Machine learning-based novelty detection for faulty wafer detection in semiconductor manufacturing

  • Authors:

  • Dongil Kim;Pilsung Kang;Sungzoon Cho;Hyoung-joo Lee;Seungyong Doh

  • Affiliations:

  • Seoul National University, 599 Gwanangno, Gwanak-gu, Seoul 151-744, Republic of Korea;Information Technology Management Programme, International Fusion School, Seoul National University of Science & Technology (SeoulTech), 232 Gongreungno, Nowon-gu, Seoul, 139-743, Republic of Kore ...;Seoul National University, 599 Gwanangno, Gwanak-gu, Seoul 151-744, Republic of Korea;Department of Engineering Science, University of Oxford, Oxford OX1 3PJ, UK;Samsung Electronics Co. Ltd., 416 Maetan-dong, Yeongtong-gu, Suwon, Gyeonggi-do, Republic of Korea

  • Venue:
  • Expert Systems with Applications: An International Journal
  • Year:
  • 2012

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Abstract

Since semiconductor manufacturing consists of hundreds of processes, a faulty wafer detection system, which allows for earlier detection of faulty wafers, is required. statistical process control (SPC) and virtual metrology (VM) have been used to detect faulty wafers. However, there are some limitations in that SPC requires linear, unimodal and single variable data and VM underestimates the deviations of predictors. In this paper, seven different machine learning-based novelty detection methods were employed to detect faulty wafers. The models were trained with Fault Detection and Classification (FDC) data to detect wafers having faulty metrology values. The real world semiconductor manufacturing data collected from a semiconductor fab were tested. Since the real world data have more than 150 input variables, we employed three different dimensionality reduction methods. The experimental results showed a high True Positive Rate (TPR). These results are promising enough to warrant further study.