Virtual Sensor Based Fault Detection and Classification on a Plasma Etch Reactor

Abstract

The SEMATECH sponsored J-88-E project teaming Texas Instruments with NeuroDyne (et al.) focused on Fault Detection and Classification (FDC) on a Lam 9600 aluminum plasma etch reactor, used in the process of semiconductor fabrication. Fault classification was accomplished by implementing a series of virtual sensor models which used data from real sensors (Lam Station sensors, Optical Emission Spectroscopy, and RF Monitoring) to predict recipe setpoints and wafer state characteristics. Fault detection and classification were performed by comparing predicted recipe and wafer state values with expected values. Models utilized include linear PLS, Polynomial PLS, and Neural Network PLS. Prediction of recipe setpoints based upon sensor data provides a capability for cross-checking that the machine is maintaining the desired setpoints. Wafer state characteristics such as Line Width Reduction and Remaining Oxide were estimated on-line using these same process sensors (Lam, OES, RFM). Wafer-to-wafer measurement of these characteristics in a production setting (where typically this information may be only sparsely available, if at all, after batch processing runs with numerous wafers have been completed) would provide important information to the operator that the process is or is not producing wafers within acceptable bounds of product quality. Production yield is increased, and correspondingly per unit cost is reduced, by providing the operator with the opportunity to adjust the process or machine before etching more wafers.