Forecasting flow time in semiconductor manufacturing using knowledge discovery in databases

Semiconductor manufacturing is characterised by a complex production process, advanced equipment, and volatile demand. Flow time (FT), noted cycle time in semiconductor manufacturing, is a key measure in the operations. This study develops FT forecasting models using knowledge discovery in databases. It follows cross industry standards for data mining, with the focus on business understanding, data pre-processing and classification techniques. The data include wafer lot transactions extracted from the manufacturing execution system of an 8-inch flash memory factory. The FT is forecasted for a single lot at a given production step. The models are constructed using 70% of the data and the rest 30% for their evaluation. The results illustrate that a decision tree model achieves 76.7% accuracy and a neural network model 88.2%. The novelty of this work is in a thorough understanding of operations, a single data source, and common classification techniques used to obtain high accuracy results. The models can generate FT forecasting for a single production step, a line segment or a complete line. They can be used to improve short term planning, overall operations and supply chain efficiency, via shift scheduling, labour and materials requirements planning, inventory management and delivery schedules.