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신경망을 이용한 SiN 박막 표면거칠기에의 이온에너지 영향 모델링
김병환(Byungwhan Kim),이주공(Joo Kong Lee) 한국표면공학회 2010 한국표면공학회지 Vol.43 No.3
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 SiH₄?NH₄ 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.
광반사분광기와 시계열 신경망 모델을 이용한 웨이퍼 간 플라즈마 챔버 누출 감시
김병환(Byungwhan Kim),권민지(Minji Kwon),김대현(Daehyun Kim),정재훈(Jaehoon Jung),우봉주(Bongju Woo) 대한전기학회 2010 정보 및 제어 심포지엄 논문집 Vol.2010 No.4
신경망과 CUSUM 제어차트를 이용하여 챔버 누출을 감시하는 기법을 보고한다. 챔버 누출 데이터는 광반사분광기를 이용하여 수집하였다. 학습된 모델의 예측성능은 0.48%로 매우 정확하였다. 모델의 감시 성능은 24개의 정상과 4개의 비정상 데이터로 평가하였다. 비정상 데이터에 대한 모델의 예측에러는 정상에 비해 확연히 구분될 정도로 큰 값이었으며, 또한 해당되는 CUSUM 고장 믿음치도 큰 값이어서 챔버 누출을 정확하게 탐지할 수 있었다.
레이디얼 베이시스 함수망을 이용한 플라즈마 식각공정 모델링
박경영,김병환,Park, Kyoungyoung,Kim, Byungwhan 한국전기전자재료학회 2005 전기전자재료학회논문지 Vol.18 No.1
A new model of plasma etch process was constructed by using a radial basis function network (RBFN). This technique was applied to an etching of silicon carbide films in a NF$_3$ inductively coupled plasma. Experimental data to train RBFN were systematically collected by means of a 2$^4$ full factorial experiment. Appropriateness of prediction models was tested with test data consisted of 16 experiments not pertaining to the training data. Prediction performance was optimized with variations in three training factors, the number of pattern units, width of radial basis function, and initial weight distribution between the pattern and output layers. The etch responses to model were an etch rate and a surface roughness measured by atomic force microscopy. Optimized models had the root mean-squared errors of 26.1 nm/min and 0.103 nm for the etch rate and surface roughness, respectively. Compared to statistical regression models, RBFN models demonstrated an improvement of more than 20 % and 50 % for the etch rate and surface roughness, respectively. It is therefore expected that RBFN can be effectively used to construct prediction models of plasma processes.
웨이블렛과 신경망을 이용한 플라즈마-유도 X-Ray Photoelectron Spectroscopy 고장 패턴의 인식
김수연(Sooyoun Kim),김병환(Byungwhan Kim) 대한전기학회 2006 정보 및 제어 심포지엄 논문집 Vol.2006 No.1
To improve device yield and throughput. faults in plasma processing equipment should be quickly and accurately diagnosed. Despite many useful information of ex-situ sensor measurements. their applications to recognize plasma faultshave not been investigated. In this study, a new technique to identify fault causes by recognizing X-ray photoelectron spectroscopy (XPS) using neural network and continuous wavelet transformation (CWT). The presented technique was evaluated with the plasma etch data. A totalof 17 experiments were conducted for model construction. Model performance was investigated from the perspectives of training error, testing error, and recognition accuracy with respect to various thresholds. CWT-based BPNN models demonstrated a higher prediction accuracy of about 26%. Their advantages over pure XPS-based models were conspicuous in all three measures at small networks.
김수연(Suyeon Kim),김병환(Byungwhan Kim) 대한전기학회 2007 대한전기학회 학술대회 논문집 Vol.2007 No.10
Radial Basis Function Network (RBFN)을 이용하여 플라즈마 전위의 예측 모델을 개발하였다. RBFN의 예측성능은 Genetic Algorithm (GA)를 이용하여 최적화 하였다. 체계적인 모델링을 위해 통계적인 실험계획법이 적용되었으며, 실험은 반구형 유도 결합형 플라즈마 장비를 이용하여 수행이 되었다. Cl₂ 플라즈마에서의 데이터 측정에는 Langmuir probe가 이용되었다. 최적화된 GA-RBFN모델을 일반 RBFN모델과 비교하였으며, 15%정도 모델의 예측성능을 향상시켰다.