A simulation method, device, equipment and medium for semiconductor production

Abstract

The present invention discloses a simulation method, device, equipment and medium for semiconductor production, aiming to improve the accuracy of semiconductor simulation. First, obtain the historical production records of wafers in different batches within a preset time period. According to the historical production records, calculate the standard deviation and the average value of the waiting time of each production equipment. Conduct cluster analysis based on the standard deviation and the average value of the waiting time of multiple production equipment. Determine at least one target production equipment according to the clustering result, and use at least one target production equipment to simulate semiconductor production. Since the target production equipment is obtained through cluster analysis based on the waiting time of each production equipment for different batches of wafers, the waiting times of the target equipment are similar. Using equipment with similar waiting times as the simulation equipment can improve the accuracy of the simulation.

Description

Technical Field

[0001] The present invention relates to the field of semiconductor manufacturing technology, and particularly to a simulation method, device, equipment and medium for semiconductor production. Background Art

[0002] A semiconductor production line is a manufacturing system with a large number of processing equipment and complex technological processes. On the same production line, there can be more than a dozen types of products being processed simultaneously. At the same time, according to different orders, combinations of demands of different types, quantities and urgencies will be generated on the production line, making the production process highly uncertain. In order to improve the production efficiency of semiconductors, usually before production, the production of semiconductors is simulated.

[0003] The simulation of semiconductor production mainly uses the queuing principle to simulate the production capacity of production equipment, generate the bottleneck effect of production equipment, and thus obtain the production sequence of production equipment during semiconductor production.

[0004] The simulation of semiconductor production is related to the production efficiency of semiconductor production. Therefore, how to improve the accuracy of semiconductor production simulation is an urgent problem for those skilled in the art. Summary of the Invention

[0005] The present invention provides a simulation method, device, equipment and medium for semiconductor production to improve the accuracy of semiconductor simulation.

[0006] In a first aspect, an embodiment of the present invention provides a simulation method for semiconductor production, the method includes:

[0007] Obtain the historical production records of different batches of wafers within a preset time period;

[0008] According to the historical production records, calculate the standard deviation and the average value of the waiting time of each production equipment;

[0009] Based on the standard deviation and the average value of the waiting time of multiple production equipments, perform clustering analysis, and determine at least one target production equipment according to the clustering result;

[0010] Use at least one of the target production equipments to simulate semiconductor production.

[0011] In a possible implementation manner, the calculating the standard deviation and the average value of the waiting time of each production equipment according to the historical production records includes:

[0012] According to the historical production records, calculate the waiting time of each batch of wafers on each production equipment;

[0013] Calculate the standard deviation and the average value of the waiting time of each production device according to the waiting time of each batch of wafers in each production device.

[0014] In a possible implementation, the clustering analysis of the standard deviation and the average value of the waiting time of multiple production devices, and determining at least one target production device according to the clustering result includes:

[0015] Perform two-cluster analysis on the standard deviation and the average value of the waiting time of multiple production devices, and select multiple production devices with a relatively strong stability from the clustering result for the standard deviation and the average value of the waiting time;

[0016] Use at least one production device among the multiple production devices corresponding to the selected group of standard deviation and average value of the waiting time as the target production device.

[0017] In a possible implementation, before simulating semiconductor production using at least one of the target devices, it further includes:

[0018] Determine that the standard deviation of the waiting time of at least one of the target devices is less than a first preset threshold; and / or

[0019] Determine that the average value of the waiting time of at least one of the target devices is less than a second preset threshold.

[0020] In a possible implementation, if there are multiple target production devices, then simulating semiconductor production using at least one of the target production devices includes:

[0021] Determine the production order of using the multiple target devices according to the preset step codes corresponding to the multiple target devices;

[0022] Simulate semiconductor production according to the production order of the multiple target devices.

[0023] In a possible implementation, the obtaining the historical production records of different batches of wafers within a preset time period includes:

[0024] Obtain the arrival time of different batches of wafers at the production device and the processing time by the production device within a preset time period.

[0025] In a possible implementation, determine the waiting time by the following method:

[0026] Use the difference between the processing time by the production device and the arrival time at the production device as the waiting time of the production device.

