Method for evaluating semiconductor device production capacity and apparatus therefor

Abstract

Embodiments of the present application relate to the field of semiconductor manufacturing, and provide a method for evaluating the production capacity of a semiconductor device and a device thereof. The method comprises: obtaining a time interval between the end of a current batch production and the end of a previous batch production of all process machines; obtaining valid data in the data as valid data; obtaining at least two initial data groups; sorting the data in the initial data groups to form a first queue data group, and obtaining an actual change rate between adjacent data in the first queue data group; providing a first coefficient, and marking part of the data in the first queue data group based on the actual change rate and the first coefficient to obtain a second queue data group; obtaining minimum valid data and maximum valid data based on the data in all second queue data groups, and obtaining the production capacity of the semiconductor device. Embodiments of the present application are beneficial to improving the accuracy of the evaluated production capacity of the semiconductor device.

Description

TECHNICAL FIELD

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

[0002] One of the tasks of capacity planning of a semiconductor factory is to plan the number of semiconductor equipment according to the capacity expansion requirement, and to 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, and 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 a semiconductor equipment is in a full load and stable operation state, for a certain determined semiconductor equipment and a certain determined process step, the number of wafers produced by the semiconductor equipment per unit time is used to measure the production capacity (WPH, wafer per hour) of the semiconductor equipment. The mathematical definition of WPH is: WPH = Run Size / Takt Time, wherein Run Size represents the maximum number of wafers allowed to be produced by a certain determined semiconductor equipment per batch, and Run Size is a fixed value when the semiconductor equipment is in a full load state, and Takt Time represents the time interval between the end of the current batch production and the end of the previous batch production of the process machine in the semiconductor equipment.

[0004] However, as the semiconductor equipment operates, the semiconductor equipment is not necessarily in a full load and stable operation state at each moment, and there is a difference between the Takt Time collected at different moments, so that there is a difference between the WPH at different moments, and these factors will all 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 the semiconductor equipment to improve the accuracy of the evaluated production capacity of the semiconductor equipment. SUMMARY

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

[0006] According to some embodiments of the present application, one aspect of the embodiments of the present application provides a method for evaluating production capacity of a semiconductor device, the semiconductor device comprising different types of process machines, the evaluation method comprising: using all the process machines to produce and process N batches of wafers, and obtaining data of all the process machines, the data being a time interval between an end of a current batch production of the process machine and an end of a previous batch production of the current batch; obtaining valid data in the data as valid data; obtaining at least two initial data groups, data in each of the initial data groups being the valid data of each batch when the same type of the process machine produces the same product and performs the same process step; sorting the data in the initial data groups to form a first queue data group, and obtaining an actual change rate between adjacent data in the first queue data group; providing a first coefficient, when the actual change rate of adjacent data in the first queue data group does not deviate from the first coefficient, marking an identification tag on a latter data in the adjacent data; obtaining a second queue data group, the second queue data group being formed by sorting the data in the first queue data group which are continuously marked with the identification tag; based on the data in all the second queue data groups, obtaining minimum valid data and maximum valid data; and 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, another aspect of the embodiments of the present application also provides a device for evaluating production capacity of a semiconductor device, comprising: a data collection module for obtaining data of all process machines, the data being a time interval between an end of a current batch production of the process machine and an end of a previous batch production of the current batch; a data processing module for processing the data, the data processing module being configured to: obtain valid data in the data as valid data; obtain at least two initial data groups, data in each of the initial data groups being the valid data of each batch when the same type of the process machine produces the same product and performs the same process step; sort the data in the initial data groups to form a first queue data group, and obtain an actual change rate between adjacent data in the first queue data group; provide a first coefficient, when the actual change rate of adjacent data in the first queue data group does not deviate from the first coefficient, mark an identification tag on a latter data in the adjacent data; obtain a second queue data group, the second queue data group being formed by sorting the data in the first queue data group which are continuously marked with the identification tag; and a obtaining module for obtaining minimum valid data and 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 scheme provided by the embodiment of the application has the following advantages:

