반도체디스플레이기술학회지 (Journal of the Semiconductor & Display Technology)

한국반도체디스플레이기술학회 (The Korean Society Of Semiconductor & Display Technology)
권호 표지

Modeling with Thin Film Thickness using Machine Learning

  • Kim, Dong Hwan (Department of Electronics Engineering, Myongji University) ;
  • Choi, Jeong Eun (Department of Electronics Engineering, Myongji University) ;
  • Ha, Tae Min (Department of Electronics Engineering, Myongji University) ;
  • Hong, Sang Jeen (Department of Electronics Engineering, Myongji University)
  • 투고 : 2019.06.08
  • 심사 : 2019.06.24
  • 발행 : 2019.06.30
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초록

Virtual metrology, which is one of APC techniques, is a method to predict characteristics of manufactured films using machine learning with saving time and resources. As the photoresist is no longer a mask material for use in high aspect ratios as the CD is reduced, hard mask is introduced to solve such problems. Among many types of hard mask materials, amorphous carbon layer(ACL) is widely investigated due to its advantages of high etch selectivity than conventional photoresist, high optical transmittance, easy deposition process, and removability by oxygen plasma. In this study, VM using different machine learning algorithms is applied to predict the thickness of ACL and trained models are evaluated which model shows best prediction performance. ACL specimens are deposited by plasma enhanced chemical vapor deposition(PECVD) with four different process parameters(Pressure, RF power, $C_3H_6$ gas flow, $N_2$ gas flow). Gradient boosting regression(GBR) algorithm, random forest regression(RFR) algorithm, and neural network(NN) are selected for modeling. The model using gradient boosting algorithm shows most proper performance with higher R-squared value. A model for predicting the thickness of the ACL film within the abovementioned conditions has been successfully constructed.

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참고문헌

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