인문학
사회과학
자연과학
공학
의약학
농수해양학
예술체육학
복합학
DBpia AI
AI 에이전트
AI 아이디어
AI 리더
AI 뷰어
Citeasy
크롬 서지관리 다운로드
지원사업
학술연구/단체지원/교육 등 연구자 활동을 지속하도록 DBpia가 지원하고 있어요.
커뮤니티
연구자들이 자신의 연구와 전문성을 널리 알리고, 새로운 협력의 기회를 만들 수 있는 네트워킹 공간이에요.
논문 기본 정보
- 저자정보
피인용 0
초록·키워드
This study presents a novel application of deep learning algorithms to enhance the modeling of 14 nm FinFETs, specifically addressing the critical aspect of material discovery. While Density Functional Theory (DFT) is essential for material characterization, its computational intensity and time-consuming nature pose significant limitations. Similarly, shallow machine learning (ML) methods, despite their utility, often struggle with extensive data preprocessing, overfitting, and inherent biases. Our approach overcomes these challenges by integrating advanced data processing with deep learning for material discovery, specifically tailored for advanced node FinFET
modeling.We meticulously prepared material descriptors and demonstrated the superior performance of deep learning in this context. With minimal fine-tuning, our deep learning model achieved a Mean Absolute Error (MAE) of approximately 0.14 eV. This performance significantly surpasses that of traditional shallow learning methods, including Support Vector Regression, Random Forest, and Extreme Gradient Boosting (XGBoost), as evidenced by a higher R² score. These results underscore the exceptional proficiency of deep learning in accelerating material discovery and, consequently, improving the accuracy of advanced node FinFET modeling. This research highlights the profound efficiency of deep learning in pushing the boundaries of semiconductor device simulation.
modeling.We meticulously prepared material descriptors and demonstrated the superior performance of deep learning in this context. With minimal fine-tuning, our deep learning model achieved a Mean Absolute Error (MAE) of approximately 0.14 eV. This performance significantly surpasses that of traditional shallow learning methods, including Support Vector Regression, Random Forest, and Extreme Gradient Boosting (XGBoost), as evidenced by a higher R² score. These results underscore the exceptional proficiency of deep learning in accelerating material discovery and, consequently, improving the accuracy of advanced node FinFET modeling. This research highlights the profound efficiency of deep learning in pushing the boundaries of semiconductor device simulation.
본문·목차
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