Method and apparatus for evaluating production capacity of semiconductor devices

Abstract

Embodiments of the present application relate to the field of semiconductor manufacturing, and provide a method and a device for evaluating the production capacity of semiconductor equipment. The method includes: obtaining the time interval between the end of the current batch production of all process machines and the end of the previous batch production of the current batch; obtaining valid data from the data as valid data; obtaining at least two initial data groups; obtaining a first data group based on the initial data groups; performing sorting processing on the data in the first data group to form a first queue data group, and obtaining the standard deviation of the data in the first queue data group; obtaining a second queue data group based on the first queue data group and the standard deviation; obtaining the minimum valid data and the maximum valid data based on the data in all the second queue data groups; and obtaining the production capacity of the semiconductor equipment based on the minimum valid data and the maximum valid data. Embodiments of the present application are beneficial to improving the accuracy of evaluating the production capacity of semiconductor equipment.

Description

Technical Field

[0001] Embodiments of the present application relate to the field of semiconductor manufacturing, and in particular, to a method and device for evaluating the production capacity of semiconductor equipment. Background Art

[0002] One of the tasks of capacity planning in a semiconductor manufacturing plant is to plan the number of semiconductor equipment according to the capacity expansion requirements, and at the same time estimate the investment amount according to the planned number of semiconductor equipment. The number of semiconductor equipment mainly depends on the production capacity of the semiconductor equipment. In addition, the production capacity of the semiconductor equipment also indirectly determines the investment amount required for capacity expansion.

[0003] Currently, in the field of semiconductor manufacturing, when the semiconductor equipment is in a fully loaded and stable operating state, for a certain determined semiconductor equipment and a determined process step, the production capacity of the semiconductor equipment is usually measured by the number of wafers produced per unit time by the semiconductor equipment (WPH, wafer per hour). The mathematical definition of WPH is: WPH = Run Size / TaktTime, where Run Size represents the maximum number of wafers that a certain determined semiconductor equipment is allowed to produce per batch. When the semiconductor equipment is in a fully loaded state, Run Size is a fixed value, and Takt Time represents the time interval between the end of the current batch production of the process machine in the semiconductor equipment and the end of the previous batch production of the current batch.

[0004] However, as the semiconductor equipment operates, the semiconductor equipment is not necessarily in a fully loaded and stable operating state at every moment, and there are differences between the Takt Time values collected at different times, resulting in differences in WPH at different times, and these factors will interfere with the evaluation of the production capacity of the semiconductor equipment. Therefore, there is an urgent need for a method for evaluating the production capacity of semiconductor equipment to improve the accuracy of the evaluated production capacity of the semiconductor equipment. Summary of the Invention

[0005] Embodiments of the present application provide a method and device for evaluating the production capacity of semiconductor equipment, which is at least beneficial to improving the accuracy of the evaluated production capacity of the semiconductor equipment.

[0006] According to some embodiments of the present application, on the one hand, an embodiment of the present application provides a method for evaluating the production capacity of a semiconductor device. The semiconductor device includes different types of process machines. The evaluation method includes: using all the process machines to perform production processing on N batches of wafers, and obtaining data of all the process machines, where the data is the time interval between the end of the current batch production and the end of the previous batch production of the process machine; obtaining valid data from the data as valid data; obtaining at least two initial data groups, where the data in each initial data group is the valid data of each batch when the process machines of the same type produce the same product and perform the same process step; based on the initial data groups, obtaining a first data group, where the data in the first data group is the valid data corresponding to all chambers in the process machines in the initial data groups being in a working state; performing sorting processing on the data in the first data group to form a first queue data group, and obtaining the standard deviation of the data in the first queue data group; based on the first queue data group and the standard deviation, obtaining a second queue data group, where the second queue data group is formed by sorting the remaining data after removing the latter data in the front and back data when the difference between adjacent front and back data in the first queue data group deviates from the standard deviation; based on the data in all the second 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 production capacity of the semiconductor device.

[0007] According to some embodiments of the present application, on the other hand, an apparatus for evaluating the production capacity of a semiconductor device is further provided, including: a data collection module for obtaining data of all process machines, where the data is the time interval between the end of the current batch production and the end of the previous batch production of the process machine; a data processing module for processing the data, and the data processing module is configured to: obtain valid data in the data as valid data; obtain at least two initial data groups, where the data in each initial data group is the valid data of each batch when the process machines of the same type produce the same product and perform the same process step; based on the initial data groups, obtain a first data group, where the data in the first data group is the valid data corresponding to all chambers in the process machines in the initial data groups being in a working state; perform sorting processing on the data in the first data group to form a first queue data group, and obtain the standard deviation of the data in the first queue data group; based on the first queue data group and the standard deviation, obtain a second queue data group, where the second queue data group is formed by sorting the remaining data after removing the latter data in the front and back data when the difference between adjacent front and back data in the first queue data group deviates from the standard deviation; an obtaining module for obtaining the minimum valid data and the maximum valid data based on the data in all the second queue data groups, and obtaining the production capacity of the semiconductor device based on the minimum valid data and the maximum valid data.

