Method for evaluating semiconductor device production capacity and apparatus therefor
By repeatedly filtering and processing semiconductor equipment process data, the boundary value of Takt Time is obtained, which solves the problem of accuracy in semiconductor equipment production capacity assessment and improves the accuracy and efficiency of investment amount assessment for capacity planning.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- CHANGXIN MEMORY TECH INC
- Filing Date
- 2021-08-12
- Publication Date
- 2026-07-31
AI Technical Summary
Existing technologies lack accuracy in assessing semiconductor equipment production capacity, leading to deviations in capacity planning investment amounts. In particular, the uncertainty of Takt Time during unstable operation of semiconductor equipment results in inaccurate WPH assessments.
By performing at least four rounds of screening on all acquired process machine data, including initial screening of valid data, combination of similar process machine data, screening of full-chamber status data of process machines, data sorting and average amplitude processing, the boundary value of Takt Time is finally obtained to improve data accuracy.
It improves the accuracy of semiconductor equipment production capacity assessment, ensures more precise investment amounts in capacity planning, provides a solid basis for decision-making, and enhances assessment efficiency and accuracy.
Smart Images
Figure CN115705544B_ABST
Abstract
Description
Technical Field
[0001] This application relates to the field of semiconductor manufacturing, and in particular to a method and apparatus for evaluating the production capacity of semiconductor equipment. Background Technology
[0002] One of the tasks of capacity planning for semiconductor manufacturing plants is to plan the number of semiconductor equipment to be produced based on capacity expansion needs, and at the same time estimate the investment amount based on the planned number of semiconductor equipment. The number of semiconductor equipment depends primarily on the production capacity of the semiconductor equipment; furthermore, the production capacity of the semiconductor equipment also indirectly determines the investment amount required for capacity expansion.
[0003] Currently, in the semiconductor manufacturing field, when semiconductor equipment is operating at full load and stably, for a specific semiconductor equipment and a specific process step, the production capacity of the semiconductor equipment is conventionally measured by the number of wafers produced per unit time (WPH, wafers per hour). The mathematical definition of WPH is: WPH = Run Size / Takt Time, where Run Size represents the maximum number of wafers allowed to be produced per batch by a specific semiconductor equipment. When the semiconductor equipment is at full load, Run Size is a fixed value, and Takt Time represents the time interval between the completion of the current batch production and the completion of the previous batch production in the semiconductor equipment.
[0004] However, as semiconductor equipment operates, it is not always at full load and running stably, and the Take Time collected at different times varies, resulting in differences in WPH (Work Per Hour) at different times. All these factors can interfere with the assessment of semiconductor equipment production capacity. Therefore, there is an urgent need for a method to assess semiconductor equipment production capacity in order to improve the accuracy of the assessment. Summary of the Invention
[0005] This application provides a method and apparatus for evaluating the production capacity of semiconductor equipment, which at least helps to improve the accuracy of the evaluated semiconductor equipment production capacity.
[0006] According to some embodiments of this application, one aspect of this application provides a method for evaluating the production capacity of semiconductor equipment. The semiconductor equipment includes different types of process equipment. The evaluation method includes: processing N batches of wafers using all the process equipment and acquiring data from all the process equipment, the data being the time interval between the end of the current batch production and the end of the previous batch production; acquiring valid data from the data as valid data; acquiring at least two initial data sets, each initial data set containing valid data from each batch when the same type of process equipment produces the same product and performs the same process step; and based on the initial data sets, acquiring a first data set, the first data set containing data where all chambers in the process equipment are in a working state. The corresponding valid data; sorting the data in the first data group to form a first queue data group; obtaining a second queue data group, which is composed of the remaining data after removing several data at the beginning and end of the first queue data group, and obtaining the average amplitude corresponding to adjacent valid data in the second queue data group; based on the second queue data group and the average amplitude, obtaining a third queue data group, which is composed of the remaining data after removing the next data in adjacent data when the actual change amplitude of adjacent data in the second queue data group deviates from the average amplitude; based on the data in all the third queue data groups, obtaining the minimum valid data and the maximum valid data; based on the minimum valid data and the maximum valid data, obtaining the semiconductor equipment production capacity.
[0007] According to some embodiments of this application, another aspect of this application provides an apparatus for evaluating the production capacity of semiconductor equipment, comprising: a data collection module for acquiring data from all process equipment, the data being the time interval between the end of the current batch production and the end of the previous batch production at the process equipment; and a data processing module for processing the data, the data processing module being configured to: acquire valid data from the data as valid data; acquire at least two initial data groups, each initial data group containing valid data from each batch when the same type of process equipment produces the same product and performs the same process step; and based on the initial data groups, acquire a first data group, the first data group containing valid data corresponding to all chambers in the process equipment being in a working state in the initial data group. The system performs sorting on the data in the first data group to form a first queue data group; it obtains a second queue data group, which is formed by sorting the remaining data after removing some data at the beginning and end of the first queue data group, and obtains the average amplitude corresponding to adjacent valid data in the second queue data group; based on the second queue data group and the average amplitude, it obtains a third queue data group, which is formed by sorting the remaining data after removing the next data in adjacent data when the actual change amplitude of adjacent data in the second queue data group deviates from the average amplitude; and it uses an acquisition module to obtain the minimum valid data and the maximum valid data based on all the data in the third queue data group, and to obtain the semiconductor equipment production capacity based on the minimum valid data and the maximum valid data.
