make the above-mentioned objects, features and advantages of the present application more obvious and easy to understand, preferred embodiments are specifically cited below and described in detail with reference to the attached drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0064] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings required for use in the embodiments will be briefly introduced below. It should be understood that the following drawings only show certain embodiments of the present application and therefore should not be regarded as limiting the scope. For ordinary technicians in this field, other related drawings can be obtained based on these drawings without paying creative work.

[0065] Figure 1 A flowchart of a method for evaluating a process tool provided in an embodiment of the present application;

[0066] Figure 2 A schematic diagram of a process for determining a machine status evaluation result provided in the present application;

[0067] Figure 3 A schematic diagram of the structure of an evaluation device for a process machine provided in an embodiment of the present application;

[0068] Figure 4 A schematic diagram of the structure of an electronic device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0069] To make the purpose, technical scheme and advantages of the embodiments of the present application clearer, the technical scheme in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all of the embodiments. The components of the embodiments of the present application usually described and shown in the drawings here can be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of the present application provided in the drawings is not intended to limit the scope of the application claimed for protection, but merely represents the selected embodiments of the present application. Based on the embodiments of the present application, each other embodiment obtained by those skilled in the art without making creative work belongs to the scope of protection of the present application.

[0070] In the semiconductor manufacturing process, the process machine is an important device for completing wafer processing, testing and handling. Its performance and status directly affect production efficiency and product quality. However, the operating status of the process machine is complex and changeable, and is easily affected by many factors, including equipment aging, environmental conditions, and fluctuations in process parameters. Therefore, in order to ensure the stability and efficiency of the production process, process machine control has become one of the core issues of concern to semiconductor manufacturers.

[0071] However, in most current statistical process control (SPC) systems, the status monitoring and control of process machines is mainly achieved through regular preventive maintenance (PM) or periodic monitoring. Although this traditional method can reduce the occurrence of machine failures to a certain extent, due to the fixed maintenance and monitoring cycles, it is often unable to capture the real-time changes in machine performance in a timely manner, which may cause some abnormal situations to be delayed or even ignored. In addition, with the increase in process complexity and the diversification of process parameters, the performance differences between different machines are gradually emerging. In this case, how to timely and accurately understand the specific operating status of each machine, so as to make targeted adjustments to process parameters, production plans and maintenance strategies, is a technical problem that needs to be solved urgently, but the traditional SPC system cannot support this demand.

[0072] Based on this, the embodiments of the present application provide a method, device, electronic device and storage medium for evaluating a process machine, which can timely and accurately understand the specific operating status of each machine.

[0073] See also Figure 1 , Figure 1 This is a flow chart of a method for evaluating a process machine provided in an embodiment of the present application. This solution can be used in conjunction with an SPC system, or the solution can be embedded in an SPC system. Figure 1As shown in , the evaluation method provided in the embodiment of the present application includes:

[0074] S101, parsing the received evaluation instruction to determine the target evaluation strategy for this time.

[0075] S102. According to the parameter acquisition strategy, data is extracted from the database using the identity identifier of the process machine to be evaluated to obtain target original data for determining the state of the process machine to be evaluated, and the target original data is analyzed and processed according to the target evaluation strategy to obtain a state evaluation result of the process machine to be evaluated.

[0076] With respect to step S101 , the target evaluation strategy includes a parameter acquisition strategy and an identity of a process tool to be evaluated.

[0077] Regarding step S102, the target original data includes: product node data and machine data.

[0078] In this way, this solution can ensure the selection of the most suitable strategy for the evaluation needs of different process machines and realize multi-dimensional evaluation by parsing the received evaluation instructions and clarifying the target evaluation strategy. By acquiring the strategy based on parameters and the identity of the process machine to be evaluated, the relevant data in the database can be accurately located, thereby avoiding the extraction and processing of redundant data and improving the efficiency of data extraction. Based on a clear target evaluation strategy and comprehensive raw data analysis, the generated status evaluation results are more authoritative and credible, providing a reliable basis for subsequent equipment maintenance, optimization and management.

[0079] Continuing with step S101 , by way of example, the target evaluation strategy provided in the present application may include: a node evaluation strategy and a device evaluation strategy.

[0080] For step S102, see Figure 2 , Figure 2 A schematic diagram of a process for determining a machine status evaluation result provided in this application, such as Figure 2 As shown, in one embodiment provided in the present application, when the target evaluation strategy is a node evaluation strategy, the status evaluation result of the process tool to be evaluated is determined by the following steps:

[0081] S10211. Identify the process node to which the process tool to be evaluated belongs.

