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An Autoencoder-Based Approach for Fault Detection in Multi-Stage Manufacturing: A Sputter Deposition and Rapid Thermal Processing Case Study.
- Source :
- IEEE Transactions on Semiconductor Manufacturing; May2022, Vol. 35 Issue 2, p166-173, 8p
- Publication Year :
- 2022
-
Abstract
- Data-driven Fault Detection and Classification approaches are becoming increasingly important in semiconductor manufacturing and in other industries aiming at implementing the Zero-defect paradigm. Two of the main challenges in developing such solutions are: (i) the complexity of sensor data, that typically presents themselves in the form of time-series, requiring the employment of time-consuming and possibly sub-optimal feature extraction approaches; (ii) the fact that faults/defects may be caused by more than a single process, but in many cases they are generated by a cascade of processes. In this paper, we tackle the first issue, by considering a two-stage case study consisting of a deposition process and a rapid thermal process. The proposed approach is based on convolutional deep autoencoders employed to perform feature extraction from time-series sensor data in frontend production equipment. We will show on the reported case study, how the proposed approach outperfoms key numbers-based approaches typically used in the industry. To allow reproducibility of the reported results and to foster research in the field, we publicly share the data used in this work. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 08946507
- Volume :
- 35
- Issue :
- 2
- Database :
- Complementary Index
- Journal :
- IEEE Transactions on Semiconductor Manufacturing
- Publication Type :
- Academic Journal
- Accession number :
- 156741937
- Full Text :
- https://doi.org/10.1109/TSM.2022.3146988