Hidden Markov Models for Endpoint Detection in Plasma Etch Processes

We investigate two statistical detection problems in plasma etch endpoint detection: change-point detection and pattern matching. Our approach is based on a segmental semi-Markov model framework. In the change-point detection problem, the change-point corresponds to state switching in the model. For pattern matching, the pattern is approximated as a sequence of linear segments that are modeled as segments (states) in the model. The segmental semi-Markov model is an extension of the standard hidden Markov model (HMM), from which learning and inference algorithms are presented to solve the problems of change-point detection and pattern matching in a Bayesian framework. Results on both simulated data and real data from semiconductor manufacturing illustrate the flexibility and accuracy of the proposed framework. 200 210 220 230 240 250 260 4500 5000 5500 6000 6500 7000 Time (s) In te ns ity (a .u .) Figure 1: An illustrative example of a change-point detection problem from plasma etching. Data are from a commercial LAM 9400 plasma etch machine.