[0027] Second aspect, an embodiment of the present invention provides a simulation device for semiconductor production, the device comprising:

[0028] An acquisition module, configured to acquire historical production records of wafers of different batches within a preset duration;

[0029] A calculation module, configured to calculate the standard deviation and the average value of the waiting duration of each production device according to the historical production records;

[0030] A clustering module, configured to perform clustering analysis on the standard deviation and the average value of the waiting duration of multiple production devices, and determine at least one target production device according to the clustering result;

[0031] A simulation module, configured to simulate semiconductor production using at least one of the target production devices.

[0032] In a possible implementation manner, the calculation module is specifically configured to:

[0033] Calculate the waiting duration of each batch of wafers on each production device according to the historical production records;

[0034] Calculate the standard deviation and the average value of the waiting duration of each production device according to the waiting duration of each batch of wafers on each production device.

[0035] In a possible implementation manner, the clustering module is specifically configured to:

[0036] Perform binary clustering analysis on the standard deviation and the average value of the waiting duration of multiple production devices, and select a group of production devices with relatively strong stability from the clustering results, where the group has the standard deviation and the average value of the waiting duration;

[0037] Use at least one production device in the selected group of production devices corresponding to the standard deviation and the average value of the waiting duration as the target production device.

[0038] In a possible implementation manner, the device further comprises a determination module;

[0039] The determination module is configured to determine that the standard deviation of the waiting duration of at least one of the target devices is less than a first preset threshold; and / or

[0040] Determine that the average value of the waiting duration of at least one of the target devices is less than a second preset threshold.

[0041] In a possible implementation manner, if there are multiple target devices, the simulation module is specifically configured to:

[0042] Determine the production order of using the multiple target devices according to the preset step codes corresponding to the multiple target devices;

[0043] Simulate semiconductor production according to the production order of the multiple target devices.

[0044] In a possible implementation manner, the obtaining module is specifically configured to:

[0045] Obtain the arrival time of wafers in different batches at the production equipment and the processing time by the production equipment within a preset time period.

[0046] In a possible implementation manner, the calculation block is specifically configured to:

[0047] Use the difference between the processing time by the production equipment and the arrival time at the production equipment as the waiting time of the production equipment.

[0048] In a third aspect, an embodiment of the present invention provides an electronic device, including: a processor; a memory for storing processor-executable instructions; wherein, the processor implements the steps of the method according to any one of the first aspect by running the executable instructions.

[0049] In a fourth aspect, an embodiment of the present invention provides a computer-readable storage medium, on which computer instructions are stored, and when the instructions are executed by a processor, the steps of the method according to any one of the first aspect are implemented.

[0050] The beneficial effects of the present invention are as follows:

[0051] The present invention first obtains the historical production records of wafers in different batches within a preset time period, calculates the standard deviation of the waiting time and the average value of the waiting time of each production equipment according to the historical production records, performs clustering analysis based on the standard deviation of the waiting time and the average value of the waiting time of multiple production equipment, determines at least one target production equipment according to the clustering result, and uses at least one target production equipment to simulate semiconductor production. Since the target production equipment is obtained by clustering analysis according to the waiting time of each production equipment of different batches of wafers, the waiting time of the target equipment is similar. Using equipment with similar waiting time as the simulation equipment can improve the accuracy of the simulation. BRIEF DESCRIPTION OF THE DRAWINGS

[0052] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following will briefly introduce the drawings required for description in the embodiments. Obviously, the following drawings are only some embodiments of the present invention. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0053] Figure 1Schematic diagram of the application scenario of the embodiment of the present invention;

[0054] Figure 2 Schematic flow chart of a semiconductor production simulation method provided by an embodiment of the present invention;

[0055] Figure 3 Schematic diagram of a two-clustering result provided by an embodiment of the present invention;

[0056] Figure 4 Schematic diagram of the structure of a semiconductor production simulation device provided by an embodiment of the present invention;

[0057] Figure 5 Schematic diagram of the structure of an electronic device provided by an embodiment of the present invention;

[0058] Figure 6 Structural diagram of a semiconductor production simulation storage medium provided by an embodiment of the present invention. Detailed implementation manners

[0059] In order to make the objectives, technical solutions and advantages of the present invention clearer, the present invention will be further described in detail below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0060] In the embodiments of the present disclosure, the term "lot" refers to, in the semiconductor processing process, using a carrier (SMIF POD) to load products, with multiple pieces (for example, 25 pieces) in one box, which is defined as a batch.

