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

研究成果: Article同行評審

11 引文 斯高帕斯(Scopus)


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.

頁(從 - 到)5720-5727
期刊IEEE Robotics and Automation Letters
出版狀態Published - 2021 7月

All Science Journal Classification (ASJC) codes

  • 控制與系統工程
  • 生物醫學工程
  • 人機介面
  • 機械工業
  • 電腦視覺和模式識別
  • 電腦科學應用
  • 控制和優化
  • 人工智慧


深入研究「Convolutional Neural Networks for Automatic Virtual Metrology」主題。共同形成了獨特的指紋。