Fault detection of reactive ion etching using time series neural networks

  • Authors:

  • Kyung-Han Ryu;Song-Jae Lee;Jaehyun Park;Dong-Chul Park;Sang J. Hong

  • Affiliations:

  • Department of Electronic Engineering, Myongji University, Yongin, Korea;Department of Information Engineering, Myongji University, Yongin, Korea;Department of Electronic Engineering, Myongji University, Yongin, Korea;Department of Information Engineering, Myongji University, Yongin, Korea;Department of Electronic Engineering & Nano-Bio Research Center, Myongji University, Yongin, Korea

  • Venue:
  • ISNN'06 Proceedings of the Third international conference on Advances in Neural Networks - Volume Part III
  • Year:
  • 2006

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Abstract

Maximizing the productivity in semiconductor manufacturing, early detection of process and/or equipment abnormality. Since most of the key processes in semiconductor production are performed under extremely high vacuum condition, no other action can be taken unless the undergoing process is terminated. In this paper, time series based neural networks have been employed to assist the decision for determining potential process fault in real-time. Principal component analysis (PCA) for the dimensionality reduction of the data is first performed to handle smoothly in real-time environment. According to the PCA, 11 system parameters were selected, and each of them were then classified using modeled and tested in time series. Successful detection on different types of process shift (or fault) was achieved with 0% false alarm.