Convolutional Neural Networks for Automatic Virtual Metrology

Yu Ming Hsieh, Tan Ju Wang, Chin Yi Lin, Li Hsuan Peng, Fan Tien Cheng, Sui Yan Shang

Research output: Contribution to journalArticlepeer-review

Abstract

To ensure stable manufacturing and high yield of production, factories (e.g., semiconductor or TFT-LCD fabs) conduct quality inspection on workpieces. They tend to adopt sampling inspection in consideration of reducing cost and cycle time, yet that fails to achieve real-time and online total inspection because of the sampling strategy and metrology delay. Automatic Virtual Metrology (AVM) is the best solution to tackle the problem mentioned above, due to the fact that it can convert sampling inspection with metrology delay into on-line and real-time total inspection. However, with the advancement of science and technology, the processes become more and more sophisticated, and the requirement for the accuracy of virtual metrology becomes higher. The current AVM prediction algorithm is the traditional machine learning method, Back-Propagation Neural Networks (BPNN). However, even if the amount of data in this method increases, the performance improvement has its limits, and it requires a strict and time-consuming feature selection process. To improve the prediction accuracy, this work proposes the deep learning method, Convolutional Neural Networks (CNN), for the AVM server. The accuracy of CNN improves as the amount of data grows. In other words, if there are sufficient data, the current accuracy limit of machine learning can be enhanced. Experimental results reveal that CNN can automatically extract highly informative features from the data and improves the original AVM accuracy.

Original languageEnglish
Article number9444197
Pages (from-to)5720-5727
Number of pages8
JournalIEEE Robotics and Automation Letters
Volume6
Issue number3
DOIs
Publication statusPublished - 2021 Jul

All Science Journal Classification (ASJC) codes

  • Control and Systems Engineering
  • Biomedical Engineering
  • Human-Computer Interaction
  • Mechanical Engineering
  • Computer Vision and Pattern Recognition
  • Computer Science Applications
  • Control and Optimization
  • Artificial Intelligence

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