Patent ID: 12222707

DESCRIPTION OF THE EMBODIMENTS

In order to enhance comprehension of the content of the disclosure, the following embodiments are specifically cited as examples in which the disclosure may be implemented. In addition, wherever possible, elements/components/steps with the same reference numerals in the drawings and the embodiments represent the same or similar components.

FIG.1is a schematic diagram of a production schedule estimation system of a semiconductor process according to an embodiment of the disclosure. With reference toFIG.1, a production schedule estimation system100includes a processing device110and a storage device120. The processing device110is coupled to the storage device120. The storage device120may store a machine learning (ML) module121and a prediction model122. The prediction model122is an artificial intelligence (AI) model. In the embodiment, the processing device110may execute the machine learning module121, so as to obtain cycle time (CT) (or also known as retention time) data of a machine group and productivity data of the machine group of the semiconductor process. The cycle time data of the machine group is a sum of waiting time prior to reaching a machine and processing time of the machine. The processing device110may respectively input current-day work-in-process data (half-finished product information and/or newly-inputted product information of the semiconductor process), the cycle time data of the machine group, and the productivity data of the machine group into the executed prediction model122, to enable the prediction model122to output current-day cycle time data and a current-day move volume for each of multiple stations (multiple machines of the semiconductor process) in the machine group.

It should be noted that the “machine group” in the embodiment may refer to, for example, a combination of multiple machines of a semiconductor process that handle the same semiconductor process task such as a furnace control process machine group, a photolithography process machine group, or an etching process machine group, which is not limited by the disclosure. In addition, the “work-in-process” mentioned in the embodiment may refer to, for example, half-finished units or wafers of a semiconductor product such as a memory chip and a processing chip in the semiconductor process, and the disclosure does not limit a type of the semiconductor product.

In the embodiment, the processing device110may, for example, include a central processing unit (CPU) with an arithmetic function, or other programmable general-purpose or special-purpose microprocessors, a digital signal processor (DSP), a programmable controller, an application-specific integrated circuit (ASIC), a programmable logic device (PLD), other similar processing devices, or a combination of these devices. The storage device120may be, for example, a dynamic random access memory (DRAM), a flash memory, or a non-volatile random access memory (NVRAM). The storage device120may store the machine learning module121, the prediction model122, and related data described in each embodiment for accessing and execution by the processing device110.

FIG.2is a flowchart of a production schedule estimation method of the semiconductor process according to an embodiment of the disclosure. With reference toFIGS.1and2, the production schedule estimation system100may execute Steps S210to S240as follows, so as to realize production schedule estimation. In the Step S210, the processing device110may obtain the current-day work-in-process data, the cycle time data of the machine group, and the productivity data of the machine group. In the embodiment, the processing device110may obtain the current-day work-in-process data inputted by a semiconductor manufacturer, or, obtain type of (unfinished) work-in-process and a quantity of the work-in-process moved on a previous day from previous-day move data as predicted by the prediction model122to serve as the current-day work-in-process data. In the embodiment, the processing device110may obtain the cycle time data of the machine group and the productivity data of the machine group provided by the machine learning module121.

In the Step S220, the processing device110may input the current-day work-in-process data, the cycle time data of the machine group, and the productivity data of the machine group into the prediction model122. In the Step S230, the processing device110may calculate the current-day cycle time data and the current-day move volume for each of the multiple stations in the machine group through the prediction model122. In the Step S240, the processing device110may calculate current-day move data according to the current-day cycle time data and the current-day move volume for each of the multiple stations in the machine group. In the embodiment, the processing device110may further calculate and obtain the current-day move data according to the current-day cycle time data and the current-day move volume for each of the multiple stations in the machine group. It should be noted that the current-day move data refers to quantities of resultant products and work-in-process (WIP) that pass through the multiple stations in one or more machine groups, and the type of work-in-process. Therefore, the production schedule estimation system100and the production schedule estimation method of the embodiment may obtain a dynamic machine stagnation time of a current-day learning result, so as to allow the semiconductor manufacturer to use this information to perform accurate semiconductor production schedule planning.

FIG.3is a flowchart of obtaining the cycle time data of the machine group and the productivity data of the machine group according to an embodiment of the disclosure. With reference toFIGS.1and3, the production schedule estimation system100may execute Steps S310to S390as follows, so as to obtain the cycle time data of the machine group and the productivity data of the machine group. In the Step S310, the processing device110reads the machine learning module121stored in the storage device120, and executes the machine learning module121. In the Step S320, the processing device110may obtain work-in-process historical data. In the Step S330, the processing device110obtains machine historical data. In the Step S340, the processing device110may perform feature extraction on the work-in-process historical data and the machine historical data. In the Step S350, the processing device110may obtain multiple feature data.

