Method and device for determining wafer cycle time

文档序号：136372 发布日期：2021-10-22 浏览：33次中文

阅读说明：本技术 晶圆循环时间的确定方法和装置 (Method and device for determining wafer cycle time ) 是由王世生于 2021-07-19 设计创作，主要内容包括：本申请实施例提供一种晶圆循环时间的确定方法和装置,获取多个站点的机台的实际生产数据,根据多个站点的机台的实际生产数据,建立随机数表,该随机数表中包括各机台的样本数据,样本数据包括晶圆的处理时间、机台宕机概率、宕机修复时间和随机数区间,使用Monte Carlo算法和随机数表,模拟晶圆在各种机台组合下的生产过程得到模拟器,模拟过程中晶圆经过每个机台时生成的随机数属于随机数表中的随机数区间,随机数用于选择机台的状态,将机台组合、晶圆数量以及模拟次数输入模拟器,得到晶圆的循环时间。该方式能够准确的模拟出晶圆的循环时间,且针对任何机台组合、晶圆数量以及模拟次数均能够得到晶圆的循环时间,应用更加灵活。(The embodiment of the application provides a method and a device for determining wafer cycle time, which are used for acquiring actual production data of machines of multiple sites, establishing a random number table according to the actual production data of the machines of the multiple sites, wherein the random number table comprises sample data of each machine, the sample data comprises processing time of a wafer, machine downtime probability, downtime repair time and a random number interval, a Monte Carlo algorithm and the random number table are used for simulating production processes of the wafer under various machine combinations to obtain a simulator, random numbers generated when the wafer passes through each machine in the simulation process belong to the random number interval in the random number table, the random numbers are used for selecting states of the machines, and the machine combinations, the number of the wafers and simulation times are input into the simulator to obtain the cycle time of the wafer. The method can accurately simulate the cycle time of the wafer, and can obtain the cycle time of the wafer aiming at any machine combination, the number of the wafers and the simulation times, so that the method is more flexible to apply.)

1. A method for determining a wafer cycle time, comprising:

acquiring actual production data of machines of a plurality of sites, wherein the actual production data of each machine comprises processing time of a wafer, downtime probability of the machine and downtime repair time, each machine has a plurality of different downtime repair times, the downtime probabilities corresponding to the different downtime repair times are different, and the sum of the downtime probabilities of each machine is 1;

according to actual production data of machines of the multiple sites, a random number table is established, wherein the random number table comprises sample data of each machine, the sample data comprises wafer processing time, machine downtime probability, downtime repair time and random number intervals, the sample data of the same machine is arranged from beginning to end according to the downtime repair time, the random number intervals in the random number table are obtained based on the same random number generation mode, the greater the machine downtime probability of the sample data is, the more random numbers included in the random number intervals of the sample data are, the random number intervals of different sample data are not overlapped, and the random numbers of the random number intervals of adjacent sample data are connected;

simulating the production process of the wafer under various machine combinations formed by the plurality of stations by using a Monte Carlo algorithm and the random number table to obtain a simulator, wherein the output of the simulator is the cycle time of the wafer passing through the machine combinations, the random number generated when the wafer passes through each machine in the simulation process belongs to a random number interval in the random number table, and the random number is used for selecting the state of the machine;

and inputting the machine combination, the number of the wafers and the simulation times into the simulator to obtain the cycle time of the wafers.

2. The method as claimed in claim 1, wherein said simulating the production process of the wafer under various tool combinations formed by the plurality of stations using Monte Carlo algorithm and the random number table results in a simulator comprising:

for each machine combination, when a wafer passes through each machine of the machine combination, generating a random number according to the random number generation mode;

according to the random number table, determining sample data to which the random number belongs;

determining the processing time and downtime repair time of the wafer corresponding to the machine according to the sample data to which the random number belongs;

adding the waiting time of the wafer on the machine, the processing time of the wafer corresponding to the machine and the downtime repair time to obtain the total stay time of the wafer on the machine;

and adding the total residence time of the wafer passing through each machine station under the machine station combination to obtain the cycle time of the wafer.