[0008] Compared with the related art, the technical solutions provided by the embodiments of the present application have the following advantages:

[0009] In the above technical solution, after obtaining the data of all process machines (this data is the Takt Time, which is the time interval between the end of the current batch production of the process machine and the end of the previous batch production of the current batch), valid data is first screened out from the data to improve the accuracy of the obtained valid data, and at least two initial data groups are obtained from the valid data; subsequently, the valid data in the initial data groups is screened to obtain the first data group to improve the accuracy of the valid data in the first data group; then, the data in the first data group is sorted to obtain the first queue data group, which is convenient for subsequent other processing of the valid data, and the standard deviation of the data in the first queue data group is obtained; based on the standard deviation, the valid data in the first queue data group is screened to obtain the second queue data group to improve the accuracy of the valid data in the second queue data group. Therefore, by screening the data of all process machines obtained at least three times, the accuracy of the valid data in the finally formed second queue data group is improved. Therefore, when subsequently obtaining the minimum valid data and the maximum valid data based on the data in all the second queue data groups and further obtaining the production capacity of the semiconductor equipment, it is beneficial to improve the accuracy of the obtained minimum valid data and maximum valid data, so as to improve the accuracy of the evaluated production capacity of the semiconductor equipment, thereby improving the accuracy of the evaluated investment amount required for capacity expansion. BRIEF DESCRIPTION OF THE DRAWINGS

[0010] One or more embodiments are exemplarily illustrated by the pictures in the corresponding drawings. Unless otherwise stated, the figures in the drawings do not constitute a scale limitation.

[0011] Figure 1 FIG. is a specific flowchart of a method for evaluating the production capacity of a semiconductor device provided by an embodiment of the present application;

[0012] Figure 2 FIG. is another specific flowchart of a method for evaluating the production capacity of a semiconductor device provided by an embodiment of the present application;

[0013] Figure 3 FIG. is still another specific flowchart of a method for evaluating the production capacity of a semiconductor device provided by an embodiment of the present application

[0014] Figure 4 FIG. is a schematic diagram of functional modules of a device for evaluating the production capacity of a semiconductor device provided by another embodiment of the present application. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0015] As can be seen from the background art, the accuracy of the currently evaluated production capacity of semiconductor devices needs to be improved.

[0016] After analysis, it is found that since the Run Size is a fixed value when the semiconductor equipment is at full load, the production capacity of the equipment is determined by the Takt Time when the semiconductor equipment is at full load and operating stably.

[0017] Due to the extremely precise manufacturing process of semiconductor equipment, generally speaking, there are mainly two forms of Takt Time: (1) Takt Time is controlled by the internal clock of the semiconductor equipment. For a certain processing step, the fluctuation range of Takt Time with the increase of the processing batch 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, Takt Time is continuously corrected by feeding back the key parameters of the process. At this time, Takt Time shows a slow increasing trend with the increase of the processing batch.

[0018] It can be seen from this that when the semiconductor equipment is at full load and operating stably, there are maximum and minimum values of Takt Time, that is, there are boundary values for Takt Time. Furthermore, it can be known that there are also maximum and minimum values for the normal production capacity of the semiconductor equipment. In order to obtain the effective boundary of Takt Time, it is necessary to correctly handle Takt Time. At present, the processing of Takt Time mainly uses the normal distribution algorithm to describe, and the concept of boundary values is not involved. 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 determining the average level of Takt Time is not high. Using this average level of Takt Time to evaluate the production capacity of semiconductor equipment will further reduce the accuracy. The lower the accuracy of evaluating the production capacity of semiconductor equipment, the greater the deviation will be generated for the investment amount required for production capacity planning. The investment amount required for production capacity planning in the semiconductor industry is generally at the level of tens of billions of US dollars. Even a deviation of one percent will result in a difference in investment amount of hundreds of millions of US dollars.