In the embodiment, the manufacturer may input the WIP historical data and the machine historical data into the production schedule estimation system100. The processing device110may extract related data such as stagnation time data of different quantities of the work-in-process and different types of the work-in-process respectively of different stations from product historical data to serve as feature data. The processing device110may extract related data such as daily move data (that is, daily production capacity), up-time data, down-time data, mean time between failures (MTBF) data, and mean time to recovery (MTTR) data of the different stations from the machine historical data to serve as feature data.

In the Step S360, the processing device110may input the foregoing multiple feature data into the machine learning module121. In the Step S370, the machine learning module121may output the cycle time data of the machine group. In the embodiment, the cycle time data of the machine group may include multiple cycle times of the machine group corresponding to the multiple stations of the same machine type in each machine group for the different quantities of the work-in-process and the different types of the work-in-process (different quantities and different types of unfinished semiconductor products). In the Step S380, the machine learning module121may output the productivity data of the machine group. In the embodiment, the productivity data of the machine group may include at least one of multiple move volume data, multiple up-time data, multiple mean time between failures data, and multiple mean time to recovery data corresponding to the multiple stations of the same machine type in each machine group for the different quantities of the work-in-process and the different types of the work-in-process (different product combinations) inputted. In the Step S390, the processing device110may end the calculation of the machine learning module121, and continue to execute the production schedule estimation of the semiconductor process inFIG.4as follows. Therefore, the processing device110of the embodiment may effectively obtain the cycle time data of the machine group and the productivity data of the machine group, so as to enable the prediction model122to predict a production result of the semiconductor process.

FIG.4is a flowchart of a production schedule estimation method of the semiconductor process according to another embodiment of the disclosure. With reference toFIGS.1and4, the production schedule estimation system100may perform Steps S410to S470as follows, so as to obtain the cycle time data of the machine group and the productivity data of the machine group. In the Step S410, the processing device110may read the prediction model122stored in the storage device120, and execute the prediction model122. In the Step S421, the prediction model122may obtain cycle time data of a machine group of a j-th group, where j is a positive integer. In the embodiment, the processing device110may obtain the cycle time data of the machine group of the j-th group that may be operated on a current day from the cycle time data of the machine group obtained in the above-mentioned embodiment, and input it into the prediction model122. In the Step S422, the prediction model122may obtain productivity data of the machine group of the j-th group. In the embodiment, the processing device110may obtain the productivity data of the machine group of the j-th group that may be operated on the current day from the productivity data of the machine group obtained in the above-mentioned embodiment, and input it into the prediction model122. In the Step S423, the prediction model122may obtain work-in-process data on an i-th day, where i is a positive integer. In the embodiment, the work-in-process data on the i-th day may be current-day work-in-process data inputted by the manufacturer, or a type of (unfinished) work-in-process and a quantity of the work-in-process moved on (i−1)-th day obtained from the (i−1)-th day move data predicted by the prediction model122to serve as the work-in-process data on the i-th day.

In the Step S430, the processing device110may execute the prediction model122of the i-th day. The prediction model122may perform a prediction calculation based on the foregoing multiple data. In the Step S441, the processing device110may obtain cycle time data of each station on the i-th day. In the Step S442, the processing device110may obtain move data of each station on the i-th day. In the embodiment, the cycle time data (CT(i,j)) of each station on the i-th day and the move data (Move(i,j)) of each station on the i-th day may be respectively determined by the prediction model122is generated based on related data such as a quantity of the work-in-process (WIP(i,j)) (that is, the quantity of the work-in-process completed on the (i−1)-th day) that is going to flow into the machine group of the j-th group at beginning of the i-th day, and a quantity of the work-in-process (Close_WIP(i,j)) (that is, work-in-process that is completed in a machine group in front of the j-th group on the i-th day) that may flow into the j-th group on the i-th day, a scheduled maintenance time (PM) (that is, maintenance time) of the machine group of the j-th group on the i-th day, and whether the i-th day is a holiday (Holiday(i)) (that is, down day).

In this regard, calculations of the cycle time quantity (CT(i,j)) of each station on the i-th day and the output quantity (Move(i,j)) of each station on the i-th day may be expressed in programming language as the following Formula (1) and Formula (2).
CT(i,j)=AI_CT_F(WIP(i,j),CloseWIP(i,j),PM(i,j),Holiday(i) . . . )  Formula (1)
Move(i,j)=AI_Move_F(WIP(i,j),CloseWIP(i,j),PM(i,j),Holiday(i) . . . )  Formula (2)

In this regard, the cycle time (CT(i,j,p,s)) of different types of the work-in-process on the i-th day of different stations in the j-th group is equal to the cycle time (CT(i,j)) of each station on the i-th day, which as a result, may be expressed in the programming language as the following Formula (3). A parameter p and a parameter s are positive integers, and the parameter p represents a certain type of the work-in-process, and the parameter s represents a certain station.
CT(i,j,p,s)=CT(i,j)  Formula (3)

In this regard, an accumulation of the cycle time of each station from first day to the i-th day may be expressed in the programming language as the following Formula (4).
Acc_CT(i,j,p,s)=Σ(S=1 tos)CT(i,j,p,s)  Formula (4)

