Journal of the Korean Society for Heat Treatment (열처리공학회지)

The Korean Society for Heat Treatment (한국열처리공학회)
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Predicting Oxynitrification layer using AI-based Varying Coefficient Regression model

AI 기반의 Varying Coefficient Regression 모델을 이용한 산질화층 예측

  • Hye Jung Park (Data Analysis and Research Team, P&S BigData Science Institute) ;
  • Joo Yong Shim (Data Analysis and Research Team, P&S BigData Science Institute) ;
  • Kyong Jun An (Heat & Surface Technology R&D Department, Korea Institute of Industrial Technology (KITECH)) ;
  • Chang Ha Hwang (Department of Artificial Intelligence Convergence, Dankook University) ;
  • Je Hyun Han (Department of Artificial Intelligence Convergence, Dankook University)
  • Received : 2023.11.02
  • Accepted : 2023.11.24
  • Published : 2023.11.30
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Abstract

This study develops and evaluates a deep learning model for predicting oxide and nitride layers based on plasma process data. We introduce a novel deep learning-based Varying Coefficient Regressor (VCR) by adapting the VCR, which previously relied on an existing unique function. This model is employed to forecast the oxide and nitride layers within the plasma. Through comparative experiments, the proposed VCR-based model exhibits superior performance compared to Long Short-Term Memory, Random Forest, and other methods, showcasing its excellence in predicting time series data. This study indicates the potential for advancing prediction models through deep learning in the domain of plasma processing and highlights its application prospects in industrial settings.

Keywords

Acknowledgement

본 논문은 한국생산기술연구원 기관주요사업 "Add-on 모듈 탑재를 통한 지능형 뿌리공정 기술개발(KITECH EO-22-0005)"의 지원으로 수행한 연구입니다.

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