[0019] An embodiment of the present application provides a method and a device for evaluating the production capacity of semiconductor equipment. In the evaluation method, data (Takt Time) of all process machines obtained is screened at least three times to improve the accuracy of valid data in the finally formed second queue data group. Therefore, subsequently, when obtaining the minimum valid data and the maximum valid data based on the data in all second queue data groups, that is, when obtaining the boundary values of Takt Time, it is beneficial to improve the accuracy of the finally evaluated production capacity of semiconductor equipment by improving the accuracy of the obtained minimum valid data and maximum valid data. In addition, due to the complex semiconductor manufacturing process flow and the dynamically changing production line conditions, the determination of the boundary values of Takt Time can play an important role. For example, the maximum and minimum values of the production capacity of semiconductor equipment are determined through the boundary values of Takt Time, providing a solid decision-making basis for the production capacity planning of semiconductor manufacturing plants. By improving the accuracy of the obtained boundary values of Takt Time, it is beneficial to improve the accuracy of the maximum and minimum values of the determined investment amount, enhancing the decision-making efficiency on the one hand and improving the accuracy of the boundary values of the investment amount required for evaluating production capacity expansion on the other hand.

[0020] To make the objectives, technical solutions, and advantages of the embodiments of the present application clearer, the embodiments of the present application will be described in detail below with reference to the accompanying drawings. However, those of ordinary skill in the art can understand that in the embodiments of the present application, many technical details are presented to help readers better understand the present application. However, even without these technical details and various changes and modifications based on the following embodiments, the technical solutions claimed in the present application can still be implemented.

[0021] An embodiment of the present application provides a method for evaluating the production capacity of semiconductor equipment. The following will describe in detail the semiconductor structure provided by an embodiment of the present application with reference to the accompanying drawings. Figure 1 It is a specific flowchart of a method for evaluating the production capacity of semiconductor equipment provided by an embodiment of the present application; Figure 2 It is another specific flowchart of a method for evaluating the production capacity of semiconductor equipment provided by an embodiment of the present application; Figure 3 It is yet another specific flowchart of a method for evaluating the production capacity of semiconductor equipment provided by an embodiment of the present application.

[0022] Refer to Figures 1 to 3 , a method for evaluating the production capacity of semiconductor equipment. The semiconductor equipment includes different types of process machines. The evaluation method includes the following steps:

[0023] S101: Use all process machines to process wafers in N batches, and obtain data of all process machines. This data is the time interval between the end of the current batch production and the end of the previous batch production of the process machine.

[0024] It should be noted that due to the complex process flow and dynamic changes in the production line conditions, when all process machines process wafers, in different process machines and / or different process steps, the number of wafers produced in a certain batch is not the maximum number of wafers that can be produced in this batch. Or, when a certain batch of process machines processes wafers, not all chambers in this process machine are in a working state. Or, the process machine of the current batch has a downtime phenomenon, which makes the time interval between the end of the current batch production and the end of the previous batch production of the process machine become larger, and so on. In the above situations, the data obtained to represent the time interval of the process machine is not when the process machine is under full load and stable operation. These situations will all affect the accuracy of the data obtained to represent the time interval of all process machines. In addition, the obtained data all carry information indicating which batch, which chambers of which process machine are in a working state, and which product is being produced and which process step is being carried out.

[0025] S102: Obtain the valid data in the data as valid data.

[0026] Among them, the step of obtaining the valid data in the data as valid data includes: based on the maximum processing amount, where the maximum processing amount is the maximum number of wafers that can be processed by the same type of process machine in any batch, retain the data corresponding to the number of wafers processed in the current batch being the maximum processing amount 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 can be produced in this batch, screen out the data representing the time interval between the end of the current batch production and the end of the previous batch production, and retain the remaining data as valid data to ensure that the valid data represents that the number of wafers produced in the current batch is the maximum number, which is beneficial to improving the accuracy of the obtained valid data.

[0028] It should be noted that the maximum number of wafers that can be processed by different types of process machines in any batch may be the same or different. The embodiments of the present application do not limit the maximum number of wafers that can be processed by the process machine in any batch.

[0029] S103: Obtain at least two initial data groups. The data in each initial data group is the valid data of the same type of process machine producing the same product and performing the same process step for each batch.

[0030] Due to the complex process flow and the dynamic and ever-changing production line conditions, different products may have the same process steps during production. For example, both products need to go through the etching process, and different products may require different types of process machines to cooperate in production. Therefore, the types of effective data obtained in step S102 are relatively complex, and the differences between two randomly selected effective data may be significant. By grouping the effective data in step S103, it is ensured that the same type of process machine produces the same product and the effective data of each batch are in the same initial data group when performing the same process step. This helps to narrow the differences between the effective data in the same initial data group and also facilitates the subsequent processing and analysis of the effective data in each initial data group, which is beneficial to improving the accuracy of the results generated after the subsequent analysis and evaluation of the effective data.

