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Denoised Residual Trace Analysis for Monitoring Semiconductor Process Faults by Jaeyeon Jang; Byung Wook Min; Chang Ouk Kim is a Engineering article available to read on EtoBox.

What is Denoised Residual Trace Analysis for Monitoring Semiconductor Process Faults about?

The detection of wafer faults in early process steps through monitoring and analyzing multivariate process trace data contribute to wafer yield improvements. Standard classification algorithms have been generally used for fault detection and classification (FDC). However, this approach can cause information loss while extracting statistical features from the trace data and cannot consider class imbalance situations where much fewer faulty wafers are generated than normal wafers. In addition, the approach does not consider normal wafer-to-wafer (W2W) variations and sensor noise inherent in the trace data. These drawbacks significantly degrade FDC performance. This paper proposes a method that builds an FDC model only with trace data of normal wafers in which W2W variations and sensor noise exist. The one-class FDC method detects the occurrence of abnormal trace patterns that cause wafer faults by removing W2W variations and sensor noise from raw traces by using denoising autoencoders, and this method finds the faultintroducing process parameters with the occurrence times. In experiments using the trace data of etch and chemical vapor deposition processes, the proposed method exhibit

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Author

Jaeyeon Jang; Byung Wook Min; Chang Ouk Kim

Publisher

IEEE; Institute of Electrical and Electronics Engineers; Institute of Electrical and Electronics Engineers (IEEE) (ISSN 0894-6507)

Published

2019

Language

EN

Field

Engineering (Physical Sciences)