3. The method of claim 2, wherein for each tool set, a wafer and a production process of a continuous production of a plurality of wafers are simulated.

4. A method as claimed in claim 3, wherein when simulating continuous production of a plurality of wafers, the plurality of wafers are subjected to a first come first process at each station passed by.

5. The method according to any one of claims 1 to 4, wherein the value of the random number interval is [0,1], and the minimum value of the random number interval of any sample data is the sum of the machine downtime probabilities of all previous sample data of the same machine, and the maximum value is the sum of the minimum value and the machine downtime probability of the sample data.

6. The method according to any one of claims 1-4, wherein the random number table further includes a cumulative downtime probability, the cumulative downtime probability of the machine is equal to the sum of the downtime probabilities of the current sample data and all the current sample data of the same machine;

the value of the random number interval is [0,1], the minimum value of the random number interval of any sample data is the downtime cumulative probability of the previous sample data, and the maximum value is the downtime cumulative probability of the sample data.

7. The method according to any one of claims 1 to 4, wherein the number of wafers is equal to the number of simulations N, wherein the number of wafers simulated at the ith time is equal to i, and i has a value of 1-N.

8. The method of claim 7, wherein inputting the tool set, the number of wafers, and the number of simulations into the simulator to obtain a cycle time of the wafer comprises:

when the simulation times are N, simulating i wafers at the ith time, and inputting the machine combination and the i wafers into the simulator to obtain the average cycle time of the simulated wafers at the ith time;

and obtaining a trend graph of the cycle time according to the average cycle time of the wafers obtained by the N times of simulation, wherein the horizontal axis of the trend graph of the cycle time is the number of the wafers, and the vertical axis is the average cycle time.

9. An apparatus for determining a wafer cycle time, comprising:

the system comprises an acquisition module, a processing module and a recovery module, wherein the acquisition module is used for acquiring actual production data of machines of a plurality of sites, and the actual production data of each machine comprises processing time of a wafer, downtime probability of the machine and downtime recovery time, each machine has a plurality of different downtime recovery times, the downtime probabilities corresponding to different downtime recovery times are different, and the sum of the downtime probabilities of each machine is 1;

the establishing module is used for establishing a random number table according to actual production data of the machines of the multiple sites, wherein the random number table comprises sample data of each machine, the sample data comprises wafer processing time, machine downtime probability, downtime repair time and random number intervals, the sample data of the same machine is arranged from beginning to end according to the downtime repair time, the random number intervals in the random number table are obtained based on the same random number generation mode, the greater the machine downtime probability of the sample data is, the more random numbers are included in the random number intervals of the sample data, the random number intervals of different sample data are not overlapped, and the random numbers in the random number intervals of adjacent sample data are connected;

the simulation module is used for simulating the production process of the wafer under various machine combinations formed by the plurality of stations by using a Monte Carlo algorithm and the random number table to obtain a simulator, the output of the simulator is the cycle time of the wafer passing through the machine combinations, the random number generated when the wafer passes through each machine in the simulation process belongs to the random number interval in the random number table, and the random number is used for selecting the state of the machine;

and the determining module is used for inputting the machine combination, the number of the wafers and the simulation times into the simulator to obtain the cycle time of the wafers.

10. An electronic device, comprising: at least one processor and memory;

the memory stores computer-executable instructions;

the at least one processor executing the computer-executable instructions stored by the memory causes the at least one processor to perform the method of any of claims 1-8.

11. A computer-readable storage medium having computer-executable instructions stored thereon, which when executed by a processor, are configured to implement the method of any one of claims 1 to 8.

12. A computer program product comprising a computer program, characterized in that the computer program, when executed by a processor, implements the method of any one of claims 1 to 8.

Technical Field

The present disclosure relates to the field of semiconductors, and more particularly, to a method and an apparatus for determining a wafer cycle time.