[0009] In the above technical scheme, after obtaining the data of all process machines (the data is Takt Time, the time interval between the end of the current batch production and the end of the previous batch production of the current batch of a process machine), the valid data is first screened 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; then the data in the initial data group is sorted to obtain a first queue data group, and the actual change rate between adjacent front and rear data in the first queue data group is obtained, which is convenient for subsequent other processing of the valid data; a first coefficient is provided, and when the actual change rate between the front and rear data deviates from the first coefficient, a recognition tag is marked on the rear data in the front and rear data, and the data in the first queue data group which is continuously marked with the recognition tag is sorted to form a second queue data group. Therefore, the actual change rate between any adjacent front and rear data in the second queue data group is within the allowed range, that is, the difference between any adjacent front and rear data in the second queue data group is small, which is beneficial to improve the accuracy of the valid data in the second queue data group. Therefore, by screening the obtained data of all process machines at least twice, the accuracy of the valid data in the second queue data group formed finally is improved. Subsequently, based on the data in all second queue data groups, the minimum valid data and the maximum valid data are obtained, which is beneficial to improve the accuracy of the obtained minimum valid data and the maximum valid data when the production capacity of the semiconductor equipment is obtained, so as to improve the accuracy of the evaluated production capacity of the semiconductor equipment, thereby improving the accuracy of the investment amount required for evaluating the capacity expansion. BRIEF DESCRIPTION OF DRAWINGS

[0010] One or more embodiments are illustrated by way of example with reference to the drawings, which are not necessarily to scale. Unless otherwise specifically noted, the drawings are not intended to be to scale.

[0011] Figure 1 A specific flowchart of a method for evaluating the production capacity of a semiconductor equipment provided by an embodiment of the application;

[0012] Figure 2 Another specific flowchart of a method for evaluating the production capacity of a semiconductor equipment provided by an embodiment of the application;

[0013] Figure 3 Still another specific flowchart of a method for evaluating the production capacity of a semiconductor equipment provided by an embodiment of the application

[0014] Figure 4 A functional module schematic diagram of an apparatus for evaluating the production capacity of a semiconductor equipment provided by another embodiment of the application. DETAILED DESCRIPTION

[0015] From the background art, it is known that the accuracy of the current evaluation of the production capacity of semiconductor equipment needs to be improved.

[0016] It is found through analysis that, since the Run Size is a fixed value when the semiconductor equipment is in a full load state, the production capacity of the equipment is determined by the Takt Time when the semiconductor equipment is in a full load and stable operation state.

[0017] Since the process of semiconductor equipment is extremely precise, generally speaking, the Takt Time has two forms of expression: (1) the Takt Time is controlled by the clock inside the semiconductor equipment, and for a certain processing step, the Takt Time fluctuates by a small range with the increase of the processing batch; (2) the Takt Time is controlled by the advanced process control system inside the semiconductor equipment, and in the process of collecting the Takt Time, the Takt Time is constantly corrected through the feedback of the key parameters of the process, at this time, the Takt Time increases slowly with the increase of the processing batch.

[0018] Therefore, under the condition that the semiconductor equipment is in a full load and stable operation state, the Takt Time has a maximum value and a minimum value, that is, the Takt Time has a boundary value, and it is known that the normal production capacity of the semiconductor equipment also has a maximum value and a minimum value. In order to obtain the effective boundary of the Takt Time, the Takt Time needs to be handled correctly. At present, the normal distribution algorithm is mainly used to describe the Takt Time, and the concept of boundary value is not involved. However, due to the unique distribution characteristics of the Takt Time in semiconductor manufacturing, the normal distribution can only roughly describe the average level of the Takt Time, and the accuracy of the determined average level of the Takt Time is not high. Using the average level of the Takt Time to evaluate the production capacity of the semiconductor equipment will further reduce the accuracy, and the lower the accuracy of the evaluation of the production capacity of the semiconductor equipment, the greater the deviation of the investment amount required for the capacity planning. The investment amount required for the capacity planning of the semiconductor industry is generally at the level of tens of billions of US dollars, and even one percent of the deviation will lead to a difference of hundreds of millions of US dollars in investment amount.

[0019] The embodiment of the present application provides a method for evaluating semiconductor equipment production capacity and a device thereof. In the evaluation method, at least twice screening is performed on the data of all process machines to improve the accuracy of effective data in the finally formed second queue data group. Subsequently, the minimum effective data and the maximum effective data are obtained based on the data in all second queue data groups, and when further obtaining the semiconductor equipment production capacity, the accuracy of the obtained minimum effective data and the maximum effective data is improved, so that the accuracy of the evaluated semiconductor equipment production capacity is improved, and the accuracy of the investment amount required for capacity expansion is improved. In addition, due to the complexity of the semiconductor manufacturing process and the dynamic changes of the production line, the determination of the boundary value of Takt Time plays an important role, for example, the maximum value and the minimum value of the semiconductor equipment production capacity are determined by the boundary value of Takt Time, which provides a solid decision basis for the capacity planning of the semiconductor manufacturing factory. By improving the accuracy of the obtained boundary value of Takt Time, the accuracy of the maximum value and the minimum value of the determined investment amount is improved, which is beneficial to improving the decision efficiency on the one hand, and is beneficial to improving the accuracy of the boundary value of the investment amount required for 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 drawings. However, those skilled in the art can understand that, in the embodiments of the present application, many technical details are proposed in order to make the readers better understand the present application. However, the technical solutions claimed by the present application can be implemented even without these technical details and various changes and modifications based on the following embodiments.