[0008] The technical solution provided in this application has at least the following advantages:
[0009] In the above technical solution, after acquiring data from all process equipment (i.e., Takt Time, the time interval between the end of the current batch of production and the end of the previous batch of production), valid data is first filtered out to improve the accuracy of the acquired valid data, and at least two initial data sets are obtained from the valid data. Then, the valid data in the initial data sets is filtered to obtain a first data set, further improving the accuracy of the valid data in the first data set. Next, the data in the first data set is sorted to obtain a first queue data set, facilitating subsequent processing of the valid data. The valid data in the first queue data set is then filtered to obtain a second queue data set, improving the accuracy of the valid data in the second queue data set, and the average amplitude corresponding to adjacent valid data in the second queue data set is obtained. Based on the average amplitude, the valid data in the second queue data set is filtered to obtain a third queue data set, improving the accuracy of the valid data in the third queue data set. Therefore, by performing at least four filtering processes on all acquired process equipment data, the accuracy of the valid data in the final third queue data set is improved. Therefore, when further obtaining semiconductor equipment production capacity based on the data from all the third queue data groups, it is beneficial to improve the accuracy of the assessed semiconductor equipment production capacity by improving the accuracy of the acquired minimum and maximum effective data, thereby improving the accuracy of the assessment of the investment amount required for capacity expansion. Attached Figure Description
[0010] One or more embodiments are illustrated by way of example with reference to the accompanying drawings, which are not to be limited in scale unless otherwise stated.
[0011] Figure 1 A specific flowchart of a method for evaluating the production capacity of semiconductor equipment provided in an embodiment of this application;
[0012] Figure 2 Another specific flowchart of a method for evaluating the production capacity of semiconductor equipment provided in an embodiment of this application;
[0013] Figure 3 Another specific flowchart of a method for evaluating semiconductor device manufacturing capability provided in an embodiment of this application.
[0014] Figure 4 A schematic diagram of the functional modules of an apparatus for evaluating the production capacity of semiconductor equipment, provided as another embodiment of this application. Detailed Implementation
[0015] As can be seen from the background art, the accuracy of the assessed semiconductor equipment production capacity needs to be improved.
[0016] Analysis revealed that since the Run Size is a fixed value when the semiconductor equipment is under full load, the production capacity of the equipment is determined by the Take Time when the semiconductor equipment is under full load and operating stably.
[0017] Because the manufacturing process of semiconductor equipment is extremely precise, generally speaking, Takt Time has two main forms: (1) Takt Time is controlled by the clock inside the semiconductor equipment. For a given processing step, the fluctuation range of Takt Time as the processing batch increases is relatively small; (2) Takt Time is controlled by the advanced process control system inside the semiconductor equipment. During the process of collecting Takt Time, the Takt Time is continuously corrected by feeding back the key parameters of the process. At this time, Takt Time shows a slow increasing trend as the processing batch increases.
[0018] Therefore, it is evident that under full load and stable operation of semiconductor equipment, Takt Time has both maximum and minimum values, meaning Takt Time has boundary values. Consequently, the normal production capacity of semiconductor equipment also has maximum and minimum values. To obtain the effective boundary of Takt Time, it is necessary to correctly process Takt Time. Currently, Takt Time is mainly described using a normal distribution algorithm, without addressing the concept of boundary values. However, due to the unique distribution characteristics of Takt Time in semiconductor manufacturing, the normal distribution can only roughly describe the average level of Takt Time, and the accuracy of the determined average level is not high. Using this average level of Takt Time to assess the production capacity of semiconductor equipment will further reduce accuracy. The lower the accuracy of the assessed semiconductor equipment production capacity, the greater the deviation in the investment amount required for capacity planning. The investment amount required for capacity planning in the semiconductor industry is generally in the tens of billions of dollars; even a one percent deviation can lead to a difference of hundreds of millions of dollars in investment.
[0019] This application provides a method and apparatus for evaluating the production capacity of semiconductor equipment. In the evaluation method, the data (Takt Time) of all acquired process equipment is filtered at least four times to improve the accuracy of the valid data in the final third queue data group. Therefore, when subsequently obtaining the minimum and maximum valid data (i.e., the boundary values of Takt Time) based on the data in all the third queue data groups, it is beneficial to improve the accuracy of the final evaluated semiconductor equipment production capacity by increasing the accuracy of the obtained minimum and maximum valid data. Furthermore, due to the complexity of semiconductor manufacturing processes and the dynamic changes in production line conditions, determining the boundary values of Takt Time plays a crucial role. For example, by determining the maximum and minimum values of semiconductor equipment production capacity through the boundary values of Takt Time, a solid decision-making basis is provided for the capacity planning of semiconductor manufacturers. By improving the accuracy of the obtained boundary values of Takt Time, it is beneficial to improve the accuracy of the determined maximum and minimum investment amounts. This improves decision-making efficiency and, more importantly, the accuracy of the boundary values of the investment amount required for capacity expansion.