[0061] The application scenarios described in the embodiments of the present disclosure are for more clearly explaining the technical solutions of the embodiments of the present disclosure, and do not constitute a limitation to the technical solutions provided by the embodiments of the present disclosure. Those of ordinary skill in the art can know that with the emergence of new application scenarios, the technical solutions provided by the embodiments of the present disclosure are equally applicable to similar technical problems. Among them, in the description of the present disclosure, unless otherwise specified, the meaning of "multiple" is two or more.

[0062] Semiconductor production is one of the most complex manufacturing systems at present, with characteristics such as uncertainty and re-entrance, and is a typical discrete event dynamic system. In actual production, simulation methods are usually used to select appropriate scheduling rules for semiconductor production lines. Therefore, how to improve the accuracy of semiconductor production simulation is a problem that needs to be solved currently.

[0063] To solve the above problems, the present invention provides a semiconductor production simulation method, device, equipment and medium to achieve the purpose of improving the accuracy of semiconductor production simulation.

[0064] First, refer to Figure 1 , which is a schematic diagram of the application scenario of the embodiment of the present invention. The production equipment 10 is communicatively connected to the network server 11 through a network, and the network can be a local area network, a wide area network, etc. During the entire production process, each production equipment 10 provides the historical production records of each lot of wafers in the production equipment to the network server.

[0065] In the embodiment of the present invention, the network server 11 obtains the historical production records of different lots of wafers provided by the production equipment 10; according to the historical production records, calculates the standard deviation and the average value of the waiting time of each batch of wafers in each production equipment; performs clustering analysis based on the standard deviation and the average value of the waiting time of multiple production equipment, and determines at least one target production equipment according to the clustering result; uses at least one target production equipment to simulate semiconductor production.

[0066] The present invention will be described in detail below through specific embodiments.

[0067] As Figure 2 shown, a semiconductor production simulation method provided by an embodiment of the present invention includes the following steps:

[0068] S201. Obtain the historical production records of different batches of wafers within a preset time period;

[0069] S202. According to the historical production records, calculate the standard deviation and the average value of the waiting time of each production equipment;

[0070] S203. Perform clustering analysis based on the standard deviation and the average value of the waiting time of multiple production equipment, and determine at least one target production equipment according to the clustering result;

[0071] S204. Use at least one target production equipment to simulate semiconductor production.

[0072] A semiconductor production simulation method provided by an embodiment of the present invention first obtains the historical production records of different batches of wafers within a preset time period, calculates the standard deviation and the average value of the waiting time of each production equipment according to the historical production records, performs clustering analysis based on the standard deviation and the average value of the waiting time of multiple production equipment, determines at least one target production equipment according to the clustering result, and uses at least one target production equipment to simulate semiconductor production. Since the target production equipment is obtained by performing clustering analysis on the waiting time of each production equipment for different batches of wafers, the waiting time of the target equipment is similar. Using equipment with similar waiting times as the simulation equipment can improve the accuracy of the simulation.

[0073] The preset duration in the embodiments of the present invention can be determined according to actual needs or manual experiments, for example, it can be 90 days.

[0074] In a specific implementation, to obtain the historical production records of different lot wafers within the preset duration, the arrival time of different lot wafers at the production equipment and the processing time by the production equipment within the preset duration can be obtained, and then the difference between the processing time by the production equipment and the arrival time at the production equipment is used as the waiting duration of the production equipment.

[0075] For example, if the arrival time of lot1 wafer at production equipment a is 10:01 and the processing time by production equipment a is 10:02, then the waiting duration of lot1 wafer at equipment a is 1 min (minute).

[0076] The following uses a specific embodiment to illustrate the waiting duration. There are a total of 4 devices, namely device A, device B, device C, and device D, and a total of 3 lot wafers. The waiting duration of each batch of wafers at each device within the preset duration is shown in Table 1 below.

[0077] Lot1 Wafer Lot2 Wafer Lot3 Wafer Equipment A 256 min 334 min 397 min Equipment B 342 min 215 min 285 min Equipment C 517 min 178 min 412 min Equipment D 712 min 412 min 396 min

[0078] Table 1

[0079] From the above table, calculate the standard deviation and the average value of the waiting duration of each production equipment. Taking the above table as an example, calculate the standard deviation and the average value of the waiting duration of devices A, B, C, and D.