[0021] The embodiment of the present application provides a method for evaluating semiconductor equipment production capacity, and the semiconductor structure provided by the embodiment of the present application will be described in detail below with reference to the drawings. Figure 1 A specific flowchart of the method for evaluating semiconductor equipment production capacity provided by the embodiment of the present application is provided. Figure 2 Another specific flowchart of the method for evaluating semiconductor equipment production capacity provided by the embodiment of the present application is provided. Figure 3 Still another specific flowchart of the method for evaluating semiconductor equipment production capacity provided by the embodiment of the present application is provided.

[0022] Reference Figures 1 to 3 A method for evaluating semiconductor equipment production capacity, the semiconductor equipment includes different types of process machines, and the evaluation method includes the following steps:

[0023] S101: using all process machines to produce N batches of wafers, and obtaining data of all process machines, the data being a time interval between an end of a current batch production of a process machine and an end of a previous batch production of the process machine.

[0024] It should be noted that, due to a complex process flow and dynamic changes of a production line, when all process machines produce wafers, the number of wafers produced by a batch in different process machines and / or different process steps is not the maximum number of wafers that can be produced by the batch, or the process machine of the current batch has a downtime phenomenon, so that 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 process machine becomes large, etc. In the above cases, the obtained data of the process machine representing the time interval are not in a full load and stable running state of the process machine, and these cases will affect the accuracy of the obtained data of all process machines representing the time interval. In addition, the obtained data carries information representing which batch, which process machine, which product and which process step.

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

[0026] In the step of obtaining valid data in the data as valid data, the step includes: based on a maximum processing amount, the maximum processing amount being a maximum number of wafers allowed to be processed in any batch for a same type of process machine, retaining data corresponding to a case that the number of wafers processed in the current batch is the maximum processing amount as valid data.

[0027] In the step, when the number of wafers produced in the current batch is not the maximum number of wafers that can be produced in the batch, the obtained data representing the time interval between the end of the current batch production and the end of the previous batch production of the process machine is filtered out, and the remaining data is retained as valid data, so as to ensure that the valid data represents the number of wafers produced in the current batch is the maximum number, and to improve the accuracy of the obtained valid data.

[0028] It should be noted that the maximum number of wafers allowed to be processed in any batch for different types of process machines can be the same or different, and the embodiments of the present application do not limit the maximum number of wafers allowed to be processed in any batch for the process machines.

[0029] S103: obtaining at least two initial data groups, and the data in each initial data group being valid data of batches when a same type of process machine produces a same product and performs a same process step.

[0030] Due to the complexity of the process flow and the dynamic and changeable status of the production line, different products may have the same process steps during production, for example, two products need to go through etching process, and different products may need different types of process machines to work together to produce, therefore, the effective data categories obtained in step S102 are more complicated, and the difference between two random effective data may be large, by step S103, the effective data is grouped, so that the effective data of each batch in the same initial data group is in the same initial data group when the same type of process machine produces the same product and performs the same process step, which is beneficial to reduce the difference between the effective data in the same initial data group, and also facilitates subsequent processing and analysis of the effective data in each initial data group, which is beneficial to improve the accuracy of the results generated after subsequent analysis and evaluation of the effective data.

[0031] In addition, due to the diversity of semiconductor devices, for some types of semiconductor devices, a process machine in a semiconductor device can have multiple chambers, when a batch of process machines processes wafers, not all chambers in the process machine can be in working condition, then the Takt Time of the batch of process machines does not represent the full load state, and the Takt Time obtained in this case will also affect the accuracy of the results generated after subsequent analysis and evaluation of the effective data.

[0032] Therefore, in some embodiments, the data of all process machines obtained can also carry information representing which chambers of which process machine are in working condition, after step S103, the effective data in the initial data group can be further judged and screened, when a batch of process machines processes wafers, not all chambers in the process machine are in working condition, the effective data of the batch is screened out, and the remaining effective data is reserved as the final initial data group, to ensure that the effective data in the final initial data group represents that all chambers in the current batch of process machines are in working condition, so that the effective data in the final initial data group is obtained when the process machine is in full load state, which is beneficial to further improve the accuracy of the remaining effective data.