[0020] To make the objectives, technical solutions, and advantages of the embodiments of this application clearer, the various embodiments of this application will be described in detail below with reference to the accompanying drawings. However, those skilled in the art will understand that many technical details have been presented in the various embodiments of this application to enable readers to better understand this application. However, the technical solutions claimed in this application can be implemented even without these technical details and various changes and modifications based on the following embodiments.
[0021] One embodiment of this application provides a method for evaluating the production capacity of semiconductor equipment. The semiconductor structure provided in one embodiment of this application will be described in detail below with reference to the accompanying drawings. Figure 1 A specific flowchart of a method for evaluating the production capacity of semiconductor equipment provided in an embodiment of this application; Figure 2 Another specific flowchart of a method for evaluating the production capacity of semiconductor equipment provided in an embodiment of this application; Figure 3 This is yet another specific flowchart of a method for evaluating the production capacity of semiconductor equipment provided in an embodiment of this application.
[0022] refer to Figures 1 to 3 A method for evaluating the production capacity of semiconductor equipment, which includes different types of process equipment, includes the following steps:
[0023] S101: Process N batches of wafers using all process equipment and acquire data from all process equipment, which is the time interval between the end of the current batch production and the end of the previous batch production.
[0024] It should be noted that due to the complexity of the process flow and the dynamic changes in production line conditions, when processing wafers, the number of wafers produced in a particular batch may not be the maximum number that batch can produce. Alternatively, not all chambers of a particular process machine may be operational during wafer processing, or the process machine may experience a shutdown, increasing the time interval between the end of the current batch and the end of the previous batch. In these cases, the obtained data representing the time intervals of the process machines are not under full load and stable operation, which will affect the accuracy of the acquired data. Furthermore, the acquired data includes information indicating which batch, which chambers of which process machine are operational, which product is being produced, and which process step is being performed.
[0025] S102: Obtain valid data from the data as valid data.
[0026] The steps for obtaining valid data from the data include: based on the maximum processing capacity, which is the maximum number of wafers that can be processed in any batch for the same type of process machine, retaining the data corresponding to the maximum processing capacity when the number of wafers processed in the current batch is the maximum processing capacity, as valid data.
[0027] In this step, when the number of wafers produced in the current batch is not the maximum number of wafers that the batch can produce, the data representing the time interval between the end of the current batch production and the end of the previous batch production is filtered out, and the remaining data is retained as valid data. This ensures that the valid data represents the maximum number of wafers produced in the current batch, which helps to improve the accuracy of the obtained valid data.
[0028] It should be noted that the maximum number of wafers that different types of process equipment are allowed to process in any batch may be the same or different. This application embodiment does not limit the maximum number of wafers that a process equipment is allowed to process in any batch.
[0029] S103: Obtain at least two initial data sets, where each initial data set contains valid data from each batch of the same type of machine tool producing the same product and performing the same process step.
[0030] Due to the complexity of the process and the dynamic changes in the production line, different products may have the same process steps during manufacturing. For example, two products may both require etching, and different products may require different types of process equipment to work together. Therefore, the categories of valid data obtained in step S102 are quite diverse, and the differences between two random valid data may be large. By grouping the valid data in step S103, the valid data of each batch when the same type of process equipment produces the same product and performs the same process step are in the same initial data group. This helps to reduce the differences between valid data in the same initial data group and facilitates the subsequent processing and analysis of the valid data in each initial data group. This also helps to improve the accuracy of the results generated after the analysis and evaluation of the valid data.
[0031] S104: Based on the initial data set, obtain the first data set, where the data in the first data set is the valid data corresponding to all chambers in the process equipment in the initial data set being in working condition.
[0032] In this step, when a batch of process equipment processes wafers, if not all chambers of the process equipment are in working condition, the valid data of that batch is filtered out, and the remaining valid data is placed in the first data group. This ensures that the valid data in the first data group represents that all chambers of the current batch of process equipment are in working condition, so that the valid data in the first data group are all obtained when the process equipment is at full load, which helps to further improve the accuracy of the retained valid data.
[0033] S105: Sort the data in the first data group to form the first queue data group.
[0034] The sorting process includes ascending or descending sorting, which facilitates further processing of the valid data.
[0035] S106: Obtain the second queue data group, which is composed of the remaining data after removing some data at the beginning and end of the first queue data group, and obtain the average amplitude corresponding to adjacent valid data in the second queue data group.
[0036] The process of obtaining the second queue data group includes the following steps:
[0037] Based on the first ratio and the second ratio, the data in the first queue data group are divided into a first group, a second group, and a third group in sequence. The first ratio is the ratio of the number of valid data in the first group to the number of valid data in the first queue data group; the second ratio is the ratio of the number of valid data in the third group to the number of valid data in the first queue data group. The second group corresponds to the second queue data group. The range of both the first ratio and the second ratio is 10% to 40%.
[0038] The first ratio and the second ratio can be selected based on the dispersion of valid data in the first queue data group. In some embodiments, when the dispersion of valid data in the first queue data group is large, both the first ratio and the second ratio can be selected to be relatively small, for example, both the first ratio and the second ratio can be 10%, to ensure a sufficient number of valid data in the second queue data group; in other embodiments, when the dispersion of valid data in the first queue data group is small, both the first ratio and the second ratio can be selected to be relatively large, for example, both the first ratio and the second ratio can be 40%; in still other embodiments, when the dispersion of valid data in the first queue data group is moderate, both the first ratio and the second ratio can be selected from the middle range of 10% to 40%, for example, both the first ratio and the second ratio can be 20%.