[0080] After obtaining the standard deviation and the average value of the waiting duration of multiple production equipment, perform clustering analysis on the standard deviation and the average value of the waiting duration of multiple devices. For example, perform binary clustering, and determine at least one target production equipment according to the clustering result.

[0081] Among them, the specific number of clusters for clustering is not limited in the embodiments of the present invention. It can be binary clustering, or ternary clustering, quaternary clustering, etc.

[0082] The following uses binary clustering as an example for illustration.

[0083] Based on the standard deviation and the average value of the waiting duration of multiple production equipment, perform binary clustering analysis, select a group of multiple production equipment with relatively strong stability from the clustering result, and use at least one production equipment among the selected multiple production equipment as the target production equipment.

[0084] In the embodiments of the present invention, the k-means clustering method can be used, where k is the number of finally aggregated clusters.

[0085] In a specific implementation, two-cluster analysis is performed based on the standard deviation and the average value of the waiting durations of multiple production devices. The standard deviation of the waiting duration can be used as the abscissa, and the average value of the waiting duration can be used as the ordinate. First, randomly select the coordinates corresponding to two production devices. Using the selected two coordinates as the initial center points of the two-cluster, for the coordinates corresponding to each production device, calculate the distances between each coordinate and the two initial centers. Group the coordinates according to the distances between the coordinates and the two initial center points, and assign the coordinate to the group with the minimum distance from the initial center point. After grouping, take the center point within the group, that is, the average of all abscissas and the center of the ordinate, to determine two new center points. Then, for the coordinates corresponding to each production device, calculate the distances between each coordinate and the two newly confirmed center points. Group the coordinates according to the calculated distances. After grouping, use the same method as above to confirm two new center points again. Repeat the above steps until the group to which each coordinate belongs remains unchanged.

[0086] As Figure 3 shown, it is a schematic diagram of the two-cluster result. As can be seen from Figure 3 it, the production devices within the small dashed box in the lower left corner are clustered with center point 2 as the center, and the production devices within the large dashed box are clustered with center point 1 as the center. As can be seen from Figure 3 it, the production devices in the small dashed box are more concentrated, so they are production devices with stronger stability, and the production devices in the large dashed box are more dispersed, so they are production devices with poorer stability.

[0087] It should be noted that the above description is based on two-cluster. In a specific implementation, it can also be three-cluster, four-cluster, etc. If it is three-cluster, select at least one production device from the production devices with the strongest stability as the target production device. It is also possible to first select any one of the two groups of production devices with stronger stability, and then select at least one production device from the production devices in any one of the groups as the target production device.

[0088] In the implementation, after selecting multiple production devices corresponding to a group with stronger stability of the standard deviation and the average value of the waiting duration, at least one production device can be randomly selected from the multiple production devices as the target production device, or it can also be selected according to a preset rule. For example, select the standard deviation of the waiting duration less than the first preset threshold, and / or select the average value of the waiting duration less than the second preset threshold.

[0089] Selecting the standard deviation of the waiting duration less than the first preset threshold, and / or selecting the average value of the waiting duration less than the second preset threshold is to ensure that there is no huge fluctuation in the waiting duration of the target production device, thereby improving production efficiency.

[0090] The first preset threshold and the second preset threshold can be determined according to the actual experience of those skilled in the art. For example, the first preset threshold can be 120 min, and the second preset threshold can be 450 min.

[0091] In one embodiment, if there are multiple target production devices, when using the target production devices to simulate semiconductor production, the production order of using the multiple target production devices can be determined according to the preset step codes corresponding to the multiple target production devices, and then semiconductor production can be simulated according to the production order of the multiple target production devices.

[0092] In practice, the step codes will be stored in each production device, that is, the steps that the production device can perform. For example, the step codes of device A can be step 1, step 3, and step 12; the step codes of device B can be step 2, step 3, step 11, and step 24.