[0033] S104: Sort the data in the initial data group to form a first queue data group, and obtain the actual change rate between adjacent data in the first queue data group.

[0034] In the first queue data group, the actual rate of change between adjacent preceding and following data is the ratio of the absolute value of the difference between adjacent preceding and following data to the preceding data in the preceding and following data. The magnitude of the actual rate of change is beneficial to reflecting the difference between adjacent preceding and following data; the larger the actual rate of change between adjacent preceding and following data, the greater the difference between adjacent preceding and following data. In this embodiment of the application, the smaller the difference between adjacent preceding and following data, the better the stability of the data group composed of valid data, which is beneficial to improving the accuracy of the results generated after subsequent analysis and evaluation based on the valid data in the data group.

[0035] S105: Provide a first coefficient, and when the actual rate of change of adjacent data in the first queue data group does not deviate from the first coefficient, mark the identification label on the next data in the adjacent data.

[0036] The actual rate of change of the data before and after in the first queue data group does not deviate from the first coefficient, as indicated by the following first relational expression:

[0037]

[0038] Among them, T Prev T represents the preceding valid data in the first queue data group, corresponding to the preceding and following data. Next C0 represents the next valid data in the first queue data group, corresponding to the previous and next data.

[0039] Mark the identification label on the second valid data in the data corresponding to the first relation.

[0040] In some embodiments, for valid data in the first queue data group, when adjacent data satisfy the above-described first relation, the next valid data in the preceding and following data is marked with 0; when adjacent data do not satisfy the above-described first relation, the next valid data in the preceding and following data is marked with 1. In other embodiments, only the next valid data in the preceding and following data that satisfies the above-described first relation may be marked. It should be noted that the above uses a label of 0 as an example; in actual applications, the specific form of the label is not limited in the embodiments of this application.

[0041] The first relationship is used to determine whether the change degree between the adjacent valid data in the first queue data group is within the allowable range. When the change degree between the 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, and the valid data can be regarded as inaccurate valid data, such as data collected when the process machine is in a downtime state, or data collected when the process machine is not in a stable operation state due to other factors. When the adjacent valid data satisfies the first relationship, it indicates that the change degree between the adjacent valid data is within the allowable range, and the valid data is marked, which is beneficial to improve the accuracy of the results generated after processing and evaluating the valid data.

[0042] The first coefficient can be in the range of 0.001 to 0.01. When the dispersion degree of the valid data in the first queue data group is large, the first coefficient can be selected to be larger, that is, the change degree between the adjacent valid data in the first queue data group is allowed to be larger. For example, the first coefficient can be 0.009 or 0.01. When the dispersion degree of the valid data in the first queue data group is small, the first coefficient can be selected to be smaller, that is, the change degree between the adjacent valid data in the first queue data group is allowed to be smaller. For example, the first coefficient can be 0.001 or 0.002.

[0043] In other embodiments, the actual change rate of the front and rear data in the first queue data group deviating from the first coefficient can be represented by the following first relationship:

[0044]

[0045] S106: Obtain a second queue data group, which is composed of the data sorted by the identification tags in the first queue data group.

[0046] On one hand, the step is beneficial to screen out inaccurate effective data in step S105, such as data acquired when the process machine is in a downtime state, and data acquired when the process machine is not in a stable operation state due to other factors, so that the remaining effective data is in the second queue data set, which is beneficial to improve the accuracy of the effective data in the second queue data set and reduce the dispersion degree of the effective data in the second queue data set; on the other hand, the data in the first queue data set that is continuously marked with the identification tag is sorted to form the second queue data set, that is, the same first queue data set can be divided into at least two second queue data sets, and the number of effective data in the first queue data set is very large, such as 10000, so that the data in the first queue data set that continuously meets the above first relationship is in the same second queue data set, which is beneficial to further screen and group the effective data in the first queue data set, thereby improving the accuracy of the results generated after subsequent processing and evaluation based on the effective data in the second queue data set.

[0047] S107: Based on the data in all second queue data sets, the minimum effective data and the maximum effective data are acquired.