[0031] S104: Based on the initial data group, obtain a first data group, where the data in the first data group are the effective data corresponding to all chambers in the process machine in the initial data group being in the working state.

[0032] In this step, when a batch of process machines processes wafers, if not all chambers in the process machine are in the working state, the effective data of this batch are screened out, so that the remaining effective data are in the first data group, ensuring that the effective data in the first data group all represent that all chambers in the current batch of process machines are in the working state, and making the effective data in the first data group all obtained when the process machine is at full load, which is beneficial to further improving the accuracy of the remaining effective data.

[0033] S105: Sort the data in the first data group to form a first queue data group, and obtain the standard deviation of the data in the first queue data group.

[0034] Among them, the sorting process includes ascending sorting or descending sorting, which is convenient for subsequent processing of the effective data. Since the standard deviation represents the degree of dispersion of the effective data in the first queue data group, the larger the standard deviation, the greater the degree of dispersion of the effective data in the first queue data group. Therefore, the obtained standard deviation can be used as a basis for determining whether the difference between adjacent front and back data in the first queue data group is too large in the subsequent process.

[0035] S106: Based on the first queue data group and the standard deviation, obtain a second queue data group, which is formed by sorting the data remaining after removing the latter data in the front and back data when the difference between adjacent front and back data in the first queue data group deviates from the standard deviation.

[0036] Among them, obtaining the second queue data group includes the following steps:

[0037] Provide a first coefficient. The deviation of the difference between adjacent front and back data in the first queue data group from the standard deviation is represented by the following first relational expression:

[0038] |T NextLot -T PrevLot |>C0*σ

[0039] Wherein, T PrevLot represents the previous valid data corresponding to the front and back data in the first queue data group, and T NextLot represents the next valid data corresponding to the front and back data in the first queue data group. C0 represents the first coefficient, and σ represents the standard deviation;

[0040] Remove the next valid data in the front and back data corresponding to the first relational expression, and sort the remaining valid data to form a second queue data group.

[0041] The above first relational expression is used to determine whether the degree of change between adjacent front and back valid data in the first queue data group is within the allowable range. When the degree of change between adjacent front and back valid data is not within the allowable range, it indicates that the difference between the next valid data and other valid data among the adjacent front and back valid data is relatively large. This valid data can be regarded as inaccurate valid data, and the inaccurate valid data is screened out. For example, the data obtained when the process machine is in a downtime state is screened out, and the data obtained when the process machine is not operating stably due to other factors is screened out, so that the remaining valid data is in the second queue data group, which is beneficial to improving the accuracy of the valid data in the second queue data group and reducing the dispersion degree of the valid data in the second queue data group, thereby being beneficial to improving the accuracy of the results generated after subsequent analysis and evaluation of the valid data.

[0042] Wherein, the range of the first coefficient can be 0.001 to 0.05. When the dispersion degree of the valid data in the first queue data group is relatively large, the first coefficient can be selected to be relatively large, that is, allowing a relatively large degree of change between adjacent front and back valid data in the first queue data group. For example, the first coefficient can be 0.049 or 0.05; when the dispersion degree of the valid data in the first queue data group is relatively small, the first coefficient can be selected to be relatively small, that is, allowing a relatively small degree of change between adjacent front and back valid data in the first queue data group. For example, the first coefficient can be 0.001 or 0.002.

[0043] In other embodiments, the next data corresponding to the actual change amplitude of the front and back data in the first queue data group deviating from the average amplitude is represented by the following first relational expression:

[0044] |T NextLot -T PrevLot |≥C0*σ

[0045] S107: Obtain the minimum valid data and the maximum valid data based on the data in all the second queue data groups.

[0046] Among them, obtaining the minimum valid data and the maximum valid data includes the following three methods:

[0047] In some embodiments, referring to Figure 1 , the minimum valid data and the maximum valid data can be directly obtained from all the second queue data groups obtained in step S106. Since in steps S101 to S106, the data representing the time interval between the end of the current batch production and the end of the previous batch production obtained is screened multiple times, the valid data in the second queue data group not only represents that the number of wafers produced in the current batch is the maximum number, but also represents that all the chambers in the process machine of the current batch are in a working state, that is, it is ensured that the valid data in the second queue data group is obtained when the process machine is in a fully loaded state. In addition, it is also beneficial to ensure that the valid data in the second queue data group does not include the data obtained when the process machine is in a downtime state or not in a stable operation. Therefore, it is beneficial to improve the accuracy of the valid data in the second queue data group, thereby improving the accuracy of the obtained minimum valid data and maximum valid data, so as to improve the accuracy of the results generated after the subsequent analysis and evaluation of the valid data.