[0082] S10212. According to the identity identifier of the process tool to be evaluated, extract from the database a plurality of first product node data for producing semiconductor products using the process tool to be evaluated at the process node and the first machine data of the process tool to be evaluated at the process node.

[0083] S10213, performing evaluation and analysis based on the extracted data of the plurality of first product nodes, the first machine data of the process machine to be evaluated, and the standard data of the process node, to determine a status evaluation result of the process machine to be evaluated.

[0084] Regarding step S10211, this step may be to determine the specific process node corresponding to the process tool to be evaluated in the semiconductor production process according to the identity of the process tool to be evaluated.

[0085] For example, it may include: using the identity identifier of the process machine to be evaluated (such as the machine number or type) to query the machine registration information table in the database, and finding the process node name to which the machine belongs according to the fields in the machine registration information table (such as machine type, purpose).

[0086] Examples of process nodes include: lithography nodes, etching nodes, cleaning nodes, etc.

[0087] Regarding step S10212, in this step, according to the identity of the process machine to be evaluated, production data related to the process machine to be evaluated is extracted from the database. The related production data may specifically include: first product node data and first machine data of the process machine to be evaluated.

[0088] The product node data may be specific data (such as yield, defect type, product function parameters, etc.) representing multiple products produced using the machine.

[0089] The machine data may be performance data related to the operation of the machine at the process node (eg, operation time, temperature, pressure, etc.).

[0090] With respect to step S10212, in one embodiment provided in the present application, the extracting from the database a plurality of first product node data for producing a semiconductor product at the process node using the process machine to be evaluated includes: extracting from the database a plurality of original product parameters for producing a semiconductor product at the process node using the process machine to be evaluated; and filtering out parameters that meet at least one data filtering requirement from the plurality of original product parameters to obtain a plurality of first product node data.

[0091] For example, the data screening requirements may include: USL screening requirements (selecting data whose all parameter values ​​are less than or equal to the upper specification limit), TARGET screening requirements (selecting data whose all parameter values ​​are equal to or close to the target value (possibly with a certain deviation range)), and LSL screening requirements (selecting data whose all parameter values ​​are greater than or equal to the lower specification limit).

[0092] Among them, when determining the first product node data, one or more of the above-mentioned screening requirements can be adopted.

[0093] This solution supports a variety of flexible data screening methods. By selecting USL, TARGET, LSL and other conditions individually or in any combination, it helps users analyze and optimize process and equipment performance from different angles.

[0094] For step S10213, the standard data may include the following information: yield threshold, mean, baseline value, control range, etc.

[0095] Continuing with step S10213, in one embodiment provided in the present application, the step of performing evaluation and analysis based on the extracted plurality of first product node data, the first machine data of the process machine to be evaluated, and the standard data of the process node to determine the status evaluation result of the process machine to be evaluated includes:

[0096] S102131. For each first product node data, calculate the performance index according to the first product node data and the standard data to determine a plurality of first performance index values.

[0097] S102132. Determine a first process performance evaluation result based on the multiple first performance indicator values.

[0098] S102133. Calculate equipment indicators based on the first equipment data of the process equipment to be evaluated, and determine a first equipment performance evaluation result based on the calculated equipment indicator value.

[0099] S102134. Comprehensively analyze the first process performance evaluation result and the first equipment performance evaluation result to determine the status evaluation result of the process tool to be evaluated.

[0100] With respect to step S102131, in this step, key production parameters are obtained from the extracted multiple first product node data, and then the performance indicators of each product node are calculated according to the standard data of the process node, and the calculation results are saved as multiple first performance indicator values.

[0101] Regarding step S102132, this step may include: counting the distribution of all first performance indicator values, calculating the comprehensive performance indicator of the process node, and then generating a process performance evaluation result according to the evaluation rules.

[0102] For example, the process performance evaluation result may include: normal product, slightly abnormal product, and seriously abnormal product.

[0103] Regarding step S102133, this step may include: extracting machine data (such as operating time, number of abnormalities), then calculating equipment indicators, and finally classifying the machine status according to the calculated equipment indicator values ​​to obtain equipment performance evaluation results.

[0104] Regarding step S102134, the step is specifically: performing a weighted comprehensive analysis on the first process performance evaluation result and the first equipment performance evaluation result to determine the status evaluation result of the process tool to be evaluated.

