Journal of the Semiconductor & Display Technology (반도체디스플레이기술학회지)

The Korean Society Of Semiconductor & Display Technology (한국반도체디스플레이기술학회)
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Neural Network-based Time Series Modeling of Optical Emission Spectroscopy Data for Fault Prediction in Reactive Ion Etching

  • Sang Jeen Hong (Department of Semiconductor Engineering, Myongji University)
  • Received : 2023.11.30
  • Accepted : 2023.12.18
  • Published : 2023.12.31
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Abstract

Neural network-based time series models called time series neural networks (TSNNs) are trained by the error backpropagation algorithm and used to predict process shifts of parameters such as gas flow, RF power, and chamber pressure in reactive ion etching (RIE). The training data consists of process conditions, as well as principal components (PCs) of optical emission spectroscopy (OES) data collected in-situ. Data are generated during the etching of benzocyclobutene (BCB) in a SF6/O2 plasma. Combinations of baseline and faulty responses for each process parameter are simulated, and a moving average of TSNN predictions successfully identifies process shifts in the recipe parameters for various degrees of faults.

Keywords

Acknowledgement

This work was supported by Korean Evaluation Institute of Industrial Technology (G01002665681).

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