[0048] In other embodiments, referring to Figure 2 , after step 106: Obtain the second queue data group and before step 107: Obtain the minimum valid data and the maximum valid data, the evaluation method may further include the following steps:

[0049] S110: Obtain a first number and a second number. The first number is the number of valid data in the second queue data group, and the second number is the number of valid data in the first data group corresponding to the second queue data group.

[0050] S120: Set a first preset value and a second preset value according to the dispersion degree of the valid data in the first data group.

[0051] S130: Retain the second queue data groups 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.

[0052] Steps S110 to S130: Screen the obtained multiple second queue data groups, and retain the second queue data groups where the second number is greater than the first preset value, that is, ensure that the number of valid data in the first data group corresponding to the second queue data group is large enough, so that the subsequent processing of the first data group to obtain the second queue data group, and the second queue data group has reference value; further, retain the second queue data groups where the ratio of the first number to the second number is greater than the second preset value, that is, ensure that the second queue data group has a sufficient number of valid data relative to the corresponding first data group. When the number of valid data in the second queue data group is small compared to the number of valid data in the corresponding first data group, the significance of the second queue data group as a reference sample is small, and it will instead affect the accuracy of the minimum valid data and the maximum valid data obtained subsequently. Therefore, steps S110 to S130 are beneficial to improving the value of the retained second queue data group as a reference sample, so as to improve the accuracy of the minimum valid data and the maximum valid data obtained subsequently.

[0053] In some other embodiments, referring to Figure 3 , after step S104: Obtain the first data group, and before step S105: Sort the data in the first data group, the evaluation method may further include:

[0054] S140: Obtain the second number, where the second number is the number of valid data in the first data group; Set the first preset value according to the dispersion degree of the valid data in the first data group; Retain the first data groups where the second number is greater than the first preset value.

[0055] This step is beneficial to ensuring that the number of valid data in the first data group is large enough and suitable as a reference sample, so that the subsequent processing of the first data group to obtain the second queue data group, and the second queue data group has reference value. In addition, screening the first data group before step S105 is beneficial to reducing the number of the first data groups. Thus, when performing subsequent operations based on the first data group, fewer valid data can be processed, so as to reduce the time required for subsequent steps and improve the evaluation efficiency of the evaluation method.

[0056] After step S106: Obtain the second queue data group, and before step S107: Obtain the minimum valid data and the maximum valid data, the evaluation method may further include:

[0057] S150: Obtain the first number, where the first number is the number of valid data in the second queue data group; Set the second preset value according to the dispersion degree of the valid data in the first data group; Retain the second queue data groups where the ratio of the first number to the second number is greater than the second preset value.

[0058] This step helps ensure that the second queue data group has a sufficient number of valid data relative to the corresponding first data group. When the number of valid data in the second queue data group is smaller than that in the corresponding first data group, the significance of the second queue data group as a reference sample is limited, and it will instead affect the accuracy of the minimum and maximum valid data obtained subsequently. Therefore, screening out this part of the second queue data group helps improve the accuracy of the minimum and maximum valid data obtained subsequently.

[0059] It should be noted that in the above two embodiments of screening the second queue data group, the steps of obtaining the minimum valid data and the maximum valid data based on the data in all the second queue data groups are as follows: Obtain the minimum valid data and the maximum valid data from all the remaining second queue data groups.

[0060] Moreover, the range of the first preset value can be 10 to 100, and the range of the second preset value is 10% to 80%. Since both the first preset value and the second preset value are set according to the dispersion degree of the valid data in the first data group, when the dispersion degree of the valid data in the first data group is large, both the first preset value and the second preset value 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 degree of the valid data in the first data group is small, both the first preset value and the second preset value 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%.

[0061] In the above three embodiments, obtaining the minimum valid data and the maximum valid data both include the following two methods:

[0062] In some embodiments, obtaining the minimum valid data and the maximum valid data includes the following steps:

[0063] Obtain the maximum and minimum values of the valid data in each second queue data group. The minimum valid data is the smallest valid data among the multiple minimum values, and the maximum valid data is the largest valid data among the multiple maximum values.

[0064] In some other embodiments, obtaining the minimum valid data and the maximum valid data includes the following steps:

[0065] Obtain a first number and a second number. The first number is the number of valid data in the second queue data group, and the second number is the number of valid data in the first data group corresponding to the second queue data group.

[0066] Use the second queue data group with the largest ratio of the first number to the second number as the reference group, where 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.

[0067] S108: Obtain the production capacity of the semiconductor device based on the minimum valid data and the maximum valid data.

[0068] Among them, obtaining the production capacity of the semiconductor device includes the following steps:

[0069] Provide a second coefficient and a third coefficient;

[0070] Provide the maximum processing volume, which is the maximum number of wafers that can be processed by the same type of processing tool in any batch.