[0039] It should be noted that in the above embodiments, the first ratio and the second ratio may be equal or unequal. For example, the first ratio may be 10% and the second ratio may be 20%. Furthermore, the selection of the first ratio and the selection of the second ratio may be different in different first queue data groups.
[0040] Obtaining the average amplitude involves the following steps:
[0041] In some embodiments, a first threshold is obtained based on a first group and a second threshold is obtained based on a third group. When the sorting process is ascending, the first threshold is the valid data with the largest value in the first group and the second threshold is the valid data with the smallest value in the third group; or, when the sorting process is descending, the first threshold is the valid data with the smallest value in the first group and the second threshold is the valid data with the largest value in the third group. An average amplitude is obtained based on the first threshold, the second threshold, and the number of valid data in the second group. The average amplitude is the ratio of the absolute value of the difference between the first threshold and the second threshold to the number of valid data in the second group.
[0042] In other embodiments, a first threshold and a second threshold are obtained based on a second group, where the first threshold is the valid data with the smallest value in the second group, the second threshold is the valid data with the largest value in the second group, and the average amplitude is the ratio of the absolute value of the difference between the first threshold and the second threshold to the number of valid data in the second group.
[0043] It should be noted that when the amount of valid data in the first queue data group is large enough, such as 1000, the difference in the average amplitude obtained by the two embodiments is small; when the amount of valid data in the first queue data group is small enough, such as 100, the embodiment of obtaining the first threshold and the second threshold based on the second group is beneficial to improving the accuracy of the obtained average amplitude.
[0044] S107: Based on the second queue data group and the average amplitude, obtain the third queue data group. The third queue data group is formed by sorting the remaining data after removing the next data in the adjacent data group when the actual change amplitude of adjacent data in the second queue data group deviates from the average amplitude.
[0045] The process of obtaining the third queue data group includes the following steps:
[0046] The first coefficient is provided, and the subsequent data corresponding to the deviation of the actual change of the preceding and following data in the second queue data group from the average is represented by the following first relational expression:
[0047] |T Next Lot -T Prev Lot |>C0*A
[0048] Among them, T Prev Lot T represents the preceding valid data in the second queue data group, corresponding to the preceding and following data. Ncxt Lot This indicates the next valid data point in the second queue data group, where C0 represents the first coefficient and A represents the average amplitude.
[0049] Remove the last valid data from the data corresponding to the first relation, and sort the remaining valid data to form the third queue data group.
[0050] The first relational expression mentioned above is used to determine whether the degree of change between adjacent valid data in the second queue data group is within the allowable range. When the degree of change between adjacent valid data is not within the allowable range, it indicates that the difference between the latter valid data and other valid data is large. This valid data can be regarded as inaccurate valid data. Inaccurate valid data is screened out. For example, data obtained when the process equipment is in a downtime state is screened out, and data obtained when the process equipment is not in stable operation due to other factors are screened out. The remaining valid data is located in the third queue data group, which is conducive to improving the accuracy of the valid data in the third queue data group and reducing the dispersion of the valid data in the third queue data group. This is conducive to improving the accuracy of the results generated after subsequent analysis and evaluation of valid data.
[0051] The first coefficient can range from 0.001 to 0.05. When the dispersion of valid data in the second queue data group is large, the first coefficient can be selected to be larger, that is, the degree of change between adjacent valid data in the second queue data group is allowed to be large. For example, the first coefficient can be 0.049 or 0.05. When the dispersion of valid data in the second queue data group is small, the first coefficient can be selected to be smaller, that is, the degree of change between adjacent valid data in the second queue data group is allowed to be small. For example, the first coefficient can be 0.001 or 0.002.
[0052] In other embodiments, the subsequent data corresponding to the deviation of the actual change magnitude of the preceding and following data in the second queue data group from the average magnitude is represented by the following first relational expression:
[0053] |T Next Lot -T Prev Lot |≥C0*A
[0054] S108: Based on the data in all third queue data groups, obtain the minimum and maximum valid data.
[0055] The methods for obtaining the minimum and maximum valid data are as follows:
[0056] In some embodiments, reference Figure 1 The minimum and maximum valid data can be directly obtained from all the third queue data groups acquired in step S107. Because the data representing the time interval between the end of the current batch production and the end of the previous batch production was screened multiple times in steps S101 to S107, the valid data in the third queue data group represents both the maximum number of wafers produced in the current batch and the fact that all chambers of the process equipment in the current batch are in operation. This ensures that the valid data in the third queue data group is acquired when the process equipment is at full load. Furthermore, it helps to ensure that the valid data in the third queue data group does not include data acquired when the process equipment is down or not operating stably. Therefore, it helps to improve the accuracy of the valid data in the third queue data group, thereby improving the accuracy of the minimum and maximum valid data, and ultimately improving the accuracy of the results generated after subsequent analysis and evaluation of the valid data.
[0057] In other embodiments, reference is made to... Figure 2 After obtaining the third queue data group in step 107 and before obtaining the minimum and maximum valid data in step 108, the evaluation method may further include the following steps:
[0058] S110: Get the first number and the second number. The first number is the number of valid data in the third queue data group, and the second number is the number of valid data in the first data group corresponding to the third queue data group.