In this regard, calculation of the move quantity (Move(i,j,p,s)) of a certain station of a certain type of the work-in-process on the i-th day may be expressed in the programming language as the following Formula (5), where AI_GA_Move_ratio_F(i,j,p,s) is a move ratio representing a certain type of the work-in-process.
Move(i,j,p,s)=AI_GA_Move_ratio_F(i,j,p,s)*Move(i,j)  Formula (5)

In the Step S450, the processing device110may calculate work-in-process data of a (i+1)-th day. In the embodiment, quantity of work-in-process (WIP(i+1,j)) on the (i+1)-th day may be a result of accumulating the quantity of the work-in-process (WIP(i+1,j,p,s)) of each type of the work-in-process at each station on the (i+1)-th day, and may be expressed in the programming language as the following Formula (6).
WIP(i+1,j)=ΣpΣsWIP(i+1,j,p,s)  Formula (6)

In addition, the quantity of the work-in-process (WIP(i+1,j,p,s)) of a certain type of the work-in-process at a certain station on the (i+1)-th day may be the quantity of the work-in-process (WIP(i,j,p,s)) of the certain type of the work-in-process at the certain station on the i-th day minus the move quantity (Move(i,j,p,s)) of the certain type of the work-in-process at the certain station on the i-th day, plus a cumulative result (ΣJMove(i,j,p,s−1)) of completed quantity of the certain type of the work-in-process at a previous station on the i-th day, where the result may be expressed in the programming language as the following Formula (7).
WIP(i+1,j,p,s)=WIP(i,j,p,s)−Move(i,j,p,s)+ΣJMove(i,J,p,s−1)  Formula (7)

It should be noted that in the process of calculating the work-in-process data on the i-th day, the processing device110may obtain the move data on the i-th day (the current-day move data), and the move data on the i-th day includes the type of the work-in-process after the work-in-process has been processed by the multiple stations in the machine group of the j-th group and move volume on the i-th day. In this regard, calculation of the move volume on the i-th day, that is, quantity of the work-in-process (Close_WIP(i+1,j)) that may flow into the machine group of the j-th group on the (i+1)-th day may be performed in the programming language as the following Formula (8).
Close_WIP(i+1,j)=ΣpΣsΣJWIP(i+1,J,p,s)  Formula (8)

In the Step S460, the processing device110may determine whether the product is completed. When the product is incomplete, the Step S410is executed again to perform prediction of the next day (i+1). When the product is completed, the Step S470is executed to end the prediction. It should be noted that the processing device110may extract next-day work-in-process data from the current-day move data. In this regard, in the process of recursively executing the Steps S410to S460, the processing device110may calculate the next-day work-in-process data according to daily cycle time data of each of the multiple stations in the machine group and daily move volume, and recursively execute the prediction model122, so as to obtain production time of the product and move quantity of the product. The move quantity of the product is a sum of multiple move volumes corresponding to every of the multiple stations in multiple machine groups on different days generated in the process of recursively executing the production schedule estimation method (the Steps S410-S460). In addition, the multiple cycle time data corresponding to the different days generated during the recursive execution of the production schedule estimation method (the Steps S410to S460) are multiple dynamic machine cycle times. Therefore, based on recursive execution results of the above-mentioned Steps S410to S470, the prediction model122may accurately predict time required for completion of the process flow of the semiconductor process product, and may predict related data such as the type of the work-in-process completed at each station of each machine group daily and the quantity of the work-in-process, so as to realized accurate production schedule estimation.

In summary, the production schedule estimation method and system of the semiconductor process according to the disclosure may realize accurate production schedule estimation results through two-stage estimation calculation. The production schedule estimation method and system of the semiconductor process according to the disclosure may first generate the dynamic cycle time data of the machine group and the productivity data of the machine group that may change with different dates through machine learning according to the work-in-process historical data and the semiconductor process machine historical data. Then, the production schedule estimation method and system of the semiconductor process according to the disclosure may further accurately predict the time required for the completion of process flow of the semiconductor process product through the prediction model according to the dynamic cycle time data of the machine group and the productivity data of the machine group that may change with the different dates. In addition, the production schedule estimation method and system of the semiconductor process according to the disclosure may also predict related estimation information such as the type of the work-in-process and the quantity of the work-in-process completed daily at each station of each machine group, so as to allow the semiconductor manufacturer to use the foregoing related estimation information to design an effective semiconductor process production schedule, thereby effectively improving the efficiency of the semiconductor process.

Although the disclosure has been described with reference to the abovementioned embodiments, they are not intended to limit the disclosure. It is apparent that any one of ordinary skill in the art may make changes and modifications to the described embodiments without departing from the spirit and the scope of the disclosure. Accordingly, the scope of the disclosure is defined by the claims appended hereto and their equivalents in which all terms are meant in their broadest reasonable sense unless otherwise indicated.