[0071] The production capacity of the semiconductor device is expressed by the following second relational expression:

[0072] WPH ∈ [Run Size / (M2 * Max), Run Size / (M1 * Min)]

[0073] Among them, WPH represents the production capacity of the semiconductor device, Run Size represents the maximum processing volume, M1 represents the second coefficient, M2 represents the third coefficient, Min represents the minimum valid data, and Max represents the maximum valid data.

[0074] Since in the above steps, the accuracy of the obtained minimum valid data and maximum valid data is relatively high, therefore, the accuracy of the boundary values of the production capacity of the semiconductor device obtained through the above second relational expression and the range composed of the boundary values is also relatively high.

[0075] Among them, the range of the second coefficient can be 0.999 - 1, and the range of the third coefficient can be 1 - 1.001. During the actual operation of the semiconductor device, due to the existence of many factors (such as the semiconductor device crashing or not being at full load), the obtained Takt Time will fluctuate greatly with the change of production batches, and this abnormal fluctuation data cannot reflect the normal level of the device. After screening the Takt Time through the above many steps, it is beneficial to improve the accuracy of the obtained minimum valid data and maximum valid data. In addition, adding the concepts of the second coefficient and the third coefficient in step S108 is beneficial to further improve the accuracy of the boundary values of the production capacity of the semiconductor device finally obtained and the range composed of the boundary values, thereby further improving the accuracy of the evaluated production capacity of the semiconductor device.

[0076] In summary, through the above evaluation method, the obtained Takt Time is screened at least three times, and the second queue data group composed of valid data is also screened, which is beneficial to improving the accuracy of the obtained minimum valid data and maximum valid data, so as to improve the accuracy of the subsequent evaluation of the production capacity of semiconductor equipment. In addition, the subsequent processing of the minimum valid data and maximum valid data to obtain the boundary value of the production capacity of semiconductor equipment and the range composed of the boundary values is beneficial to further improving the accuracy of the evaluated production capacity of semiconductor equipment, thereby improving the accuracy of the investment amount required for evaluating capacity expansion.

[0077] Another embodiment of the present application further provides a device for evaluating the production capacity of semiconductor equipment, which is used to implement the method for evaluating the production capacity of semiconductor equipment in the above embodiment. The following will describe in detail the device for evaluating the production capacity of semiconductor equipment provided by another embodiment of the present application with reference to the accompanying drawings. Figure 4 It is a schematic diagram of the functional modules of the device for evaluating the production capacity of semiconductor equipment provided by another embodiment of the present application.

[0078] Refer to Figure 4 , the device for evaluating the production capacity of semiconductor equipment includes: a data collection module 401, which is used to obtain the data of all process machines, and the data is the time interval between the end of the current batch production of the process machine and the end of the previous batch production of the current batch; a data processing module 402, which is used to process the data, and the data processing module 402 is configured to: obtain the valid data in the data as valid data; obtain at least two initial data groups, and the data in each initial data group is the valid data of each batch when the same type of process machines produce the same product and perform the same process step; based on the initial data groups, obtain a first data group, and the data in the first data group is the valid data corresponding to all chambers in the process machines in the initial data groups being in the working state; perform sorting processing on the data in the first data group to form a first queue data group; obtain the second queue data group as the data formed by sorting the remaining data after removing the latter data in the front and back data when the difference between the adjacent front and back data in the first queue data group deviates from the standard deviation; an obtaining module 403, which is used to obtain the minimum valid data and the maximum valid data based on the data in all the second queue data groups, and obtain the production capacity of the semiconductor equipment based on the minimum valid data and the maximum valid data.

[0079] Among them, the data processing module 402 includes: a validity filtering unit 412, configured to obtain valid data in the data as valid data; a grouping unit 422, configured to obtain at least two initial data groups, where the data in each initial data group is valid data of each batch when the same type of processing machine produces the same product and performs the same process step, and based on the initial data groups, obtain a first data group, where the data in the first data group is the valid data corresponding to all chambers in the processing machines in the initial data groups being in a working state; a sorting unit 432, configured to perform sorting processing on the data in the first data group to form a first queue data group; a first calculation unit 442, configured to obtain the average amplitude corresponding to adjacent valid data in the second queue data group; a screening unit 452, configured to obtain a second queue data group based on the first queue data group and the standard deviation, where the second queue data group is formed by sorting the remaining data after removing the latter data in the front and back data when the difference between adjacent front and back data in the first queue data group deviates from the standard deviation, that is, after removing the latter data in the front and back data, the remaining valid data in the first queue data group is sorted to form the second queue data group.