[0059] S120: Set a first preset value and a second preset value based on the dispersion of the valid data in the first data group.
[0060] S130: Retain the third queue data group where the second number is greater than the first preset value and the ratio of the first number to the second number is greater than the second preset value.
[0061] Steps S110 to S130 involve filtering the acquired multiple third queue data groups, retaining the third queue data groups where the second number is greater than a first preset value. This ensures that the number of valid data points in the first data group corresponding to the third queue data group is sufficient, making the third queue data group valuable for subsequent processing. Furthermore, third queue data groups where the ratio of the first number to the second number is greater than a second preset value are retained. This ensures that the third queue data group has a sufficient number of valid data points relative to its corresponding first data group. Since a third queue data group with a smaller number of valid data points compared to its corresponding first data group is not very meaningful as a reference sample and may even affect the accuracy of the subsequently acquired minimum and maximum valid data points, steps S110 to S130 are beneficial in improving the stability of the retained third queue data groups as reference samples, thereby improving the accuracy of the subsequently acquired minimum and maximum valid data points.
[0062] In yet other embodiments, reference is made to Figure 3 After obtaining the first data group in step S104, and before sorting the data in the first data group in step S105, the evaluation method may further include:
[0063] S140: Obtain the second number, which is the number of valid data in the first data group; set a first preset value according to the dispersion of the valid data in the first data group; retain the first data group whose second number is greater than the first preset value.
[0064] This step helps ensure that the number of valid data points in the first data set is sufficient to serve as a suitable reference sample, enabling subsequent processing of the first data set to obtain the third queue data set, which then has reference value. Furthermore, filtering the first data set before step S105 helps reduce the number of data points in the first data set. This allows for processing fewer valid data points during subsequent operations based on the first data set, reducing the time required for subsequent steps and improving the evaluation efficiency of the evaluation method.
[0065] After obtaining the third queue data group in step S107 and before obtaining the minimum and maximum valid data in step S108, the evaluation method may further include:
[0066] S150: Obtain the first number, which is the number of valid data in the third queue data group; set a second preset value based on the dispersion of valid data in the first data group; retain the third queue data group whose ratio of the first number to the second number is greater than the second preset value.
[0067] This step helps ensure that the third data group has a sufficient number of valid data points compared to its corresponding first data group. Since the number of valid data points in the third data group is smaller than that in the corresponding first data group, this third data group is not very meaningful as a reference sample and may even affect the accuracy of the subsequently obtained minimum and maximum valid data points. Therefore, filtering out this portion of the third data group helps improve the accuracy of the subsequently obtained minimum and maximum valid data points.
[0068] It should be noted that in the two embodiments of filtering the third queue data group described above, the steps of obtaining the minimum and maximum valid data based on the data in all the third queue data groups are as follows: obtain the minimum and maximum valid data from all the remaining third queue data groups.
[0069] Furthermore, the range of the first preset value can be 10 to 100, and the range of the second preset value can be 10% to 80%. Since both the first and second preset values are set according to the dispersion of the effective data in the first data group, when the dispersion of the effective data in the first data group is large, both the first and second preset values can be selected to be smaller. For example, the first preset value can be 10 or 15, and the second preset value can be 10% or 15%. When the dispersion of the effective data in the first data group is small, both the first and second preset values can be selected to be larger. For example, the first preset value can be 95 or 100, and the second preset value can be 75% or 80%.
[0070] In the three embodiments described above, obtaining the minimum and maximum valid data each includes the following two methods:
[0071] In some embodiments, obtaining the minimum valid data and the maximum valid data includes the following steps:
[0072] Get the maximum and minimum values of valid data in each third queue data group. The minimum valid data is the smallest valid data among multiple minimum values, and the maximum valid data is the largest valid data among multiple maximum values.
[0073] In other embodiments, obtaining the minimum and maximum valid data includes the following steps:
[0074] Get the first and second numbers. The first number is the number of valid data in the third queue data group, and the second number is the number of valid data in the first data group corresponding to the third queue data group.
[0075] The third queue of data with the largest ratio of the first number to the second number is taken as the reference group. The minimum valid data is the minimum value of the valid data in the reference group, and the maximum valid data is the maximum value of the valid data in the reference group.
[0076] S109: Obtain semiconductor equipment production capacity based on the minimum and maximum valid data.
[0077] Acquiring semiconductor equipment manufacturing capabilities includes the following steps:
[0078] Provide a second and a third coefficient;
[0079] Provides the maximum processing capacity, which is the maximum number of wafers allowed to be processed in any batch for the same type of process equipment;
[0080] Semiconductor equipment production capacity is expressed by the following second equation:
[0081] WPH∈[Run Size / (M2*Max), Run Size / (M1*Min)]
[0082] Where WPH represents semiconductor equipment production capacity, Run Size represents maximum processing volume, M1 represents the second coefficient, M2 represents the third coefficient, Min represents the minimum effective data, and Max represents the maximum effective data.
[0083] Because the minimum and maximum valid data obtained in the above steps are highly accurate, the boundary values of semiconductor equipment production capacity obtained through the second relational expression and the range formed by the boundary values are also highly accurate.