Background

The process of wafer (wafer) in the semiconductor industry is extremely complex, with thousands of fabrication processes. Each wafer will go through thousands of stations in sequence from beginning to end of production, and each station will correspond to a different number of machines.

The time from the beginning of entry of the wafer into the first site to the end of the last site is called the cycle time of the wafer. The cycle time is mainly used for determining the production capacity of a machine, and the required processing time of the machine from the time when a certain product is put into the machine to the time when the product is processed is an index of the production efficiency. However, in actual production, since the number of process stations is too many, the combination of machines is various, and it is difficult to measure the cycle time of wafer production under different combinations of machines, and the cost is huge.

Disclosure of Invention

According to some embodiments, a first aspect of the present application provides a method for determining a wafer cycle time, comprising:

and inputting the machine combination, the number of the wafers and the simulation times into the simulator to obtain the cycle time of the wafers.

Optionally, the simulating a production process of the wafer under various machine combinations formed by the plurality of stations by using a Monte Carlo algorithm and the random number table to obtain a simulator includes:

for each machine combination, when a wafer passes through each machine of the machine combination, generating a random number according to the random number generation mode;

according to the random number table, determining sample data to which the random number belongs;

determining the processing time and downtime repair time of the wafer corresponding to the machine according to the sample data to which the random number belongs;

and adding the total residence time of the wafer passing through each machine station under the machine station combination to obtain the cycle time of the wafer.

Optionally, for each machine combination, a generation process of continuous production of one wafer and multiple wafers is simulated respectively.

Optionally, when simulating continuous production of a plurality of wafers, the plurality of wafers at each station passing by follows a first-come-first-processed principle.

Optionally, a value of the random number interval is [0,1], a minimum value of the random number interval of any sample data is a sum of machine downtime probabilities of all previous sample data of the same machine, and a maximum value is a sum of the minimum value and the machine downtime probability of the sample data.

Optionally, the random number table further includes a downtime cumulative probability, where the downtime cumulative probability of the machine is equal to the sum of the downtime probabilities of the current sample data and all the current sample data of the same machine;

Optionally, the number of the wafers is equal to the simulation time N, where the number of the wafers simulated in the ith time is equal to i, and a value of i is 1 to N.

Optionally, the inputting the machine combination, the number of wafers, and the simulation times into the simulator to obtain the cycle time of the wafers includes:

According to some embodiments, a second aspect of the present application provides an apparatus for determining a wafer cycle time, comprising:

and the determining module is used for inputting the machine combination, the number of the wafers and the simulation times into the simulator to obtain the cycle time of the wafers.

According to some embodiments, a third aspect of the present application provides an electronic device comprising: at least one processor and memory;

the memory stores computer-executable instructions;

the at least one processor executing the computer-executable instructions stored by the memory causes the at least one processor to perform the method according to the first aspect of the application.

According to some embodiments, a fourth aspect of the present application provides a computer-readable storage medium having stored therein computer-executable instructions for implementing the method according to the first aspect of the present application when executed by a processor.

According to some embodiments, a fifth aspect of the present application provides a computer program product comprising a computer program which, when executed by a processor, performs the method as described in the first aspect of the present application.

The embodiment of the application provides a method and a device for determining wafer cycle time, which are used for acquiring actual production data of machines of a plurality of sites, establishing a random number table according to the actual production data of the machines of the plurality of sites, wherein the random number table comprises sample data of each machine, the sample data comprises processing time of a wafer, machine downtime probability, downtime repair time and a random number interval, the random number interval in the random number table is obtained based on the same random number generation mode, a simulator is obtained by using a Monte Carlo algorithm and the random number table to simulate the production process of the wafer under various machine combinations formed by the plurality of sites, the random number generated when the wafer passes through each machine in the simulation process belongs to the random number interval in the random number table, the random number is used for selecting the state of the machine, and the machine combinations, the number of the wafer and the simulation times are input into the simulator, and obtaining the cycle time of the wafer. The method of the embodiment of the application has at least the following advantages: the method can accurately simulate the cycle time of the wafer, can obtain the cycle time of the wafer aiming at any machine combination, the number of the wafers and the simulation times, and is more flexible to apply.