[0105] Here, corresponding weight values ​​are set in advance for process performance and equipment performance.

[0106] Continuing with step S102, in another embodiment provided in the present application, when the target evaluation strategy is an equipment evaluation strategy, the status evaluation result of the process tool to be evaluated is determined by the following steps:

[0107] S10221. Identify the process node to which the process tool to be evaluated belongs, and determine at least one candidate process tool of the same type as the process tool to be evaluated and belonging to the same process node.

[0108] S10222. Based on the identity identifier of the process machine to be evaluated, extract from the database a plurality of first product node data for producing semiconductor products using the process machine to be evaluated at the process node, and the first machine data of the process machine to be evaluated at the process node. At the same time, based on the identity identifier of each alternative process machine, extract from the database a plurality of second product node data for producing semiconductor products using each alternative process machine at the process node, and the second machine data of each alternative process machine at the process node.

[0109] S10223. Perform evaluation and analysis based on the multiple first product node data, the multiple second product node data, the first machine data of the process machine to be evaluated, and the second machine data of each candidate process machine to determine a status evaluation result of the process machine to be evaluated.

[0110] Regarding step S10221, the step may specifically include: determining the process node corresponding to the machine to be evaluated from the database through the identification of the machine to be evaluated, and retrieving a list of machines of the same type and belonging to the same process node as the machine to be evaluated from the machine database to ensure that there is at least one candidate machine.

[0111] If there is no alternative machine available, it will prompt to adjust the evaluation strategy or supplement the data.

[0112] The alternative process tools are other tools of the same type (eg, the same model or specification) as the tool to be evaluated and that perform tasks at the same process node, serving as comparison reference objects.

[0113] Regarding step S10222, this step may specifically include: extracting product node data produced by the machine to be evaluated from the database, and recording the performance indicators of each batch of products or each product produced by it. At the same time, extracting product node data of each candidate machine at the same process node from the database.

[0114] In addition, the data can be stored in a structured manner to ensure that the same production conditions (such as the same batch and the same product specifications) of the machine to be evaluated and the alternative machines can be matched.

[0115] In an implementation provided in the present application, after determining the first product node data and the second product node data, the evaluation method further includes: using at least one filtering method to perform abnormal data filtering processing on the multiple first product node data and the multiple second product node data.

[0116] For example, the filtering process may include: filtering according to filtering conditions such as n*IQR / n*Sigma / PERCENTILE*.

[0117] With respect to step S10223, in one embodiment provided in the present application, performing evaluation and analysis based on the plurality of first product node data, the plurality of second product node data, the first machine data of the process machine to be evaluated, and the second machine data of each candidate process machine to determine the status evaluation result of the process machine to be evaluated includes:

[0118] S102231. Determine standard verification data based on the multiple first product node data and the multiple second product node data.

[0119] S102232. For each first product node data, calculate the performance indicator according to the first product node data and the standard verification data to determine a plurality of second performance indicator values.

[0120] S102233. Determine a second process performance evaluation result based on the multiple second performance indicator values.

[0121] S102234, calculating equipment indicators according to the first equipment data of the process equipment to be evaluated and the second equipment data of each candidate process equipment, and determining a second equipment performance evaluation result according to the calculated equipment indicator value.

[0122] S102235. Comprehensively analyze the second process performance evaluation result and the second equipment performance evaluation result to determine the status evaluation result of the process tool to be evaluated.

[0123] With respect to step S102231, the step may specifically include: aggregating the first product node data and the second product node data, and grouping them according to the same product batch and process conditions. Calculating the average or median of the key performance indicators (such as yield, defect rate, and production capacity) in each group of data, filtering outliers, and outputting a verification data set containing the standard value of each performance indicator.

[0124] Regarding step S102232, this step may specifically include: calculating the difference or ratio between each indicator in the first product node data and the standard verification data, calculating the standard deviation of the deviation values ​​of multiple product node data, and then outputting the calculation results for each indicator to determine multiple second performance indicator values.

[0125] Regarding step S102233, this step may specifically include: calculating the overall process performance score by weighting multiple second performance indicator values, comparing the comprehensive score with a preset threshold, evaluating the process performance status, and finally obtaining a second process performance evaluation result.

[0126] Regarding step S102234, this step may specifically include: comparing the first machine data and the second machine data, calculating indicators such as operating efficiency and failure rate deviation, comprehensively deriving an equipment performance score based on the equipment indicator values, and determining an equipment performance evaluation result based on the score.