[0048] Among them, acquiring the minimum effective data and the maximum effective data includes the following three ways:

[0049] In some embodiments, with reference to Figure 1 The minimum effective data and the maximum effective data can be directly acquired from all second queue data sets acquired in step S106. Since the data acquired to represent the time interval between the end of the current batch production and the end of the previous batch production in steps S101 to S106 is screened multiple times, the effective data in the second queue data set not only represents that the number of wafers produced in the current batch is the maximum number, but also represents that all chambers in the process machine are in a working state, that is, it is ensured that the effective data in the second queue data set is acquired when the process machine is in a full load state, and in addition, it is also beneficial to ensure that the effective data in the second queue data set does not include data acquired when the process machine is in a downtime state or is not in a stable operation state. Therefore, it is beneficial to improve the accuracy of the effective data in the second queue data set, thereby improving the accuracy of the acquired minimum effective data and the maximum effective data, and improving the accuracy of the results generated after subsequent analysis and evaluation of the effective data.

[0050] In other embodiments, with reference to Figure 2 After step 106: acquiring the second queue data set, before step 107: acquiring the minimum effective data and the maximum effective data, the evaluation method can further include the following steps:

[0051] S110: Obtain a first number and a second number, the first number being a number of valid data in the second queue data group, and the second number being a number of valid data in the initial data group corresponding to the second queue data group.

[0052] S120: Set a first preset value and a second preset value according to a discrete degree of the valid data in the initial data group.

[0053] S130: Reserve the second queue data group whose second number is greater than the first preset value and whose first number and second number have a ratio greater than the second preset value.

[0054] Steps S110 to S130 filter the obtained multiple second queue data groups, and reserve the second queue data group whose second number is greater than the first preset value, i.e., ensure that the number of valid data in the initial data group corresponding to the second queue data group is sufficient, so that after processing the initial data group to obtain the second queue data group, the second queue data group has reference value; further, reserve the second queue data group whose first number and second number have a ratio greater than the second preset value, i.e., ensure that the second queue data group has a sufficient number of valid data relative to the initial data group corresponding thereto, since the number of valid data in the second queue data group is smaller than the number of valid data in the initial data group corresponding thereto, the second queue data group has little significance as a reference sample, and instead affects the accuracy of the subsequently obtained minimum valid data and maximum valid data. Therefore, steps S110 to S130 are beneficial to improve the stability of the reserved second queue data group as a reference sample, so as to improve the accuracy of the subsequently obtained minimum valid data and maximum valid data.

[0055] In yet some embodiments, the reference Figure 3 After step S103: obtaining the initial data group, before step S104: sorting the data in the initial data group, the evaluation method can further include:

[0056] S140: Obtain the second number, the second number being a number of valid data in the initial data group; set the first preset value according to a discrete degree of the valid data in the initial data group; and reserve the initial data group whose second number is greater than the first preset value.

[0057] This step is beneficial to ensure that the number of valid data in the initial data group is sufficient and suitable as a reference sample, so that when the initial data group is subsequently processed to obtain the second queue data group, the second queue data group has reference value. In addition, filtering the initial data group before step S104 is beneficial to reduce the number of initial data groups, so that when subsequent operations are performed based on the initial data group, fewer valid data can be processed, thereby reducing the time required for subsequent steps and improving the evaluation efficiency of the evaluation method.

[0058] After the step S106 of obtaining the second queue data set, before the step S107 of obtaining the minimum valid data and the maximum valid data, the evaluation method can further comprise:

[0059] S150: obtaining a first number, the first number being a number of valid data in the second queue data set; setting a second preset value according to a dispersion degree of the valid data in the initial data set; and reserving the second queue data set whose ratio of the first number to the second number is greater than the second preset value.

[0060] This step is beneficial to ensure that the second queue data set has a sufficient number of valid data relative to the initial data set corresponding thereto. Since the number of valid data in the second queue data set is smaller than the number of valid data in the initial data set corresponding thereto, the second queue data set has little significance as a reference sample, and instead affects the accuracy of the minimum valid data and the maximum valid data obtained subsequently. Therefore, the second queue data set is screened out, which is beneficial to improve the accuracy of the minimum valid data and the maximum valid data obtained subsequently.

[0061] It should be noted that in the two embodiments of screening the second queue data set, the step of obtaining the minimum valid data and the maximum valid data based on the data in all the second queue data sets comprises: obtaining the minimum valid data and the maximum valid data from all the remaining second queue data sets.

[0062] Moreover, the first preset value can range from 10 to 100, and the second preset value can range from 10% to 80%. Since the first preset value and the second preset value are both set according to the dispersion degree of the valid data in the initial data set, when the dispersion degree of the valid data in the initial data set is large, the first preset value and the second preset value can both 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 initial data set is small, the first preset value and the second preset value can both 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%.

