반도체 소재ㆍ부품 대학원
정창욱
부교수Changwook Jeong
Changwook Jeong
The Jeong Research Group
미래 반도체 소재/소자 개발을 위해 물리와 인공지능 모델을 적극 활용하며 주요 연구분야는 아래와 같습니다.
- 미래반도체 메모리 및 로직 소자 설계
· 2D, Oxide, Ferro 등 신물질 기반 소자 한계 연구
· 소자 성능 / 산포 / 신뢰성을 고려한 소자 설계
· 채널 / 컨택 / 절연막 동시 최적화 모델링
- 신개념 반도체 소재 및 물리 연구
· 신개념 구동 방식의 메모리 및 로직 동작 연구
· 나노 소자 내 에너지 변환 이해 및 극대화 연구
· 적층형 상변화 메모리: 물성 변화 메커니즘 등
- 인공지능-물리 융합 모델링
· 물질-소자-회로 연계 end-to-end 모델링 방법론
· 고정밀 원자수준 모델 및 TCAD 모델 개선 연구
· 인공 시냅스 소자 특성을 이용한 연산 모델 개발
The Jeong Group explores the physics and technology of nanoelectronic devices and is interested in working at the junction between physical modeling and machine learning in order to fully exploit their combined strengths to address many engineering problems with competing multi-objectives and of such large scale and complexity.
We are an interdisciplinary research team with experimentalists and theorists to verify our model and work on a series of research topics in the semiconductor domain for the next 20 years. Current work focuses on developing and applying novel approaches to explore new ideas for emerging materials and devices for many applications such as electronic, energy conversion, optoelectronic, etc., and to better design and manufacture semiconductor materials and devices in complex combinatorial search spaces.
The Jeong Group explores the physics and technology of nanoelectronic devices and is interested in working at the junction between physical modeling and machine learning in order to fully exploit their combined strengths to address many engineering problems with competing multi-objectives and of such large scale and complexity.
We are an interdisciplinary research team with experimentalists and theorists to verify our model and work on a series of research topics in the semiconductor domain for the next 20 years. Current work focuses on developing and applying novel approaches to explore new ideas for emerging materials and devices for many applications such as electronic, energy conversion, optoelectronic, etc., and to better design and manufacture semiconductor materials and devices in complex combinatorial search spaces.
미래반도체 소자 설계, 신개념 반도체 소재/소자 모델링(Design of advanced nanoelectronics from atom to system based on physics and deep learning model)
Design of advanced nanoelectronics from atom to system based on physics and deep learning model
인공지능-물리 융합 반도체 모델링 기술(Novel simulation methodology with physics-deep learning hybrid approach)
Novel simulation methodology with physics-deep learning hybrid approach
국가과학기술표준분류
ED. 전기/전자 > ED04. 반도체소자·시스템 > ED0499. 달리 분류되지 않는 반도체소자/시스템