Drawings

Fig. 1 is a flowchart of a method for determining a wafer cycle time according to an embodiment of the present disclosure;

FIG. 2 is a graph of the trend of the average cycle time for the simulation of continuous production of multiple wafers;

FIG. 3 is a schematic flow chart of a simulation process of the Monte Carlo algorithm;

fig. 4 is a schematic structural diagram of an apparatus for determining a wafer cycle time according to a third embodiment of the present application;

fig. 5 is a schematic structural diagram of an electronic device according to a fourth embodiment of the present invention.

Detailed Description

Reference will now be made in detail to the exemplary embodiments, examples of which are illustrated in the accompanying drawings. When the following description refers to the accompanying drawings, like numbers in different drawings represent the same or similar elements unless otherwise indicated. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with the present application. Rather, they are merely examples of apparatus and methods consistent with certain aspects of the present application, as detailed in the appended claims.

wafer is a wafer, and is made of pure silicon (Si). The specification is generally classified into 6 inches, 8 inches and 12 inches, and the wafer refers to a silicon wafer used for manufacturing a silicon semiconductor integrated circuit, and is called a wafer because the wafer has a circular shape.

The process of manufacturing wafers is complicated, each wafer may pass through thousands of stations in sequence from the beginning to the end of the production, each station is used for processing the wafer differently, each station may include one or more machines, and the number of machines included in each station may be different. When a station comprises a plurality of machines, the plurality of machines process the same wafer, after the wafer reaches the station, any idle machine can be selected to process the wafer, if all machines at the station are busy, the wafer is in a waiting state, and the wafer is processed after the idle machines are available.

In order to know the generation efficiency of the machine, the cycle time (cycle time) of wafer production needs to be counted. At present, the cycle time of the wafer is difficult to be counted because of more stations of the process. In order to solve the problems in the prior art, an embodiment of the present application provides a method for determining a wafer cycle time, which simulates a wafer generation process through a Monte Carlo (Monte Carlo) algorithm to obtain a simulator, and the simulator can obtain the cycle time of a wafer under any machine combination.

The Monte Carlo method is also called a statistical simulation method, and solves the calculation problem by using random numbers (or pseudo-random numbers), and is an important numerical calculation method. The application of the Monte Carlo method in solving the practical problem mainly has two parts: when a certain process is simulated by a Monte Carlo method, random variables of various probability distributions need to be generated; and estimating the digital characteristics of the model by using a statistical method so as to obtain a numerical solution of the actual problem.

The Monte Carlo method mainly comprises the following three steps.

Step one, constructing or describing a probability process: for the problem which has random nature, such as particle transport problem, the probability process is mainly described and simulated correctly, for the certainty problem which is not random nature, an artificial probability process must be constructed in advance, and some parameters of the artificial probability process are just the solution of the required problem. I.e. to convert the problem of not having random properties into the problem of random properties.

Step two, sampling from known probability distribution is realized: after the probabilistic model is constructed, since each probabilistic model can be considered to be composed of various probability distributions, generating random variables (or random vectors) of known probability distributions becomes a basic means for realizing simulation experiments by the Monte Carlo method, which is also called random sampling. One of the simplest, most basic, and most important probability distributions is a uniform distribution (or rectangular distribution) over (0, 1). Random numbers are random variables with such a uniform distribution. The problem of generating random numbers is that of sampling from this distribution. On a computer, random numbers can be generated by a physical method, but the random numbers are expensive, cannot be repeated and are inconvenient to use. Another method is to generate with a mathematical recurrence formula. The sequence thus generated is different from a truly random number sequence and is therefore called a pseudo-random number, or pseudo-random number sequence. However, it has been shown by various statistical tests to have properties similar to a true random number, or sequence of random numbers, and can therefore be used as a true random number. From the known distributed random sampling there are various methods, which, unlike the uniformly distributed sampling from (0,1), are realized by means of random sequences, that is to say, on the premise of generating random numbers. It follows that random numbers are the basic tool we implement Monte Carlo simulations.