[0063] In the above three embodiments, the obtaining of the minimum valid data and the maximum valid data includes the following two ways:

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

[0065] The maximum value and the minimum value of the valid data in each second queue data set are obtained, the minimum valid data being the minimum valid data in the multiple minimum values, and the maximum valid data being the maximum valid data in the multiple maximum values.

[0066] In some embodiments, the obtaining the minimum valid data and the maximum valid data comprises the following steps:

[0067] obtaining a first number and a second number, the first number being a number of valid data in the second queue data group, and the second number being a number of valid data in the first initial data group corresponding to the second queue data group.

[0068] taking the second queue data group with the largest ratio of the first number to the second number as a reference group, the minimum valid data being a minimum value of valid data in the reference group, and the maximum valid data being a maximum value of valid data in the reference group.

[0069] S108: obtaining the semiconductor equipment production capacity based on the minimum valid data and the maximum valid data.

[0070] wherein the obtaining the semiconductor equipment production capacity comprises the following steps:

[0071] providing a second coefficient and a third coefficient;

[0072] providing a maximum processing amount, the maximum processing amount being a maximum number of wafers allowed to be processed in any batch by a same type of processing machine;

[0073] the semiconductor equipment production capacity is represented by the following second relationship:

[0074] WPH∈[Run Size / (M2*Max), Run Size / (M1*Min)]

[0075] wherein WPH represents the semiconductor equipment production capacity, 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.

[0076] Since the minimum valid data and the maximum valid data obtained in the above steps are of high accuracy, the boundary value of the semiconductor equipment production capacity obtained by the above second relationship and the range formed by the boundary value are also of high accuracy.

[0077] The second coefficient can range from 0.999 to 1, and the third coefficient can range from 1 to 1.001. During actual operation of the semiconductor equipment, due to the existence of many factors (such as equipment downtime or not in full load state), the Takt Time obtained will fluctuate greatly with the change of production batch, and such abnormal fluctuation data cannot reflect the normal level of the equipment. After the Takt Time is screened through the above steps, the accuracy of the minimum effective data and the maximum effective data obtained is improved. In addition, the concept of the second coefficient and the third coefficient is added in step S108, which is beneficial to further improve the accuracy of the boundary value of the semiconductor equipment production capacity obtained and the range constituted by the boundary value, thereby further improving the accuracy of the evaluated semiconductor equipment production capacity.

[0078] In summary, through the above evaluation method, the obtained Takt Time is screened at least twice, and the second queue data set composed of effective data is also screened, which is beneficial to improve the accuracy of the minimum effective data and the maximum effective data obtained, so as to improve the accuracy of the subsequent evaluation of the semiconductor equipment production capacity. In addition, the minimum effective data and the maximum effective data are processed to obtain the boundary value of the semiconductor equipment production capacity and the range constituted by the boundary value, which is beneficial to further improve the accuracy of the evaluated semiconductor equipment production capacity, thereby improving the accuracy of the investment amount required for the evaluation of production capacity expansion.

[0079] Another embodiment of the present application also provides a device for evaluating the production capacity of a semiconductor equipment, which is used to implement the method for evaluating the production capacity of a semiconductor equipment in the above embodiments. The device for evaluating the production capacity of a semiconductor equipment provided by another embodiment of the present application will be described in detail below with reference to the accompanying drawings. Figure 4 The functional module schematic diagram of the device for evaluating the production capacity of a semiconductor equipment provided by another embodiment of the present application.

[0080] Reference Figure 4The application discloses a device for evaluating semiconductor equipment production capacity, comprising: a data collection module 401 for acquiring data of all process machines, the data being a time interval between a current batch production end and a previous batch production end of the process machines; a data processing module 402 for processing the data, the data processing module 402 being configured to: acquire valid data in the data as valid data; acquire at least two initial data groups, data in each initial data group being valid data of each batch when process machines of the same type produce the same product and perform the same process step; perform sorting processing on the data in the initial data groups to form a first queue data group, and acquire an actual change rate between adjacent data in the first queue data group; provide a first coefficient, and when the actual change rate of the adjacent data in the first queue data group does not deviate from the first coefficient, mark an identification tag on the latter data in the adjacent data; acquire a second queue data group, the second queue data group being formed by sorting the data in the first queue data group which are continuously marked with the identification tag; and an acquisition module 403 for acquiring minimum valid data and maximum valid data based on the data in all second queue data groups, and acquiring the semiconductor equipment production capacity based on the minimum valid data and the maximum valid data.