[0084] The second coefficient ranges from 0.999 to 1, and the third coefficient ranges from 1 to 1.001. During the actual operation of semiconductor equipment, due to various factors (such as equipment downtime or not operating at full capacity), the acquired Take Time can fluctuate significantly with changes in production batches. This abnormal fluctuation does not reflect the normal operating level of the equipment. Filtering the Take Time through the aforementioned steps improves the accuracy of the minimum and maximum effective data obtained. Furthermore, adding the concepts of the second and third coefficients in step S109 further improves the accuracy of the final acquired boundary values of the semiconductor equipment's production capacity and the range formed by these boundary values, thereby further improving the accuracy of the assessed semiconductor equipment production capacity.
[0085] In summary, the above evaluation method not only involves at least four rounds of screening of the acquired Take Time data but also screening of the third cohort of valid data. This improves the accuracy of the minimum and maximum valid data obtained, thereby enhancing the accuracy of the subsequent assessment of semiconductor equipment production capacity. Furthermore, processing the minimum and maximum valid data to obtain the boundary values of semiconductor equipment production capacity and the range defined by these boundary values further improves the accuracy of the assessed semiconductor equipment production capacity, thereby increasing the accuracy of the assessment of the investment amount required for capacity expansion.
[0086] Another embodiment of this application provides an apparatus for evaluating the production capacity of semiconductor equipment, used to implement the method for evaluating the production capacity of semiconductor equipment described in the above embodiments. The apparatus for evaluating the production capacity of semiconductor equipment provided in another embodiment of this application will be described in detail below with reference to the accompanying drawings. Figure 4 A schematic diagram of the functional modules of an apparatus for evaluating the production capacity of semiconductor equipment, provided as another embodiment of this application.
[0087] refer to Figure 4 An apparatus for evaluating the production capacity of semiconductor equipment, comprising: a data collection module 401 for acquiring data from all process equipment, the data being the time interval between the end of the current batch production and the end of the previous batch production at the process equipment; and a data processing module 402 for processing the data, the data processing module 402 being configured to: acquire valid data from the data as valid data; acquire at least two initial data sets, each initial data set containing valid data from each batch when the same type of process equipment produces the same product and performs the same process step; based on the initial data sets, acquire a first data set, the first data set containing valid data corresponding to all chambers in the process equipment being in a working state; and process the data in the first data set... The data is sorted to form a first queue data group; a second queue data group is obtained, which is formed by sorting the remaining data after removing some data at the beginning and end of the first queue data group, and the average amplitude corresponding to adjacent valid data in the second queue data group is obtained; based on the second queue data group and the average amplitude, a third queue data group is obtained, which is formed by sorting the remaining data after removing the next data in adjacent data when the actual change amplitude of adjacent data in the second queue data group deviates from the average amplitude; the acquisition module 403 is used to obtain the minimum valid data and the maximum valid data based on all the data in the third queue data group, and to obtain the semiconductor equipment production capacity based on the minimum valid data and the maximum valid data.
[0088] The data processing module 402 includes: a validity filtering unit 412, used to obtain valid data from the data as valid data; a grouping unit 422, used to obtain at least two initial data groups, each initial data group containing valid data from batches of the same type of machine tool producing the same product and performing the same process step, and based on the initial data groups, obtaining a first data group, the first data group containing valid data corresponding to all chambers in the machine tool being in working state in the initial data group; a sorting unit 432, used to sort the data in the first data group to form a first queue data group; and a first screening unit. Selection unit 442 is used to remove several data points at the beginning and end of the first queue data group, and the remaining data in the first queue data group are sorted to form the second queue data group; first calculation unit 452 is used to obtain the average amplitude corresponding to adjacent valid data in the second queue data group; second filtering unit 462 is used to, based on the second queue data group and the average amplitude, when the actual change amplitude of adjacent data in the second queue data group deviates from the average amplitude, sort the remaining data after removing the next data in the adjacent data, that is, after removing the next data in the adjacent data, the remaining valid data in the second queue data group are sorted to form the third queue data group.
[0089] In some embodiments, the data processing module 402 includes a first storage unit 414 and a second storage unit 424, wherein the validity filtering unit 412, the grouping unit 422, and the sorting unit 432 are all located in the first storage unit 414, and the first filtering unit 442, the first calculation unit 452, and the second filtering unit 462 are all located in the second storage unit 424. The data processing module 402 further includes a data transfer unit 472, used to transfer valid data from the first queue data group to the first filtering unit 442, that is, to transfer the data finally stored in the first storage unit 414 to the second storage unit 424.
[0090] In other embodiments, the validity filtering unit, grouping unit, sorting unit, first filtering unit, first calculation unit and second filtering unit are all located in the same storage unit, so there is no need for a data transfer unit to transmit data between the sorting unit and the first filtering unit.
[0091] The acquisition module 403 includes: a coefficient configuration unit 413, which provides a second coefficient, a third coefficient, and a maximum processing quantity, wherein the maximum processing quantity is the maximum number of wafers that can be processed in any batch for the same type of process equipment; and a second calculation unit 423, which calculates the semiconductor equipment production capacity based on the minimum effective data, the maximum effective data, the second coefficient, the third coefficient, and the maximum processing quantity.