And step three, establishing various estimators, namely investigating and registering the results of the simulation experiment, and obtaining the solution of the problem from the results.

In an embodiment of the present application, Monte Carlo is used to perform approximate simulation, and if the cycle time of a wafer produced by using a certain machine combination is to be calculated, factors that may affect the cycle time of the wafer, such as the possibility of downtime of the machine under the machine combination, the length of repair time after downtime, and the busy condition of the machine, need to be considered. Therefore, these conditions can be set at the beginning of the simulation and the simulation run 1000 or more times to obtain the estimated cycle time. Generally, the more the number of simulations, the more accurate the estimation.

Fig. 1 is a flowchart of a method for determining a wafer cycle time according to an embodiment of the present disclosure, and as shown in fig. 1, the method according to the embodiment includes the following steps.

The method includes the steps of S101, obtaining actual production data of machines of multiple sites, wherein the actual production data of each machine comprises processing time of a wafer, downtime probabilities of the machines and downtime repair time, each machine has multiple different downtime repair time, the downtime probabilities corresponding to different downtime repair time are different, and the sum of the downtime probabilities of each machine is 1.

During the actual production process of the wafer, a large amount of production data is generated, and the actual production data of the machines at the multiple stations is obtained by collecting the production data of the wafer, which is also called as historical production data. The processing time (process time) of the wafer refers to the actual processing time of the machine to the wafer, each machine may have a plurality of different downtime modification times (recovery times), and different downtime repair times correspond to different downtime probabilities. As shown below, table one is a processing time table and table two is a repair time table.

Watch 1

Watch two

Machine station ID	Repair time	Probability of downtime
			A	0	0.97
A	10	0.003
			A	20	0.006
A	30	0.01
			A	40	0.006
A	50	0.005
			B	0	0.6
B	10	0.05
			B	20	0.21
……	……	……

S102, establishing a random number table according to actual production data of machines of a plurality of stations.

The random number table comprises sample data of each machine, each sample data comprises processing time of a wafer, machine downtime probability, downtime repair time and random number intervals, and the sample data of the same machine is arranged from small to large according to the downtime repair time, wherein the random number intervals in the random number table are obtained based on the same random number generation mode, the larger the downtime probability of the machine of the sample data is, the more random numbers included in the random number intervals of the sample data are, the random number intervals of different sample data are not overlapped, and the random numbers in the random number intervals of adjacent sample data are connected.

The random number is used to indicate the state of the machine, and the state of the machine is random, so the generated random number can indicate the state of the machine.

In an exemplary manner, the value of the random number interval is [0,1], the minimum value of the random number interval of any sample data is the sum of the machine downtime probabilities of all previous sample data of the same machine, and the maximum value is the sum of the minimum value and the machine downtime probability of the sample data.

In another exemplary manner, the random number table further includes a cumulative downtime probability (cumulative ratio), where the cumulative downtime probability of the machine is equal to the sum of the current sample data of the same machine and the downtime probabilities of all the current sample data. When the value of the random number interval is [0,1], correspondingly, the minimum value of the random number interval of any sample data is the downtime accumulated probability of the previous sample data, and the maximum value is the downtime accumulated probability of the sample data.

Referring to table three, table three is a schematic diagram of a random number table.