[0093] When using the target device to simulate semiconductor production, for the feasibility and continuity of the simulation production, the production order can be set for the target device according to the step codes, and finally the production simulation can be performed according to this production order. The average value of the simulation waiting duration obtained by simulating the semiconductor production using the semiconductor production simulation method provided in the embodiments of the present invention and the average value of the actual waiting duration during actual production according to this simulation order. Using the semiconductor production simulation method of the embodiments of the present invention, the simulation reduction degree reaches more than 80%.

[0094] Based on the same inventive concept, the embodiments of the present disclosure also provide a semiconductor production simulation device. Since this device is the device in the method of the embodiments of the present disclosure, and the principle of this device for solving problems is similar to that of this method, the implementation of this device can refer to the implementation of the method, and the repeated parts will not be elaborated.

[0095] As Figure 4 shown, the above device includes the following modules:

[0096] An acquisition module 401, configured to acquire the historical production records of different batches of wafers within a preset duration;

[0097] A calculation module 402, configured to calculate the standard deviation and the average value of the waiting duration of each production device according to the historical production records;

[0098] A clustering module 403, configured to perform clustering analysis on the standard deviation and the average value of the waiting duration of multiple production devices, and determine at least one target production device according to the clustering result;

[0099] A simulation module 404, configured to use at least one target production device to simulate semiconductor production.

[0100] As an alternative implementation, the calculation module is specifically configured to:

[0101] Calculate the waiting time of each batch of wafers on each production device according to the historical production records;

[0102] Calculate the standard deviation and the average value of the waiting time of each production device according to the waiting time of each batch of wafers on each production device.

[0103] As an alternative implementation, the clustering module is specifically configured to:

[0104] Perform two-cluster analysis on the standard deviation and the average value of the waiting time of multiple production devices, and select multiple production devices with a relatively strong stability from the clustering results for the standard deviation and the average value of the waiting time;

[0105] Use at least one production device among the multiple production devices corresponding to the selected group of standard deviation and average value of the waiting time as the target production device.

[0106] As an alternative implementation, the device further includes a determination module;

[0107] The determination module is configured to determine that the standard deviation of the waiting time of at least one of the target devices is less than a first preset threshold; and / or

[0108] Determine that the average value of the waiting time of at least one of the target devices is less than a second preset threshold.

[0109] As an alternative implementation, if there are multiple target devices, the simulation module is specifically configured to:

[0110] Determine the production order of using the multiple target devices according to the preset step codes corresponding to the multiple target devices;

[0111] Simulate semiconductor production according to the production order of the multiple target devices.

[0112] As an alternative implementation, the acquisition module is specifically configured to:

[0113] Acquire the arrival time of wafers in different batches at the production device and the processing time by the production device within a preset duration.

[0114] As an alternative implementation, the calculation block is specifically configured to:

[0115] Use the difference between the processing time by the production device and the arrival time at the production device as the waiting time of the production device.

[0116] Based on the same inventive concept, an electronic device for semiconductor production is also provided in the embodiments of the present disclosure. Since this electronic device is the same as the one in the method of the embodiments of the present disclosure, and the principle of this electronic device to solve problems is similar to that of the method, the implementation of this electronic device can refer to the implementation of the method, and the repeated parts will not be elaborated.

[0117] The following will describe the electronic device 50 according to this embodiment of the present disclosure with reference to Figure 5 to describe the electronic device 50 according to this embodiment of the present disclosure. Figure 5 The shown electronic device 50 is only an example, and should not bring any limitations to the functions and usage scopes of the embodiments of the present disclosure.

[0118] As Figure 5 shown, the electronic device 50 can be presented in the form of a general computing device. For example, it can be a terminal device. The components of the electronic device 50 may include but are not limited to: at least one of the above-mentioned processors 51, at least one of the above-mentioned memories 52 for storing processor-executable instructions, and a bus 53 connecting different system components (including the memory 52 and the processor 51), and the processor is the processor of the intelligent airport device.

[0119] The processor realizes the following steps by running the executable instructions:

[0120] Obtain the historical production records of wafers in different batches within a preset time period;

[0121] According to the historical production records, calculate the standard deviation and the average value of the waiting time of each production device;

[0122] Based on the standard deviation and the average value of the waiting time of multiple production devices, perform clustering analysis, and determine at least one target production device according to the clustering result;

[0123] Use at least one of the target production devices to simulate semiconductor production.