[0092] In summary, the apparatus used to assess semiconductor equipment production capacity can perform at least four screenings on the acquired Take Time and further screening on a third cohort of valid data. This improves the accuracy of the minimum and maximum valid data obtained, thereby enhancing the accuracy of the subsequent assessment of semiconductor equipment production capacity. Furthermore, subsequent processing of the minimum and maximum valid data to obtain the boundary values of semiconductor equipment production capacity and the range defined by these boundary values further improves the accuracy of the assessed semiconductor equipment production capacity, thereby increasing the accuracy of the assessment of the investment amount required for capacity expansion.
[0093] Those skilled in the art will understand that the above-described embodiments are specific examples of implementing this application, and in practical applications, various changes in form and detail may be made without departing from the spirit and scope of this application. Any person skilled in the art can make their own modifications and alterations without departing from the spirit and scope of this application; therefore, the scope of protection of this application should be determined by the scope defined in the claims.
Claims
1. A method for evaluating the production capacity of semiconductor equipment, said semiconductor equipment comprising different types of process equipment, characterized in that, include: The process equipment is used to process N batches of wafers, and data of all the process equipment is acquired. The data is the time interval between the end of the current batch production and the end of the previous batch production of the current batch. Select the valid data from the data as valid data; Obtain at least two initial data sets, wherein the data in each initial data set is the valid data of each batch when the same type of process machine produces the same product and performs the same process step; Based on the initial data set, a first data set is obtained, wherein the data in the first data set is the valid data corresponding to all chambers in the process equipment being in working state in the initial data set; The data in the first data group is sorted to form a first queue data group; Obtain a second queue data group, which is composed of sorted data remaining after removing some data at the beginning and end of the first queue data group, and obtain the average amplitude corresponding to adjacent valid data in the second queue data group. Based on the second queue data group and the average amplitude, a third queue data group is obtained. The third queue data group is formed by sorting the remaining data after removing the next data in the adjacent data group when the actual change amplitude of adjacent data in the second queue data group deviates from the average amplitude. Based on the data in all the third queue data groups, obtain the minimum and maximum valid data; Based on the minimum effective data and the maximum effective data, obtain the semiconductor equipment production capacity; The steps for obtaining valid data from the data as the valid data include: Based on the maximum processing capacity, which is the maximum number of wafers that can be processed in any batch for the same type of process machine, the data corresponding to the current batch processing number of wafers being the maximum processing capacity is retained as the valid data; The steps for obtaining the semiconductor equipment production capacity include: Provide a second and a third coefficient; Provide a maximum processing capacity, which is the maximum number of wafers allowed to be processed in any batch for the same type of process equipment; The semiconductor equipment production capacity is represented by the following second equation: Wherein, WPH represents the semiconductor equipment production capacity, and Run Size represents the maximum processing volume. This represents the second coefficient. The third coefficient is represented by Min, the minimum valid data is represented by Max, and the maximum valid data is represented by Max.
2. The method as described in claim 1, characterized in that, The steps for obtaining the second queue data group include: Based on the first ratio and the second ratio, the data in the first queue data group is divided into a first group, a second group, and a third group in sequence. The first ratio is the ratio of the number of valid data in the first group to the number of valid data in the first queue data group; the second ratio is the ratio of the number of valid data in the third group to the number of valid data in the first queue data group. The second group corresponds to the second queue data group. The range of both the first ratio and the second ratio is 10% to 40%.
3. The method as described in claim 2, characterized in that, The sorting process includes ascending sorting or descending sorting, and the step of obtaining the average magnitude includes: A first threshold is obtained based on the first group, and a second threshold is obtained based on the third group. The first threshold is the valid data with the largest value in the first group, and the second threshold is the valid data with the smallest value in the third group. Alternatively, the first threshold is the valid data with the smallest value in the first group, and the second threshold is the valid data with the largest value in the third group. The average amplitude is obtained based on the first threshold, the second threshold, and the number of valid data in the second group. The average amplitude is the ratio of the absolute value of the difference between the first threshold and the second threshold to the number of valid data in the second group.
4. The method as described in claim 1, characterized in that, The steps for obtaining the third queue data group include: A first coefficient is provided, and the subsequent data corresponding to the actual change magnitude of the preceding and following data in the second queue data group deviating from the average magnitude is represented by the following first relational expression: in, This indicates the preceding valid data in the second queue data group, corresponding to the preceding and following data. This indicates the next valid data in the second queue data group that corresponds to the previous data. Let A represent the first coefficient and A represent the average amplitude. Remove the last valid data from the data corresponding to the first relation, and sort the remaining valid data to form the third queue data group.
5. The method as described in claim 4, characterized in that, The range of the first coefficient is 0.001 to 0.
05.
6. The method as described in claim 1, characterized in that, After obtaining the third queue data group, and before obtaining the minimum valid data and the maximum valid data, the process further includes: Obtain a first number and a second number, wherein the first number is the number of valid data in the third queue data group, and the second number is the number of valid data in the first data group corresponding to the third queue data group; A first preset value and a second preset value are set according to the degree of dispersion of the effective data in the first data group; The third queue data group is retained if the second number is greater than the first preset value and the ratio of the first number to the second number is greater than the second preset value.