[0080] In some embodiments, the data processing module 402 includes a first storage unit 414 and a second storage unit 424. Among them, 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 calculation unit 442 and the screening unit 452 are all located in the second storage unit 424. The data processing module 402 further includes: a data transfer unit 462, configured to transfer the valid data in the first queue data group to the first calculation unit 442, that is, transfer the data finally stored in the first storage unit 414 to the second storage unit 424.

[0081] In other embodiments, the validity filtering unit, the grouping unit, the sorting unit, the first calculation unit, and the screening unit are all located in the same storage unit. Therefore, there is no need for a data transfer unit to perform data transmission between the sorting unit and the first calculation unit.

[0082] Among them, the obtaining module 403 includes: a coefficient configuration unit 413, configured to provide a second coefficient, a third coefficient, and a maximum processing amount, where the maximum processing amount is the maximum number of wafers allowed to be processed by the same type of processing machine in any batch; a second calculation unit 423, configured to calculate the production capacity of the semiconductor device according to the minimum valid data, the maximum valid data, the second coefficient, the third coefficient, and the maximum processing amount.

[0083] In summary, the device for evaluating the production capacity of semiconductor equipment can screen the obtained Takt Time at least three times, and also screen the second queue data group composed of valid data, which is beneficial to improving the accuracy of the obtained minimum valid data and maximum valid data, so as to improve the accuracy of the subsequent evaluation of the production capacity of semiconductor equipment. In addition, the subsequent processing of the minimum valid data and maximum valid data to obtain the boundary value of the production capacity of semiconductor equipment and the range composed of the boundary values is beneficial to further improving the accuracy of the evaluation of the production capacity of semiconductor equipment, thereby improving the accuracy of the investment amount required for evaluating capacity expansion.

[0084] Those of ordinary skill in the art can understand that the above embodiments are specific embodiments for implementing the present application. In actual applications, various changes can be made to them in form and details without departing from the spirit and scope of the present application. Any person skilled in the art can make their own changes and modifications without departing from the spirit and scope of the present application. Therefore, the protection scope of the present application should be subject to the scope defined by the claims.

Claims

1. A method for evaluating the production capacity of semiconductor devices, the semiconductor devices including different types of process machines, characterized in that, Including: Performing production processing on wafers of N batches using all the process machines, and obtaining data of all the process machines, where the data is the time interval between the end of production of the current batch and the end of production of the previous batch of the current batch by the process machines; Obtaining the valid data in the data as valid data; Obtaining at least two initial data groups, where the data in each initial data group is the valid data of each batch when the process machines of the same type produce the same product and perform the same process step; Based on the initial data groups, obtaining a first data group, where the data in the first data group is the valid data corresponding to all chambers in the process machines in the initial data groups being in a working state; Performing sorting processing on the data in the first data group to form a first queue data group, and obtaining the standard deviation of the data in the first queue data group; Based on the first queue data group and the standard deviation, obtaining a second queue data group, where the second queue data group is formed by sorting the remaining data after removing the latter data in the front and back data when the difference between adjacent front and back data in the first queue data group deviates from the standard deviation; Based on the data in all the second 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 production capacity of the semiconductor device.

2. The method according to claim 1, characterized in that, The step of obtaining the valid data in the data as the valid data includes: Based on the maximum processing amount, where the maximum processing amount is the maximum number of wafers allowed to be processed by the process machines of the same type in any batch, retaining the data corresponding to the number of wafers processed in the current batch being the maximum processing amount as the valid data.

3. The method according to claim 1, characterized in that, The step of obtaining the second queue data group includes: Providing a first coefficient, and the deviation of the difference between adjacent front and back data in the first queue data group from the standard deviation is represented by the following first relational expression: |T Next Lot -T Prev Lot |>C0*σ where T Prev Lot represents the previous valid data corresponding to the front and back data in the first queue data group, and T Next Lot represents the next valid data corresponding to the front and back data in the first queue data group, C0 represents the first coefficient, and σ represents the standard deviation; Removing the latter valid data in the front and back data that satisfies the first relational expression, and sorting the remaining valid data to form the second queue data group.

4. The method according to claim 3, characterized in that The range of the first coefficient is 0.001 - 0.

05.

5. The method according to claim 1, characterized in that, After obtaining the second queue data group and before obtaining the minimum valid data and the maximum valid data, it further includes: Obtaining a first number and a second number, where the first number is the number of valid data in the second queue data group, and the second number is the number of valid data in the first data group corresponding to the second queue data group; Setting a first preset value and a second preset value according to the degree of dispersion of the valid data in the first data group; Retaining the second 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.

