Journal of the Semiconductor & Display Technology (반도체디스플레이기술학회지)

The Korean Society Of Semiconductor & Display Technology (한국반도체디스플레이기술학회)
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Verified Deep Learning-based Model Research for Improved Uniformity of Sputtered Metal Thin Films

스퍼터 금속 박막 균일도 예측을 위한 딥러닝 기반 모델 검증 연구

  • Received : 2023.03.08
  • Accepted : 2023.03.16
  • Published : 2023.03.31
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Abstract

As sputter equipment becomes more complex, it becomes increasingly difficult to understand the parameters that affect the thickness uniformity of thin metal film deposited by sputter. To address this issue, we verified a deep learning model that can predict complex relationships. Specifically, we trained the model to predict the height of 36 magnets based on the thickness of the material, using Support Vector Machine (SVM), Multilayer Perceptron (MLP), 1D-Convolutional Neural Network (1D-CNN), and 2D-Convolutional Neural Network (2D-CNN) algorithms. After evaluating each model, we found that the MLP model exhibited the best performance, especially when the dataset was constructed regardless of the thin film material. In conclusion, our study suggests that it is possible to predict the sputter equipment source using film thickness data through a deep learning model, which makes it easier to understand the relationship between film thickness and sputter equipment.

Keywords

Acknowledgement

이 논문은 2022년 정부(산업통상자원부)와 한국산업기술평가관리원의 소재부품기술개발사업(No. 20017354)으로 수행된 연구 결과입니다.

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