7. The method as described in claim 1, characterized in that, After obtaining the first data group, and before sorting the data in the first data group, the process further includes: Obtain a second number, which is the number of valid data in the first data group; A first preset value is set based on the degree of dispersion of the effective data in the first data group; The first data group whose second number is greater than the first preset value is retained; After obtaining the third queue data group, and before obtaining the minimum valid data and the maximum valid data, the process further includes: Obtain the first number, which is the number of valid data in the third queue data group; A second preset value is set based on the degree of dispersion of the effective data in the first data group; The third queue data group whose ratio of the first number to the second number is greater than the second preset value is retained.
8. The method as described in claim 6 or 7, characterized in that, The steps for obtaining the minimum valid data and the maximum valid data based on the data in all the third queue data groups include: Obtain the minimum valid data and the maximum valid data from all remaining third queue data groups.
9. The method as described in claim 8, characterized in that, The first preset value is in the range of 10 to 100.
10. The method as described in claim 8, characterized in that, The second preset value ranges from 10% to 80%.
11. The method as described in claim 1, characterized in that, The steps for obtaining the minimum valid data and the maximum valid data include: Obtain the maximum and minimum values of the valid data in each of the third queue data groups, wherein the minimum valid data is the smallest valid data among the plurality of minimum values, and the maximum valid data is the largest valid data among the plurality of maximum values.
12. The method as described in claim 1, characterized in that, The steps for obtaining the minimum valid data and the maximum valid data include: Obtain a first number and a second number, wherein the first number is the number of valid data in the third queue data group, and the second number is the number of valid data in the first data group corresponding to the third queue data group; The third queue data group with the largest ratio of the first number to the second number is taken as the reference group. The minimum valid data is the minimum value of the valid data in the reference group, and the maximum valid data is the maximum value of the valid data in the reference group.
13. The method as described in claim 1, characterized in that, The second coefficient ranges from 0.999 to 1, and the third coefficient ranges from 1 to 1.
001.
14. An apparatus for evaluating the production capacity of semiconductor equipment, characterized in that, include: The data collection module is used to acquire data from all process equipment, wherein the data is the time interval between the end of the current batch production and the end of the previous batch production of the current batch. A data processing module is configured to process the data. The process involves: acquiring valid data from the data set; acquiring at least two initial data sets, where each initial data set contains valid data from batches of the same type of machine tool producing the same product and performing the same process step; acquiring a first data set based on the initial data sets, where the data in the first data set corresponds to valid data where all chambers in the machine tool are in working condition; sorting the data in the first data set to form a first queue data set; acquiring a second queue data set, which is formed by sorting the remaining data after removing some data from the first and last data sets in the first queue data set, and acquiring the average amplitude corresponding to adjacent valid data in the second queue data set; and acquiring a third queue data set based on the second queue data set and the average amplitude, where the third queue data set is formed by sorting the remaining data after removing the next data from adjacent data sets when the actual change amplitude of adjacent data in the second queue data set deviates from the average amplitude. The acquisition module is used to acquire the minimum valid data and the maximum valid data based on the data in all the third queue data groups, and to acquire the semiconductor equipment production capacity based on the minimum valid data and the maximum valid data; Obtaining valid data from the data as the valid data includes: Based on the maximum processing capacity, which is the maximum number of wafers that can be processed in any batch for the same type of process machine, the data corresponding to the current batch processing number of wafers being the maximum processing capacity is retained as the valid data; Acquiring the semiconductor equipment manufacturing capability includes: Provide a second and a third coefficient; Provide a maximum processing capacity, which is the maximum number of wafers allowed to be processed in any batch for the same type of process equipment; The semiconductor equipment production capacity is represented by the following second equation: Wherein, WPH represents the semiconductor equipment production capacity, and Run Size represents the maximum processing volume. This represents the second coefficient. The third coefficient is represented by Min, the minimum valid data is represented by Max, and the maximum valid data is represented by Max.
15. The apparatus as claimed in claim 14, characterized in that, The data processing module includes: A validity filtering unit is used to obtain valid data from the data as the valid data. A grouping unit is used to acquire at least two initial data groups, wherein the data in each initial data group is the valid data of each batch when the same type of process machine produces the same product and performs the same process step, and based on the initial data group, acquires the first data group, wherein the data in the first data group is the valid data corresponding to all chambers in the process machine being in working state in the initial data group. A sorting unit is used to sort the data in the first data group to form the first queue data group; The first filtering unit is used to remove several data at the beginning and end of the first queue data group, and the remaining data in the first queue data group are sorted to form the second queue data group. The first calculation unit is used to obtain the average amplitude corresponding to adjacent valid data in the second queue data group; The second filtering unit is used to, based on the second queue data group and the average amplitude, remove the next data in the adjacent data when the actual change amplitude of adjacent data in the second queue data group deviates from the average amplitude, and sort the remaining valid data in the second queue data group to form the third queue data group.
16. The apparatus as claimed in claim 15, characterized in that, The data processing module further includes: A data transfer unit is used to transfer the valid data in the first queue data group to the first filtering unit.
17. The apparatus as claimed in claim 14 or 15, characterized in that, The acquisition module includes: A coefficient configuration unit is used to provide the second coefficient, the third coefficient, and the maximum processing quantity; The second calculation unit is used to calculate the semiconductor equipment production capacity based on the minimum effective data, the maximum effective data, the second coefficient, the third coefficient, and the maximum processing volume.