Watch III

Machine station ID	Time of treatment	Repair time	Probability of downtime	Cumulative downtime probability	Random number interval
						A	10	0	0.97	0.97	0-0.97
A	10	10	0.003	0.973	0.97-0.973
						A	10	20	0.006	0.979	0.973-0.979
A	10	30	0.01	0.989	0.979-0.989
						A	10	40	0.006	0.995	0.989-0.995
A	10	50	0.005	1	0.995-1
						B	20	0	0.6	0.6	0-0.6
B	20	10	0.05	0.65	0.6-0.65
						B	20	20	0.21	0.86	0.65-0.86
……	……	……	……	……	……

Each row in the third table represents one sample data, the value range of the random number in the random number table is 0-1, and the random number takes three digits after a decimal point, so that the total number of 1000 random numbers in the range of 0-1 is 0.001, 0.993, 0.854 and the like. It is understood that the new random number may be obtained by multiplying the random number in the table by a predetermined multiple, for example, by 1000, and the range of the random number is 0-1000, in this case, 970 random numbers are included in the original random number interval 0-0.97, which is respectively 0.000-0.970, and the new random number interval is 1-970.

It is understood that the random number table may not include the processing time, and the processing time of the machine is stored in a table separately.

S103, simulating the production process of the wafer under various machine combinations formed by the plurality of stations by using a Monte Carlo algorithm and a random number table to obtain a simulator, wherein the output of the simulator is the cycle time of the wafer passing through the machine combinations, the random number generated when the wafer passes through each machine in the simulation process belongs to a random number interval in the random number table, and the random number is used for selecting the state of the machine.

The plurality of stations can form a plurality of machine combinations, illustratively, one machine combination is represented as 1-2-1, the meaning of the machine combination is that 3 stations are provided, the first station has one machine, the second station has 2 machines, and the 3 rd station has 1 machine. Under the same machine combination, multiple times of simulation can be performed, and the states of the selected machines are possibly different due to different random numbers generated by each simulation.

Optionally, for each machine combination, a generation process of a wafer and a continuous production of multiple wafers are simulated respectively. In an actual production process, a plurality of wafers are continuously produced at the same time. When simulating continuous production of a plurality of wafers, the wafers pass through each station and are processed first in advance. When the wafer arrives at a certain station, if no idle machine exists, the wafer is in a waiting state, and the waiting time of the wafer is uncertain.

And S104, inputting the machine combination, the number of the wafers and the simulation times into the simulator to obtain the cycle time of the wafers.

The simulator is obtained through multiple times of simulation, and a subsequent user can flexibly input any machine combination, the number of wafers and the simulation times, so that the cycle time of the wafers can be obtained.

In one possible implementation, the number of wafers is equal to the simulation number N, where the number of wafers simulated for the ith time is equal to i, and the value of i is 1-N. And when the simulation times are N, simulating i wafers in the ith time, and inputting the machine combination and the i wafers into the simulator during the ith simulation to obtain the average cycle time of the i simulated wafers, namely the average cycle time of the i wafers output by the simulator during the ith simulation. Each simulation obtains an average cycle time, and then, a trend graph of the cycle time is obtained according to the average cycle time of the wafers obtained by the N simulations (i.e., N average cycle times), wherein the horizontal axis of the trend graph of the cycle time is the number of the wafers, and the vertical axis of the trend graph of the cycle time is the average cycle time.

For example, if there are 100 wafers in total and the simulation times is 100, then under the tool set, the production situation of 1 wafer is simulated at the 1 st time to obtain the cycle time of the wafer, the production situations of two wafers are simulated at the 2 nd time to obtain the cycle time of each wafer, and so on, and the production situation of 100 wafers is simulated at the 100 th time to obtain the cycle time of 100 wafers. After each simulation, the average cycle time of a plurality of wafers is obtained according to the obtained cycle time of each wafer, for example, when the simulation is performed for the 50 th time, the cycle times of 50 wafers are added and then divided by 50 to obtain the average cycle time of the simulation.

Table four is a schematic diagram of the average cycle time when simulating the production of multiple wafers, and is shown below.

