Scheduling semiconductor manufacturing processes to enhance system identification

Abstract

Semiconductor manufacturing is characterized by a manufacturing line with multiple processing tools, products, and other sources of variation. Run-to-run control applications need a constant stream of information about the state of the process in order to perform well. The trace of the state error covariance matrix from the Kalman filter is used as a metric for determining the information content of a particular data set to the run-to-run control algorithm. Processing decisions such as batch scheduling, equipment allocation, and sampling plans are shown to have an effect on estimator performance. Algorithms using the state error covariance matrix are developed that can optimize the factory schedule in order to provide run-to-run control algorithms with the best possible information. Simulation results demonstrate that measurable improvements in state estimation and control output performance can be achieved by using information from the process estimator.