[0081] The data processing module 402 comprises: an effectiveness filtering unit 412 for acquiring valid data in the data as valid data; a grouping unit 422 for acquiring at least two initial data groups, data in each initial data group being valid data of each batch when process machines of the same type produce the same product and perform the same process step; a sorting unit 432 for performing sorting processing on the data in the initial data groups to form a first queue data group; a first calculation unit 442 for acquiring an actual change rate between adjacent data in the first queue data group; a coefficient setting unit 452 for providing a first coefficient; a marking unit 462 for marking an identification tag on the latter data in the adjacent data when the actual change rate of the adjacent data in the first queue data group does not deviate from the first coefficient; and a screening unit 472 for acquiring a second queue data group, the second queue data group being formed by sorting the data in the first queue data group which are continuously marked with the identification tag.

[0082] In some embodiments, the data processing module 402 comprises 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 located in the first storage unit 414, and the first calculation unit 442, the coefficient setting unit 452, the marking unit 462 and the screening unit 472 are located in the second storage unit 424. The data processing module 402 further comprises a data transfer unit 482, configured to transfer the valid data in the first queue data group to the first calculation unit 442, i.e. to transfer the data finally stored in the first storage unit 414 to the second storage unit 424.

[0083] In other embodiments, the validity filtering unit, the grouping unit, the sorting unit, the first calculation unit and the screening unit are located in the same storage unit, so that no data transfer unit is needed between the sorting unit and the first calculation unit.

[0084] The acquisition module 403 comprises a coefficient configuration unit 413, configured to provide the second coefficient, the third coefficient and the maximum processing amount, wherein the maximum processing amount is the maximum number of wafers allowed to be processed in any batch by the same type of process machine; and a second calculation unit 423, configured to calculate the semiconductor equipment production capacity according to the minimum valid data, the maximum valid data, the second coefficient, the third coefficient and the maximum processing amount.

[0085] In summary, the device for evaluating the semiconductor equipment production capacity can screen the acquired Takt Time at least twice, and screen the second queue data group composed of valid data, which is conducive to improving the accuracy of the acquired minimum valid data and maximum valid data, so as to improve the accuracy of the subsequent evaluated semiconductor equipment production capacity. In addition, the subsequent processing of the minimum valid data and the maximum valid data to obtain the boundary value of the semiconductor equipment production capacity and the range composed of the boundary value is conducive to further improving the accuracy of the evaluated semiconductor equipment production capacity, so as to improve the accuracy of the investment amount required for the evaluation of the production capacity expansion.

[0086] Those skilled in the art can understand that the above-mentioned embodiments are specific embodiments for implementing the present application, and in actual application, various changes can be made in form and details without departing from the spirit and scope of the present application. Any person skilled in the art can make various changes and modifications without departing from the spirit and scope of the present application, therefore the protection scope of the present application should be limited by the scope defined in the claims.

Claims

1. A method for evaluating production capacity of a semiconductor facility, the semiconductor facility comprising different types of process tools, characterized by, The method comprises the following steps: obtaining data of N batches of wafers produced by all the process machines, and 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 machines; obtaining valid data in the data as the valid data; obtaining 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 machines produce the same product and perform the same process step; sorting the data in the initial data groups to form a first queue data group, and obtaining the actual change rate between adjacent data in the first queue data group; providing a first coefficient, and marking an identification tag on the latter data in the adjacent data in the first queue data group when the actual change rate of the adjacent data does not deviate from the first coefficient; obtaining a second queue data group, which is composed of the data in the first queue data group that are continuously marked with the identification tag; obtaining minimum valid data and maximum valid data based on the data in all the second queue data groups; obtaining the production capacity of semiconductor equipment based on the minimum valid data and the maximum valid data.

2. The method of claim 1, wherein, The step of obtaining the valid data in the data as the valid data comprises: reserving the data corresponding to the maximum processing amount as the valid data, wherein the maximum processing amount is the maximum number of wafers allowed to be processed in any batch by the same type of process machines.

3. The method of claim 1, wherein, The actual change rate of the adjacent data in the first queue data group is represented by the following first relationship: wherein T Prev Lot represents a previous said valid data corresponding to a previous data pair of said first queue data group, T Next Lot represents a next said valid data corresponding to a next data pair of said first queue data group, and C0 represents said first coefficient; The identification tag is marked on the latter valid data in the adjacent data corresponding to the first relationship.