Watch four

Fig. 2 is a trend graph of the average cycle time when a plurality of wafers are continuously produced, and as shown in fig. 2, the simulation result is the simulation result in the station combination 1-2-1, and the average cycle time when 1 wafer, 2 wafers, 3 wafers … … and 100 wafers are continuously produced is simulated, the horizontal axis is the wafer data, and the vertical axis is the average cycle time. From table four and fig. 2, it can be seen that the average cycle time of the wafer is gradually increased as the wafer data is increased under one tool set. It is understood that as the wafer data increases, the overall trend of the average cycle time of the wafer increases, but local data fluctuations are not excluded, e.g., in 100 simulation runs, the average cycle time obtained from 50-58 simulations increases, but the average cycle time obtained from 59 th simulation decreases.

In another possible implementation, the number of wafers and the number of simulation times may be different, for example, the number of wafers is 100, and the number of simulation times is 5. Then 5 simulations are needed in total, each simulation is needed to simulate the process of continuous production of 100 wafers, each simulation results in an average cycle time of the wafer, and the average cycle times of the 5 obtained wafers may be different. Or, if the number of wafers is 100 and the simulation time is 1, the simulation is only required once.

In the embodiment, the actual production data of the machines of the multiple sites is acquired, a random number table is established according to the actual production data of the machines of the multiple sites, the random number table comprises sample data of each machine, the sample data comprises processing time of a wafer, machine downtime probability, downtime repair time and a random number interval, wherein the random number interval in the random number table is obtained based on the same random number generation mode, a simulator is obtained by simulating the production process of the wafer under various machine combinations formed by the multiple sites by using a Monte Carlo algorithm and the random number table, the output of the simulator is the cycle time of the wafer passing through the machine combination, the random number generated when the wafer passes through each machine in the simulation process belongs to the random number interval in the random number table, the random number is used for selecting the state of the machine, the machine combination, the number of the wafer and the simulation times are input into the simulator, and obtaining the cycle time of the wafer. The method can accurately simulate the cycle time of the wafer, and can obtain the cycle time of the wafer aiming at any machine combination, the number of the wafers and the simulation times, so that the method is more flexible to apply.

On the basis of the first embodiment, the second embodiment of the present invention provides a simulation process of a Monte Carlo algorithm, as shown in fig. 3, the method provided by the present embodiment includes the following steps, and the present embodiment is an implementation manner of step S103 in the first embodiment.

S201, aiming at each machine combination, when a wafer passes through each machine of the machine combination, a random number is generated according to a random number generation mode.

The generated random number represents the state of the machine, and different states of the machine correspond to different processing time and repair time.

S202, determining sample data to which the random number belongs according to the random number table.

And determining a random number interval to which the generated random number belongs according to the random number table, wherein sample data of the random number interval to which the random number belongs is the sample data to which the random number belongs. And determining sample data to which the random number belongs, namely determining the processing time and downtime repair time of the wafer corresponding to the machine.

And S203, determining the processing time and the downtime repair time of the wafer corresponding to the machine according to the sample data to which the random number belongs.

S204, adding the waiting time of the wafer on the machine, the processing time of the wafer corresponding to the machine and the downtime repair time to obtain the total stay time of the wafer on the machine.

In the simulation process, a scenario of continuous production of a plurality of wafers exists, and when a plurality of wafers are continuously produced, the wafers may need to wait for a period of time before being processed after arriving at a certain machine. Therefore, the total residence time of the wafer on one machine is obtained by adding the waiting time of the wafer on the machine, the processing time of the wafer corresponding to the machine and the downtime repair time.

S205, the total residence time of the wafer passing through each machine under the machine combination is added to obtain the cycle time of the wafer.

And adding the total residence time of the wafer in each machine to obtain the cycle time of the wafer.

Fig. 4 is a schematic structural diagram of an apparatus for determining a wafer cycle time according to a third embodiment of the present disclosure, and as shown in fig. 4, the apparatus 100 according to the third embodiment includes the following modules.

