Methodology for Important Sensor Screening for Fault Detection and Classification in Semiconductor Manufacturing
| Autor: | Li Fei, Marcella Miller, Feng Zhu, Jay Lee, Yinglu Wang, Xiang Li, Xiaodong Jia |
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| Rok vydání: | 2021 |
| Předmět: |
Computer science
Feature extraction Feature selection Condensed Matter Physics computer.software_genre Industrial and Manufacturing Engineering Kernel principal component analysis Fault detection and isolation Electronic Optical and Magnetic Materials Visualization Support vector machine Kernel (linear algebra) Redundancy (engineering) Data mining Electrical and Electronic Engineering computer |
| Zdroj: | IEEE Transactions on Semiconductor Manufacturing. 34:65-73 |
| ISSN: | 1558-2345 0894-6507 |
| Popis: | Feature design and selection is challenging because of huge data volume and high-mix production systems. Most engineers still rely on human experts to suggest the specific sensor channel and specific time frames of data from which to design the features. This study proposes a novel approach for important sensor screening to prioritize the useful sensor channels for FDC model development in semiconductor manufacturing. The proposed method can be used as a pre-processing step prior to feature extraction, and the selected sensor channels can be leveraged by process engineers for finer feature design. In this research, firstly, time series alignment kernels (TSAKs) are proposed to handle multivariate trace data. Then, the proposed method combines 5 different time series alignment kernels (TSAKs) with a feature selection algorithm, minimum Redundancy Maximum Relevance (mRMR), to identify the important sensor channels. Furthermore, a TSAK+Kernel Principal Component Analysis (KPCA) algorithm is proposed for a visualization tool. Lastly, the TSAK+Support Vector Machine (SVM) is employed for results validation. In this study, validation of the proposed method is based on both open-source datasets and the proprietary datasets from a real production line. |
| Databáze: | OpenAIRE |
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