6. The method according to claim 1, wherein After obtaining the first data group and before performing sorting processing on the data in the first data group, it further includes: Obtaining a second number, where the second number is the number of valid data in the first data group; Set a first preset value according to the dispersion degree of the valid data in the first data group; Retain the first data group in which the second number is greater than the first preset value; After obtaining the second queue data group, it further includes: Obtain a first number, where the first number is the number of valid data in the second queue data group; Set a second preset value according to the dispersion degree of the valid data in the first data group; Retain the second queue data group in which the ratio of the first number to the second number is greater than the second preset value.

7. The method according to claim 5 or 6, characterized in that, Based on the data in all the second queue data groups, the steps of obtaining the minimum valid data and the maximum valid data include: Obtain the minimum valid data and the maximum valid data from all the remaining second queue data groups.

8. The method according to claim 7, wherein The range of the first preset value is 10 to 100.

9. The method according to claim 7, wherein The range of the second preset value is 10% to 80%.

10. The method according to claim 1, wherein The steps of obtaining the minimum valid data and the maximum valid data include: Obtain the maximum value and the minimum value of the valid data in each second 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.

11. The method according to claim 1, wherein The steps of obtaining the minimum valid data and the maximum valid data include: Obtain a first number and a second number. The first number is the number of valid data in the second queue data group, and the second number is the number of valid data in the first data group corresponding to the second queue data group; Take the second queue data group with the largest ratio of the first number to the second number 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.

12. The method according to claim 10 or 11, characterized in that, Based on the minimum valid data and the maximum valid data, the steps of obtaining the production capacity of the semiconductor device include: Provide a second coefficient and a third coefficient; Provide a maximum processing amount, where the maximum processing amount is the maximum number of wafers that can be processed by the same type of processing machine in any batch; The production capacity of the semiconductor device is represented by the following second relational expression: WPH ∈ [Run Size / (M2 * Max), Run Size / (M1 * Min)] Where, WPH represents the production capacity of the semiconductor device, Run Size represents the maximum processing amount, M1 represents the second coefficient, M2 represents the third coefficient, Min represents the minimum valid data, and Max represents the maximum valid data.

13. The method according to claim 12, wherein The range of the second coefficient is 0.999 to 1, and the range of the third coefficient is 1 to 1.

001.

14. A device for evaluating the production capacity of semiconductor devices, characterized in that, Include: A data collection module for obtaining data of all processing machines. The data is the time interval between the end of the current batch production and the end of the previous batch production of the processing machine; A data processing module for processing the data. The data processing module is configured as: Obtain the valid data in the said data as the valid data; obtain at least two initial data groups, where the data in each said 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; based on the initial data groups, obtain a first data group, where the data in the first data group is the valid data corresponding to all chambers in the process machine in the initial data group being in a working state; perform sorting processing on the data in the first data group to form a first queue data group, and obtain the standard deviation of the data in the first queue data group; based on the first queue data group and the standard deviation, obtain a second queue data group, where the second queue data group is formed by sorting the remaining data after removing the latter data among the front and back data when the difference between adjacent front and back data in the first queue data group deviates from the standard deviation. An obtaining module, configured to obtain the minimum valid data and the maximum valid data based on the data in all the second queue data groups, and obtain the production capacity of the semiconductor device based on the minimum valid data and the maximum valid data.

15. The device according to claim 14, characterized in that, The said data processing module includes: An effectiveness filtering unit, configured to obtain the valid data in the said data as the valid data; A grouping unit, configured to obtain at least two of the said initial data groups, where 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 groups, obtain the first data group, where the data in the first data group is the valid data corresponding to all chambers in the process machine in the initial data group being in a working state; A sorting unit, configured to perform sorting processing on the data in the first data group to form the first queue data group; A first calculation unit, configured to obtain the standard deviation of the data in the first queue data group; A screening unit, configured to obtain the second queue data group based on the first queue data group and the standard deviation, where the second queue data group is formed by sorting the remaining data after removing the latter data among the front and back data when the difference between adjacent front and back data in the first queue data group deviates from the standard deviation.

16. The device according to claim 15, wherein, The said data processing module further includes: A data transfer unit, configured to transfer the valid data in the first queue data group to the first calculation unit.

17. The device according to claim 14 or 15, characterized in that, The said obtaining module includes: A coefficient configuration unit, configured to provide a second coefficient, a third coefficient, and a maximum processing amount, where the maximum processing amount is the maximum number of wafers that can be processed by the same type of process machine in any batch A second calculation unit, configured to calculate the production capacity of the semiconductor device according to the minimum valid data, the maximum valid data, the second coefficient, the third coefficient, and the maximum processing amount.

Images