4. The method of claim 3, wherein, The range of the first coefficient is 0.001-0.

01.

5. The method of claim 1, wherein, After obtaining the second queue data group, before obtaining the minimum valid data and the maximum valid data, the method further comprises the following steps: obtaining a first number and a second number, wherein the first number is the number of the valid data in the second queue data group, and the second number is the number of the valid data in the initial data group corresponding to the second queue data group; setting a first preset value and a second preset value according to the dispersion degree of the valid data in the initial data group; reserving the second queue data group when 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 of claim 1, wherein, After obtaining the initial data group, before sorting the data in the initial data group, the method further comprises the following steps: obtaining a second number, wherein the second number is the number of the valid data in the initial data group; setting a first preset value according to the dispersion degree of the valid data in the initial data group; reserving the initial data group when the second number is greater than the first preset value. After obtaining the second queue data group, the method further comprises the following steps: obtaining a first number, the first number being a number of the valid data in the second queue data set; setting a second preset value according to a discrete degree of the valid data in the initial data set; reserving the second queue data set whose ratio of the first number to the second number is greater than the second preset value.

7. The method of claim 5 or 6, wherein, The step of obtaining the minimum valid data and the maximum valid data based on data in all the second queue data sets comprises: obtaining the minimum valid data and the maximum valid data from all the remaining second queue data sets.

8. The method of claim 7, wherein, The first preset value ranges from 10 to 100.

9. The method of claim 7, wherein, The second preset value ranges from 10% to 80%.

10. The method of claim 1, wherein, The step of obtaining the minimum valid data and the maximum valid data comprises: obtaining a maximum value and a minimum value of the valid data in each second queue data set, the minimum valid data being the minimum valid data in a plurality of minimum values, and the maximum valid data being the maximum valid data in a plurality of maximum values.

11. The method of claim 1, wherein, The step of obtaining the minimum valid data and the maximum valid data comprises: obtaining a first number and a second number, the first number being a number of the valid data in the second queue data set, and the second number being a number of the valid data in the initial data set corresponding to the second queue data set; taking the second queue data set whose ratio of the first number to the second number is the largest as a reference set, the minimum valid data being a minimum value of the valid data in the reference set, and the maximum valid data being a maximum value of the valid data in the reference set.

12. The method of claim 10 or 11, wherein, The step of obtaining the semiconductor equipment production capacity based on the minimum valid data and the maximum valid data comprises: providing a second coefficient and a third coefficient; providing a maximum processing amount, the maximum processing amount being a maximum number of wafers allowed to be processed in any batch by the same type of process machine; The semiconductor equipment production capacity is represented by a second relationship as follows: WPH ∈ [Run Size / (M2*Max), Run Size / (M1*Min)] wherein WPH represents the semiconductor equipment production capacity, 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 of claim 12, wherein, 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, comprising: a data collection module configured to obtain data of all process machines, the data being a time interval between an end of a current batch production of the process machine and an end of a previous batch production of the current batch; a data processing module configured to process the data, the data processing module being configured to: The data processing module comprises: an effectiveness filtering unit configured to obtain valid data in the data as valid data; a grouping unit configured to obtain at least two initial data groups, each of the initial data groups comprising the valid data of each batch when a same type of the process machine produces a same product and performs a same process step; an ordering unit configured to order the data in the initial data groups to form a first queue data group; 15. The apparatus of claim 14, wherein, a first calculating unit configured to obtain an actual change rate between adjacent data in the first queue data group; a coefficient setting unit configured to provide a first coefficient; a marking unit configured to mark an identification tag on the latter data in the adjacent data in the first queue data group when the actual change rate does not deviate from the first coefficient; a screening unit configured to obtain a second queue data group, the second queue data group being formed by the data in the first queue data group that are successively marked with the identification tag. The data processing module further comprises: a data transfer unit configured to transfer the valid data in the first queue data group to the first calculating unit. The obtaining module comprises: a coefficient configuration unit configured to provide a second coefficient, a third coefficient, and a maximum processing amount, the maximum processing amount being a maximum number of wafers allowed to be processed in any batch for a same type of the process machine; 16. The apparatus of claim 15, wherein, a second calculating unit configured to calculate the semiconductor equipment production capacity according to the minimum valid data, the maximum valid data, the second coefficient, the third coefficient, and the maximum processing amount. ​ 17. The apparatus of claim 14 or 15, wherein, ​ ​ ​

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