[0124] As an optional implementation manner, the calculating the standard deviation and the average value of the waiting time of each production device according to the historical production records includes:

[0125] According to the historical production records, calculate the waiting time of each batch of wafers on each production device;

[0126] According to the waiting time of each batch of wafers on each production device, calculate the standard deviation and the average value of the waiting time of each production device.

[0127] As an alternative implementation, performing a clustering analysis on the standard deviation of the waiting durations of multiple production devices and the average value of the waiting durations, and determining at least one target production device according to the clustering result, includes:

[0128] Performing a two-clustering analysis on the standard deviation of the waiting durations of multiple production devices and the average value of the waiting durations, and selecting, from the clustering result, multiple production devices with a relatively strong stability in the standard deviation of the waiting durations and the average value of the waiting durations;

[0129] Regarding at least one production device among the multiple production devices corresponding to the selected group of the standard deviation of the waiting durations and the average value of the waiting durations as the target production device.

[0130] As an alternative implementation, before simulating semiconductor production using at least one of the target devices, it further includes:

[0131] Determining that the standard deviation of the waiting duration of at least one of the target devices is less than a first preset threshold; and / or

[0132] Determining that the average value of the waiting duration of at least one of the target devices is less than a second preset threshold.

[0133] As an alternative implementation, if there are multiple target production devices, then simulating semiconductor production using at least one of the target production devices includes:

[0134] Determining the production order of using the multiple target devices according to the preset step codes corresponding to the multiple target devices;

[0135] Simulating semiconductor production according to the production order of the multiple target devices.

[0136] As an alternative implementation, obtaining the historical production records of wafers in different batches within a preset duration includes:

[0137] Obtaining the arrival time of wafers in different batches at the production device and the processing time by the production device within a preset duration.

[0138] As an alternative implementation, determining the waiting duration by the following method:

[0139] Taking the difference between the processing time by the production device and the arrival time at the production device as the waiting duration of the production device.

[0140] Bus 53 represents one or more of several types of bus structures, including a memory bus or a memory controller, a peripheral bus, a processor, or a local bus using any bus structure among multiple bus structures.

[0141] The memory 52 may include a readable medium in the form of volatile memory, such as random access memory (RAM) 521 and / or cache memory 522, and may further include read-only memory (ROM) 523.

[0142] The memory 52 may also include a program / utility 525 having a set (at least one) of program modules 524. Such program modules 524 include, but are not limited to: an operating system, one or more application programs, other program modules, and program data. Each or some combination of these examples may include an implementation of a network environment.

[0143] The electronic device 50 may also communicate with one or more external devices 54 (such as a keyboard, a pointing device, etc.), may also communicate with one or more devices that enable a user to interact with the electronic device 50, and / or communicate with any device that enables the electronic device 50 to communicate with one or more other computing devices (such as a router, a modem, etc.). Such communication may be carried out through an input / output (I / O) interface 55. Moreover, the electronic device 50 may also communicate with one or more networks (such as a local area network (LAN), a wide area network (WAN), and / or a public network, such as the Internet) through a network adapter 56. As shown in the figure, the network adapter 56 communicates with other modules of the electronic device 50 through a bus 53. It should be understood that, although not shown in the figure, other hardware and / or software modules may be used in conjunction with the electronic device 50, including but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data backup storage systems, etc.

[0144] In some possible implementation manners, various aspects of the present disclosure may also be implemented in the form of a storage medium, which includes program code. When the storage medium runs on a terminal device, the program code is used to cause the terminal device to execute the steps of each module in the resource treatment analysis device according to various exemplary embodiments of the present disclosure described in the above "Exemplary Method" section of this specification. For example, the terminal device may be used to obtain the historical production records of different batches of wafers within a preset time period; calculate the standard deviation and the average value of the waiting time of each production device according to the historical production records; perform clustering analysis based on the standard deviation and the average value of the waiting time of multiple production devices, and determine at least one target production device according to the clustering result; use at least one target production device to simulate semiconductor production.

[0145] The storage medium may adopt any combination of one or more readable media. The readable media may be a readable signal medium or a readable storage medium. The readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the above. More specific examples (non-exhaustive list) of the readable storage medium include: an electrical connection having one or more wires, a portable disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above.

