Journal of the Korean institute of surface engineering (한국표면공학회지)

The Korean Institute of Surface Engineering (한국표면공학회)
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Neural Network Modeling of Ion Energy Impact on Surface Roughness of SiN Thin Films

신경망을 이용한 SiN 박막 표면거칠기에의 이온에너지 영향 모델링

  • Kim, Byung-Whan (Department of Electronic Engineering, Sejong University) ;
  • Lee, Joo-Kong (Department of Information and Communication, Sejong University)
  • 김병환 (세종대학교 전자공학과) ;
  • 이주공 (세종대학교 정보통신공학과)
  • Received : 2010.06.16
  • Accepted : 2010.06.29
  • Published : 2010.06.30
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Abstract

Surface roughness of deposited or etched film strongly depends on ion bombardment. Relationships between ion bombardment variables and surface roughness are too complicated to model analytically. To overcome this, an empirical neural network model was constructed and applied to a deposition process of silicon nitride (SiN) films. The films were deposited by using a pulsed plasma enhanced chemical vapor deposition system in $SiH_4$-$NH_4$ plasma. Radio frequency source power and duty ratio were varied in the range of 200-800 W and 40-100%. A total of 20 experiments were conducted. A non-invasive ion energy analyzer was used to collect ion energy distribution. The diagnostic variables examined include high (or) low ion energy and high (or low) ion energy flux. Mean surface roughness was measured by using atomic force microscopy. A neural network model relating the diagnostic variables to the surface roughness was constructed and its prediction performance was optimized by using a genetic algorithm. The optimized model yielded an improved performance of about 58% over statistical regression model. The model revealed very interesting features useful for optimization of surface roughness. This includes a reduction in surface roughness either by an increase in ion energy flux at lower ion energy or by an increase in higher ion energy at lower ion energy flux.

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References

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