Improved Plasma Etch Endpoint Detection Using Attention-Based Long Short-Term Memory Machine Learning

Author:

Kim Ye Jin1,Song Jung Ho2,Cho Ki Hwan2,Shin Jong Hyeon2,Kim Jong Sik2ORCID,Yoon Jung Sik2,Hong Sang Jeen1ORCID

Affiliation:

1. Department of Semiconductor Engineering, Myongji University, Yongin 17058, Republic of Korea

2. Plasma E.I. Convergence Research Center, Korea Research Institute of Fusion Energy, Daejeon 34133, Republic of Korea

Abstract

Existing etch endpoint detection (EPD) methods, primarily based on single wavelengths, have limitations, such as low signal-to-noise ratios and the inability to consider the long-term dependencies of time series data. To address these issues, this study proposes a context of time series data using long short-term memory (LSTM), a kind of recurrent neural network (RNN). The proposed method is based on the time series data collected through optical emission spectroscopy (OES) data during the SiO2 etching process. After training the LSTM model, the proposed method demonstrated the ability to detect the etch endpoint more accurately than existing methods by considering the entire time series. The LSTM model achieved an accuracy of 97.1% in a given condition, which shows that considering the flow and context of time series data can significantly reduce the false detection rate. To improve the performance of the proposed LSTM model, we created an attention-based LSTM model and confirmed that the model accuracy is 98.2%, and the performance is improved compared to that of the existing LSTM model.

Funder

National Research Council of Science & Technology

Plasma E.I. Conversion Research Center

Publisher

MDPI AG

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