[0146] As Figure 6 shown, a storage medium 60 for resource treatment analysis according to an embodiment of the present disclosure is described. It may adopt a portable compact disk read-only memory (CD-ROM), include program code, and may run on a terminal device, such as a personal computer. However, the storage medium of the present disclosure is not limited thereto. In this document, the readable storage medium may be any tangible medium that contains or stores a program that can be used by or in conjunction with an instruction execution system, apparatus, or device.

[0147] The readable signal medium may include a data signal propagated in a baseband or as part of a carrier wave, which carries the readable program code. Such a propagated data signal may take various forms, including - but not limited to - electromagnetic signals, optical signals, or any suitable combination of the above. The readable signal medium may also be any readable medium other than the readable storage medium that can send, propagate, or transmit a program for use by or in conjunction with an instruction execution system, apparatus, or device.

[0148] The program code contained on the readable medium may be transmitted using any appropriate medium, including - but not limited to - wireless, wired, optical fiber, RF, etc., or any suitable combination of the above.

[0149] Program code for performing the operations of the present disclosure can be written in any combination of one or more programming languages, including object-oriented programming languages such as Java, C++, etc., and also including conventional procedural programming languages such as the "C" language or similar programming languages. The program code can be executed entirely on the user's computing device, partially on the user's device, executed as a stand-alone software package, partially on the user's computing device and partially on a remote computing device, or entirely on a remote computing device or server. In cases involving a remote computing device, the remote computing device can be connected to the user's computing device through any type of network, including a local area network (LAN) or a wide area network (WAN), or alternatively, can be connected to an external computing device (e.g., by using an Internet service provider to connect through the Internet).

[0150] It should be noted that although several modules or sub-modules of the system are mentioned in the above detailed description, this division is merely exemplary and not mandatory. In fact, according to the embodiments of the present disclosure, the features and functions of two or more of the above-described modules can be embodied in one module. Conversely, the features and functions of one module described above can be further divided and embodied by multiple modules.

[0151] In addition, although the operations of the various modules of the system of the present disclosure are described in a specific order in the drawings, this does not require or imply that these operations must be performed in that specific order, or that all of the operations shown must be performed to achieve the desired result. Additionally or alternatively, some operations can be omitted, multiple operations can be combined into one operation for execution, and / or one operation can be decomposed into multiple operations for execution.

[0152] Those skilled in the art should understand that the embodiments of the present disclosure can be provided as a method, a system, or a computer storage medium. Therefore, the present disclosure can take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present disclosure can take the form of a computer storage medium implemented on one or more computer-usable storage media (including but not limited to disk memory and optical memory, etc.) that contain computer-usable program code.

[0153] The present disclosure is described with reference to the flowcharts and / or block diagrams of methods, apparatuses (systems), and computer storage media according to embodiments of the present disclosure. It should be understood that each flow and / or block in the flowchart and / or block diagram, and the combination of flows and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to the processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing devices to generate a machine, such that the instructions executed by the processor of the computer or other programmable data processing devices produce an apparatus for implementing the functions specified in one flow Figure 1 one flow or multiple flows and / or blocks Figure 1 or multiple blocks.

[0154] These computer program instructions can also be stored in a computer-readable memory that can direct a computer or other programmable data processing device to work in a specific manner, such that the instructions stored in the computer-readable memory produce a manufacture including an instruction apparatus that implements the functions specified in one flow Figure 1 one flow or multiple flows and / or blocks Figure 1 or multiple blocks.

[0155] These computer program instructions can also be loaded onto a computer or other programmable data processing device, such that a series of operation steps are executed on the computer or other programmable device to generate a computer-implemented process, and thus the instructions executed on the computer or other programmable device provide steps for implementing the functions specified in one flow Figure 1 one flow or multiple flows and / or blocks Figure 1 or multiple blocks.

[0156] Those skilled in the art will readily conceive of other embodiments of the present disclosure after considering the specification and practicing the invention disclosed herein. This application is intended to cover any variations, uses, or adaptations of the present disclosure, which follow the general principles of the present disclosure and include known common knowledge or conventional technical means in the technical field not disclosed herein. The specification and embodiments are only considered exemplary, and the true scope and spirit of the present disclosure are pointed out by the following claims.

[0157] It should be understood that the present disclosure is not limited to the exact structures described above and shown in the drawings, and various modifications and changes can be made without departing from its scope. The scope of the present disclosure is only limited by the appended claims.

