A Phase Space Reconstruction Based Approach to Throughput Prediction in Semiconductor Wafer Fabrication System

In order to manage and control semiconductor wafer fabrication system (SWFS) more effectively, the daily throughput prediction data of wafer fab are often used in the planning and scheduling of SWFS. In this paper, an artificial neural network (ANN) prediction method based on phase space reconstruction (PSR) and ant colony optimization (ACO) is presented, in which the phase space reconstruction theory is used to reconstruct the daily throughput time series, the ANN is used to construct the daily throughput prediction model, and the ACO is used to train the connection weight and bias values of the neural network prediction model. Testing with factory operation data and comparing with the traditional method show that the proposed methodology is effective.