The obtaining module 11 is configured to obtain actual production data of machines at multiple sites, where the actual production data of each machine includes processing time of a wafer, downtime probabilities of the machines, and downtime repair time, where each machine has multiple different downtime repair times, the downtime probabilities corresponding to different downtime repair times are different, and a sum of the downtime probabilities of each machine is 1;

the establishing module 12 is configured to establish a random number table according to actual production data of the machines at the multiple sites, where the random number table includes sample data of each machine, the sample data includes processing time of a wafer, machine downtime probability, downtime repair time, and random number intervals, and the sample data of the same machine is arranged from beginning to end according to the downtime repair time, where the random number intervals in the random number table are obtained based on the same random number generation manner, and the greater the machine downtime probability of the sample data is, the more random numbers included in the random number intervals of the sample data are, the more random number intervals of different sample data are not overlapped, and the random numbers in the random number intervals of adjacent sample data are connected;

the simulation module 13 is configured to simulate, by using a Monte Carlo algorithm and the random number table, a production process of a wafer in various machine combinations formed by the multiple stations to obtain a simulator, where an output of the simulator is a cycle time of the wafer passing through the machine combination, a random number generated when the wafer passes through each machine in the simulation process belongs to a random number interval in the random number table, and the random number is used to select a state of the machine;

and the determining module 14 is used for inputting the machine combination, the number of the wafers and the simulation times into the simulator to obtain the cycle time of the wafers.

Optionally, the simulation module 13 is specifically configured to:

for each machine combination, when a wafer passes through each machine of the machine combination, generating a random number according to the random number generation mode;

according to the random number table, determining sample data to which the random number belongs;

determining the processing time and downtime repair time of the wafer corresponding to the machine according to the sample data to which the random number belongs;

and adding the total residence time of the wafer passing through each machine station under the machine station combination to obtain the cycle time of the wafer.

Optionally, for each machine combination, a generation process of continuous production of one wafer and multiple wafers is simulated respectively.

Optionally, when simulating continuous production of a plurality of wafers, the plurality of wafers at each station passing by follows a first-come-first-processed principle.

Optionally, the number of the wafers is equal to the simulation time N, where the number of the wafers simulated in the ith time is equal to i, and a value of i is 1 to N.

Optionally, the determining module is specifically configured to:

The apparatus of this embodiment may be configured to perform the method described in the first embodiment or the second embodiment, and the specific implementation manner and the technical effect are similar, which are not described herein again.

It should be noted that, in the method for determining the wafer cycle time of the apparatus provided in the foregoing embodiment, only the division of the functional modules is illustrated, and in practical applications, the function distribution may be completed by different functional modules according to needs, that is, the internal structure of the apparatus may be divided into different functional modules to complete all or part of the functions described above.

Fig. 5 is a schematic structural diagram of an electronic device according to a fourth embodiment of the present invention, and as shown in fig. 5, the electronic device 200 includes: the processor 21 is configured to execute the instructions stored in the memory, so that the electronic device 200 executes the method according to the first embodiment or the second embodiment, where the specific implementation manner and the technical effect are similar, and details are not repeated here.

A fifth embodiment of the present invention provides a computer-readable storage medium, where a computer-executable instruction is stored in the computer-readable storage medium, and the computer-executable instruction is used by a processor to implement the method according to the first embodiment or the second embodiment, where specific implementation manners and technical effects are similar and are not described herein again.

A sixth embodiment of the present invention provides a computer program product, including a computer program, where when the computer program is executed by a processor, the method according to the first embodiment or the second embodiment is implemented, and specific implementation manners and technical effects are similar, and are not described herein again.

Other embodiments of the present application will be apparent to those skilled in the art from consideration of the specification and practice of the invention disclosed herein. This application is intended to cover any variations, uses, or adaptations of the invention following, in general, the principles of the application and including such departures from the present disclosure as come within known or customary practice within the art to which the invention pertains. It is intended that the specification and examples be considered as exemplary only, with a true scope and spirit of the application being indicated by the following claims.

It will be understood that the present application is not limited to the precise arrangements described above and shown in the drawings and that various modifications and changes may be made without departing from the scope thereof. The scope of the application is limited only by the appended claims.

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Method and device for determining wafer cycle time

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