Artificial neural network based detection and diagnosis of plasma-etch faults

Abstract

The plasma-etch process is one of many steps in the fabrication of semiconductor wafers. Currently, fault-detection/diagnosis for this process is done primarily by visual inspection of graphically displayed process data. By observing these data, experienced technicians can detect and classify many types of faults. The tediousness and intrinsic human unreliability of this method, as well as the high cost of mistakes, makes automation attractive. In this paper, five artificial neural network approaches for detecting and diagnosing four common plasma-etch fault conditions are examined. The data used for training and testing the networks were collected during a 162 day period, in which over 46,000 wafers were etched. The best accuracy achieved in this study is approximately 98.7% correct fault-detection for the four fault types, 100% correct fault classification, and a 2.3% false alarm rate. The five neural-based approaches are described in detail, and results are given for each approach.