Production schedule estimation method and system of semiconductor process

A production schedule estimation system of a semiconductor process, comprising:

• a storage device, configured to store a prediction model;
• and
• a processing device, coupled to the storage device and configured to recursively execute;
• obtaining a current-day work-in-process data, a cycle time data of a machine group, and a productivity data of the machine group, inputting 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 model, and calculating a current-day cycle time data and a current-day move volume for each of a plurality of stations in the machine group through the prediction model,
• wherein the current-day move data comprises a type of a work-in-process and a current-day move volume after the work-in-process has been processed by the plurality of stations in the machine group on the current day;
• calculating a current-day move data according to the current-day cycle time data and the current-day move volume for the each of the plurality of stations in the machine group;
• calculating a next-day work-in-process data according to the current-day cycle time data and the current-day move volume for the each of the plurality of stations in the machine group, and executing the prediction model, so as to obtain a product production time and a quantity of a product produced;
• summing a plurality of move volumes corresponding to each of the plurality of stations in a plurality of machine groups on different days generated according to above recursive execution to generate the quantity of the product produced and the product production time;
• estimating a production schedule according to the product production time and the quantity of the product produced and controlling the machine group based on the estimated production schedule;
• obtaining a work-in-process historical data and a machine historical data;
• performing a feature extraction on the work-in-process historical data and the machine historical data, so as to obtain a plurality of feature data;
• and
• inputting the plurality of feature data into a machine learning module, to enable the machine learning module to perform a prediction to output the cycle time data of the machine group and the productivity data of the machine group.