[0127] Regarding step S102235, this step may specifically include: performing a weighted comprehensive analysis on the second process performance evaluation result and the second equipment performance evaluation result to determine the status evaluation result of the process tool to be evaluated.

[0128] The status evaluation result of the process tool to be evaluated obtained in step S102 may be displayed in a graphical form (a dashboard or a heat map) for intuitive analysis.

[0129] In this way, this application significantly improves the timeliness and accuracy of machine status determination through accurate data extraction, comprehensive data analysis, automated evaluation process and real-time result feedback. Therefore, this solution can not only help to quickly discover abnormalities and take measures, but also provide strong support for optimizing processes and equipment maintenance, ultimately improving production efficiency and product quality.

[0130] Based on the same inventive concept, an evaluation device corresponding to the evaluation method is also provided in the embodiment of the present application. Since the principle of solving the problem by the device in the embodiment of the present application is similar to the above-mentioned evaluation method in the embodiment of the present application, the implementation of the device can refer to the implementation of the method, and the repeated parts will not be repeated.

[0131] See also Figure 3 , Figure 3 This is a schematic diagram of the structure of an evaluation device for a process machine provided in an embodiment of the present application. Figure 3 As shown in , the evaluation device 300 includes:

[0132] The determination module 310 is used to parse the received evaluation instruction and determine the target evaluation strategy for this time; wherein the target evaluation strategy includes a parameter acquisition strategy and an identity of the process machine to be evaluated;

[0133] The evaluation module 320 is used to extract data from the database according to the parameter acquisition strategy using the identity identifier of the process machine to be evaluated, obtain target original data for determining the state of the process machine to be evaluated, and analyze and process the target original data according to the target evaluation strategy to obtain the state evaluation result of the process machine to be evaluated; wherein the target original data includes: product node data and machine data.

[0134] Optionally, the evaluation module 320 is further configured to:

[0135] When the target evaluation strategy is a node evaluation strategy, identifying the process node to which the process tool to be evaluated belongs;

[0136] According to the identity identifier of the process tool to be evaluated, extracting from the database a plurality of first product node data for producing semiconductor products using the process tool to be evaluated at the process node and first machine data of the process tool to be evaluated at the process node;

[0137] An evaluation analysis is performed based on the extracted data of the plurality of first product nodes, the first machine data of the process machine to be evaluated, and the standard data of the process node to determine a status evaluation result of the process machine to be evaluated.

[0138] Optionally, when the evaluation module 320 is used to extract a plurality of first product node data for producing a semiconductor product using the process tool to be evaluated under the process node from the database, the evaluation module 320 is used to:

[0139] Extracting from the database a plurality of original product parameters for producing semiconductor products using the process tool to be evaluated at the process node;

[0140] Parameters meeting at least one data screening requirement are screened out from the multiple original product parameters to obtain multiple first product node data.

[0141] Optionally, the evaluation module 320 is further configured to:

[0142] When the target evaluation strategy is an equipment evaluation strategy, identifying the process node to which the process tool to be evaluated belongs, and determining at least one candidate process tool of the same type as the process tool to be evaluated and belonging to the same process node;

[0143] According to the identity of the process machine to be evaluated, extract from the database a plurality of first product node data for producing semiconductor products using the process machine to be evaluated at the process node and first machine data of the process machine to be evaluated at the process node, and at the same time, according to the identity of each candidate process machine, extract from the database a plurality of second product node data for producing semiconductor products using each candidate process machine at the process node and second machine data of each candidate process machine at the process node;

[0144] An evaluation analysis is performed based on the plurality of first product node data, the plurality of second product node data, the first machine data of the process machine to be evaluated, and the second machine data of each candidate process machine to determine a status evaluation result of the process machine to be evaluated.

[0145] Optionally, the evaluation module 320 is further configured to:

[0146] At least one filtering method is used to perform abnormal data filtering processing on the plurality of first product node data and the plurality of second product node data.

[0147] Optionally, when the evaluation module 320 is used to perform evaluation and analysis based on the extracted plurality of first product node data, the first machine data of the process machine to be evaluated, and the standard data of the process node to determine the status evaluation result of the process machine to be evaluated, the evaluation module 320 is used to:

[0148] For each first product node data, a performance indicator calculation is performed according to the first product node data and the standard data to determine a plurality of first performance indicator values;

[0149] Determining a first process performance evaluation result according to the plurality of first performance indicator values;

[0150] Calculating equipment indicators according to the first equipment data of the process equipment to be evaluated, and determining a first equipment performance evaluation result according to the calculated equipment indicator value;

[0151] A comprehensive analysis is performed on the first process performance evaluation result and the first equipment performance evaluation result to determine a status evaluation result of the process tool to be evaluated.

