Many studies on the prediction of manufacturing results using sensor signals have been conducted in the field of fault detection and classification (FDC) for semiconductor manufacturing processes. However, fault diagnosis used to find clues as to root causes remains a challenging area. In particular, process monitoring using neural networks has been employed to only a limited extent because it is a black box model, making the relationships between input data and output results difficult to interpret in actual manufacturing settings, despite its high classification performance. In this paper, we propose a convolutional neural network (CNN) model, named FDC-CNN, in which a receptive field tailored to multivariate sensor signals slides along the time axis, to extract fault features. This approach enables the association of the output of the first convolutional layer with the structural meaning of the raw data, making it possible to locate the variable and time information that represents process faults. In an experiment on a chemical vapor deposition process, the proposed method outperformed other deep learning models.
Bibliographical noteFunding Information:
This work was supported in part by the Global Ph.D. Fellowship Program through the National Research Foundation of Korea (NRF) under Grant NRF-2015H1A2A1031081, in part by the Technology Innovation Program (Development of Big Data-Based Analysis and Control Platform for Semiconductor Manufacturing Plants) through the Ministry of Trade, Industry and Energy, South Korea, under Grant 10045913, and in part by the NRF through the Ministry of Science, ICT and Future Planning, South Korea, under Grant NRF-2016R1A2B4008337.
© 2017 IEEE.
All Science Journal Classification (ASJC) codes
- Electronic, Optical and Magnetic Materials
- Condensed Matter Physics
- Industrial and Manufacturing Engineering
- Electrical and Electronic Engineering