Characteristic prediction method, method of manufacturing semiconductor device, recording medium of characteristic prediction program, characteristic prediction apparatus, and trained model generation method

Anon-transitory computer-readable recording medium having stored therein a characteristic prediction program for causing a computer to perform:

• acquiring a trained model defining an association between a serial number and a characteristic, the serial number being based on a time-series arrangement of a plurality of first processes executed by a processing apparatus and based on an arrangement order of a plurality of wafers on which a first process is to be simultaneously executed in each of the plurality of first processes, the arrangement order of the plurality of wafers being determined in the processing apparatus, the processing apparatus being configured to arrange the plurality of wafers and simultaneously execute the first process, and the characteristic being measured in each of the plurality of wafers after a second process different from the first process is executed on the plurality of wafers on which the first process has been executed;
• and
• inputting, into the trained model, first serial numbers of a plurality of wafers in the first process executed by the processing apparatus and measured first characteristics corresponding to the first serial numbers to predict second characteristics corresponding to second serial numbers after the first serial numbers, wherein
• the trained model includes a time-series model using the serial number as a time series.

A characteristic prediction apparatus comprising:

• a processor;
• and
• a memory storing program instructions that cause the processor to:
• acquire a trained model defining an association between a serial number and a characteristic, the serial number being based on a time-series arrangement of a plurality of first processes executed by a processing apparatus and based on an arrangement order of a plurality of wafers on which a first process is to be simultaneously executed in each of the plurality of first processes, the arrangement order of the plurality of wafers being determined in the processing apparatus, the processing apparatus being configured to arrange the plurality of wafers and simultaneously execute the first process, and the characteristic being measured in each of the plurality of wafers after a second process different from the first process is executed on the plurality of wafers on which the first process has been executed;
• and
• input, into the trained model, first serial numbers of a plurality of wafers in the first process executed by the processing apparatus and measured first characteristics corresponding to the first serial numbers to predict second characteristics corresponding to second serial numbers after the first serial numbers, wherein the trained model includes a time-series model using the serial number as a time series.

A trained model generation method comprising:

• acquiring training data in which a serial number is associated with a characteristic, the serial number being based on a time-series arrangement of a plurality of first processes executed by a processing apparatus and based on an arrangement order of a plurality of wafers on which a first process is to be simultaneously executed in each of the plurality of first processes, the arrangement order of the plurality of wafers being determined in the processing apparatus, the processing apparatus being configured to arrange the plurality of wafers and simultaneously execute the first process, and the characteristic being measured in each of the plurality of wafers after a second process different from the first process is executed on the plurality of wafers on which the first process has been executed;
• and
• by performing machine learning on the training data, generating a trained model for predicting, by receiving an input of first serial numbers of a plurality of wafers in the first process executed by the processing apparatus and measured first characteristics corresponding to the first serial numbers, second characteristics corresponding to second serial numbers after the first serial numbers, wherein
• the trained model includes a time-series model using the serial number as a time series.