Claims

1. A simulation method for semiconductor production, characterized in that, The method includes: Obtaining the historical production records of wafers in different batches within a preset time period; Calculating the standard deviation and average value of the waiting time of each production device according to the historical production records; Performing clustering analysis based on the standard deviation and average value of the waiting time of multiple production devices, and determining at least one target production device according to the clustering result, specifically including: performing binary clustering analysis on the standard deviation and average value of the waiting time of multiple production devices, and selecting from the clustering results a group of multiple production devices with stronger stability in the standard deviation and average value of the waiting time; Taking at least one production device among the multiple production devices corresponding to the selected group of standard deviation and average value of the waiting time as the target production device; Using at least one target production device to simulate semiconductor production.

2. The method according to claim 1, characterized in that, The calculating the standard deviation and average value of the waiting time of each production device according to the historical production records includes: Calculating the waiting time of each batch of wafers on each production device according to the historical production records; Calculating the standard deviation and average value of the waiting time of each production device according to the waiting time of each batch of wafers on each production device.

3. The method according to claim 1, characterized in that, Before using at least one target production device to simulate semiconductor production, it further includes: Determining that the standard deviation of the waiting time of at least one target production device is less than a first preset threshold; and / or Determining that the average value of the waiting time of at least one target production device is less than a second preset threshold.

4. The method according to claim 1, characterized in that, If there are multiple target production devices, the using at least one target production device to simulate semiconductor production includes: Determining the production order of using the multiple target production devices according to the preset step codes corresponding to the multiple target production devices; Simulating semiconductor production according to the production order of the multiple target production devices.

5. The method according to any one of claims 1 - 4, characterized in that, The obtaining the historical production records of wafers in different batches within a preset time period includes: Obtaining the arrival time of wafers in different batches at the production device and the processing time by the production device within a preset time period.

6. The method according to claim 5, characterized in that, Determining the waiting time in the following manner: Taking the difference between the processing time by the production device and the arrival time at the production device as the waiting time of the production device.

7. A simulation device for semiconductor production, characterized in that, The device includes: An obtaining module, configured to obtain the historical production records of wafers in different batches within a preset time period; A calculating module, configured to calculate the standard deviation and average value of the waiting time of each production device according to the historical production records; A clustering module, configured to perform binary clustering analysis on the standard deviation and average value of the waiting time of multiple production devices, and select from the clustering results a group of multiple production devices with stronger stability in the standard deviation and average value of the waiting time; taking at least one production device among the multiple production devices corresponding to the selected group of standard deviation and average value of the waiting time as the target production device; A simulation module, configured to use at least one target production device to simulate semiconductor production.

8. The device according to claim 7, characterized in that, The calculation module is specifically configured to: Calculate the waiting duration of each batch of wafers on each production device according to the historical production records; Calculate the standard deviation and the average value of the waiting duration of each production device according to the waiting duration of each batch of wafers on each production device.

9. The device according to claim 7, characterized in that, The device further includes a determination module; The determination module is configured to determine that the standard deviation of the waiting duration of at least one of the target production devices is less than a first preset threshold; and / or Determine that the average value of the waiting duration of at least one of the target production devices is less than a second preset threshold.

10. The device according to claim 7, characterized in that, If there are multiple target production devices, the simulation module is specifically configured to: Determine the production order of using the multiple target production devices according to the preset step codes corresponding to the multiple target production devices; Simulate semiconductor production according to the production order of the multiple target production devices.

11. The device according to any one of claims 7 - 10, characterized in that, The acquisition module is specifically configured to: Acquire the arrival time of different batches of wafers at the production device and the processing time by the production device within a preset duration.

12. The device according to claim 11, characterized in that, The calculation module is specifically configured to: Use the difference between the processing time by the production device and the arrival time at the production device as the waiting duration of the production device.

13. An electronic device, characterized in that, Comprising: A processor; A memory for storing executable instructions of the processor; wherein, the processor realizes the steps of the method according to any one of claims 1 to 6 by running the executable instructions.

14. A computer - readable and writable storage medium, on which computer instructions are stored, characterized in that, When the instructions are executed by the processor, the steps of the method according to any one of claims 1 to 6 are realized.

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