[0152] Optionally, when the evaluation module 320 is used to perform evaluation and analysis based on the plurality of first product node data, the plurality of second product node data, the first machine data of the process machine to be evaluated, and the second machine data of each candidate process machine to determine the status evaluation result of the process machine to be evaluated, the evaluation module 320 is used to:

[0153] Determining standard verification data according to the plurality of first product node data and the plurality of second product node data;

[0154] For each first product node data, a performance indicator calculation is performed according to the first product node data and the standard verification data to determine a plurality of second performance indicator values;

[0155] Determining a second process performance evaluation result according to the plurality of second performance indicator values;

[0156] Calculating equipment indicators according to the first equipment data of the process equipment to be evaluated and the second equipment data of each candidate process equipment, and determining a second equipment performance evaluation result according to the calculated equipment indicator value;

[0157] A comprehensive analysis is performed on the second process performance evaluation result and the second equipment performance evaluation result to determine a status evaluation result of the process tool to be evaluated.

[0158] See also Figure 4 , Figure 4 This is a schematic diagram of the structure of an electronic device provided in an embodiment of the present application. Figure 4 As shown in , the electronic device 400 includes a processor 410 , a memory 420 and a bus 430 .

[0159] The memory 420 stores machine-readable instructions executable by the processor 410. When the electronic device 400 is running, the processor 410 communicates with the memory 420 via the bus 430. When the machine-readable instructions are executed by the processor 410, the above-mentioned Figure 1 as well as Figure 2 The specific implementation of the steps in the method embodiment shown can be found in the method embodiment, and will not be repeated here.

[0160] The present application also provides a computer-readable storage medium on which a computer program is stored. When the computer program is executed by a processor, the computer program can execute the above-mentioned Figure 1 as well as Figure 2 The specific implementation of the steps in the method embodiment shown can be found in the method embodiment, and will not be repeated here.

[0161] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0162] In the several embodiments provided in the present application, it should be understood that the disclosed systems, devices and methods can be implemented in other ways. The device embodiments described above are merely schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some communication interfaces, and the indirect coupling or communication connection of devices or units can be electrical, mechanical or other forms.

[0163] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0164] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit.

[0165] If the functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a non-volatile computer-readable storage medium that is executable by a processor. Based on this understanding, the technical solution of the present application can essentially be embodied in the form of a software product, or in other words, the part that contributes to the prior art or the part of the technical solution. The computer software product is stored in a storage medium, including several instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present application. The aforementioned storage medium includes: various media that can store program codes, such as a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk.

[0166] Finally, it should be noted that the above-described embodiments are only specific implementation methods of the present application, which are used to illustrate the technical solutions of the present application, rather than to limit them. The protection scope of the present application is not limited thereto. Although the present application is described in detail with reference to the above-mentioned embodiments, ordinary technicians in the field should understand that any technician familiar with the technical field can still modify the technical solutions recorded in the above-mentioned embodiments within the technical scope disclosed in the present application, or can easily think of changes, or make equivalent replacements for some of the technical features therein; and these modifications, changes or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present application, and should be included in the protection scope of the present application. Therefore, the protection scope of the present application shall be based on the protection scope of the claims.

Claims

1. A method for evaluating a process tool, characterized in that: The evaluation methods include: Parsing the received evaluation instruction to determine the target evaluation strategy for this time; wherein the target evaluation strategy includes a parameter acquisition strategy and an identity of the process machine to be evaluated; According to the parameter acquisition strategy, data is extracted from the database using the identity identifier of the process machine to be evaluated to obtain target original data for determining the status of the process machine to be evaluated, and the target original data is analyzed and processed according to the target evaluation strategy to obtain a status evaluation result of the process machine to be evaluated; wherein the target original data includes: product node data and machine data.

2. The evaluation method according to claim 1, characterized in that: When the target evaluation strategy is a node evaluation strategy, the status evaluation result of the process tool to be evaluated is determined by the following steps: Identify the process node to which the process tool to be evaluated belongs; According to the identity identifier of the process tool to be evaluated, extracting from the database a plurality of first product node data for producing semiconductor products using the process tool to be evaluated at the process node and first machine data of the process tool to be evaluated at the process node; An evaluation analysis is performed based on the extracted data of the plurality of first product nodes, the first machine data of the process machine to be evaluated, and the standard data of the process node to determine a status evaluation result of the process machine to be evaluated.

3. The evaluation method according to claim 2, characterized in that: The step of extracting from the database a plurality of first product node data for producing semiconductor products using the process tool to be evaluated at the process node comprises: Extracting from the database a plurality of original product parameters for producing semiconductor products using the process tool to be evaluated at the process node; Parameters meeting at least one data screening requirement are screened out from the multiple original product parameters to obtain multiple first product node data.

4. The evaluation method according to claim 1, characterized in that: When the target evaluation strategy is an equipment evaluation strategy, the status evaluation result of the process tool to be evaluated is determined by the following steps: Identify the process node to which the process tool to be evaluated belongs, and determine at least one candidate process tool of the same type and belonging to the same process node as the process tool to be evaluated; According to the identity of the process machine to be evaluated, extract from the database a plurality of first product node data for producing semiconductor products using the process machine to be evaluated at the process node and first machine data of the process machine to be evaluated at the process node, and at the same time, according to the identity of each candidate process machine, extract from the database a plurality of second product node data for producing semiconductor products using each candidate process machine at the process node and second machine data of each candidate process machine at the process node; An evaluation analysis is performed based on the plurality of first product node data, the plurality of second product node data, the first machine data of the process machine to be evaluated, and the second machine data of each candidate process machine to determine a status evaluation result of the process machine to be evaluated.

5. The evaluation method according to claim 4, characterized in that: Before performing evaluation and analysis based on the plurality of first product node data, the plurality of second product node data, the first machine data of the process machine to be evaluated, the second machine data of each candidate process machine, and the standard data of the process node, the evaluation method further includes: At least one filtering method is used to perform abnormal data filtering processing on the plurality of first product node data and the plurality of second product node data.

6. The evaluation method according to claim 2, characterized in that: The step of performing evaluation and analysis based on the extracted data of the plurality of first product nodes, the first machine data of the process machine to be evaluated, and the standard data of the process node to determine the state evaluation result of the process machine to be evaluated includes: For each first product node data, a performance indicator calculation is performed according to the first product node data and the standard data to determine a plurality of first performance indicator values; Determining a first process performance evaluation result according to the plurality of first performance indicator values; Calculating equipment indicators according to the first equipment data of the process equipment to be evaluated, and determining a first equipment performance evaluation result according to the calculated equipment indicator value; A comprehensive analysis is performed on the first process performance evaluation result and the first equipment performance evaluation result to determine a status evaluation result of the process tool to be evaluated.

7. The evaluation method according to claim 4, characterized in that: The step of performing evaluation and analysis based on the plurality of first product node data, the plurality of second product node data, the first machine data of the process machine to be evaluated, and the second machine data of each candidate process machine to determine the status evaluation result of the process machine to be evaluated includes: Determining standard verification data according to the plurality of first product node data and the plurality of second product node data; For each first product node data, a performance indicator calculation is performed according to the first product node data and the standard verification data to determine a plurality of second performance indicator values; Determining a second process performance evaluation result according to the plurality of second performance indicator values; Calculating equipment indicators according to the first equipment data of the process equipment to be evaluated and the second equipment data of each candidate process equipment, and determining a second equipment performance evaluation result according to the calculated equipment indicator value; A comprehensive analysis is performed on the second process performance evaluation result and the second equipment performance evaluation result to determine a status evaluation result of the process tool to be evaluated.

8. An evaluation device for a process machine, characterized in that: The evaluation device comprises: A determination module, used to parse the received evaluation instruction and determine the target evaluation strategy for this time; wherein the target evaluation strategy includes a parameter acquisition strategy and an identity of the process machine to be evaluated; An evaluation module is used to extract data from a database using the identity identifier of the process machine to be evaluated according to the parameter acquisition strategy, obtain target original data for determining the state of the process machine to be evaluated, and analyze and process the target original data according to the target evaluation strategy to obtain a state evaluation result of the process machine to be evaluated; wherein the target original data includes: product node data and machine data.

9. An electronic device, characterized in that: include: A processor, a memory and a bus, wherein the memory stores machine-readable instructions executable by the processor, and when the electronic device is running, the processor and the memory communicate via the bus, and the machine-readable instructions are executed by the processor to execute the steps of the evaluation method as described in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps of the evaluation method according to any one of claims 1 to 7 are executed.

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