Future Semiconductor Technology

미래 반도체 연구실

관련기사 바로가기
미래 반도체 연구실

The Future Semiconductor Lab aims to contribute to the industrialization of the developed technol￾ogy and source research through research on the source technology related to More Moore and More Than Moore, as well as technology support for semiconductor materials/parts/equipment.
o More Moore
- Research and development of high dielectric materials and low dielectric materials
- Research on extreme scaling of semiconductor devices using 2D materials
- Research on stacked next-generation semiconductor devices
o More Than Moore
- Research on next-generation Memristor devices
- Research on Memristor-based AI Chip
o Semiconductor material/part/equipment technical support
- Contribute to the sustainable development of the semiconductor industry by localizing
semiconductor materials and supporting original technology
- Test bed construction and technical support
미래 반도체 연구실은 More Moore와 More Than Moore 관련 원천 기술 연구 그리고 반도체 소재/부품/
장비 기술 지원 연구를 통한 원천 연구와 개발된 기술의 산업화에 기여하는 것이 목표임
o More Moore - 고유전 소재, 저유전 소재 연구 개발
- 2D 소재를 이용한 반도체 소자의 극한 Scaling 연구
- 적층형 차세대 반도체 소자 연구
o More Than Moore
- 차세대 Memristor 소자 연구
- Memristor 기반 AI Chip 연구
o 반도체 소재/부품/장비 기술 지원
- 반도체 소재 국산화 및 원천 기술 지원으로 반도체 산업의 지속 발전에 기여
- Test BED 구축 및 기술 지원

Major research field

Semiconductor, Memristor, Artificial intelligence device

Desired field of research

Semiconductor material/device/design convergence research, Construction of future semiconductor research platform

Research Keywords and Topics

• Memory device and semiconductor process
• New memory (STT-MRAM / PCM) research and development
• AI device and energy efficient computing research

Research Publications
MORE

• Nature Communications, “Spontaneous sparse learning for PCM-based memristor neural networks” , Dong-Hyeok Lim, Shuang Wu, Rong Zhao, Jung-Hoon Lee, Hongsik Jeong & Luping Shi / 2021-1
• IEEE TRANSACTIONS ON ELECTRON DEVICES, “Exploring Cycle-to-Cycle and Device￾to-Device Variation Tolerance in MLC Storage-Based Neural Network Training” , Lee Jung-Hoon, Jeong Hongsik, Lim Dong-Hyeok, Ma Huimin, Shi Luping / 2019-05
• JOURNAL OF PHYSICS D-APPLIED PHYSICS, “Memristor devices for neural networks”, Jeong Hongsik, Shi Luping / 2019-01

Patents

• Artificial neuron semiconductor element having three-dimensional structure and artificial neuron semiconductor system using same / US10014348B2 / Hongsik Jeong et. al
• Variable resistance memory device and a variable resistance memory system including the same / US 9230642B2 / Hongsik Jeong et. al.

국가과학기술표준분류

  • ED. 전기/전자
  • ED04. 반도체소자·시스템
  • ED0401. Si 소자

국가기술지도분류

  • 정보-지식-지능화 사회 구현
  • 010400. 반도체/나노 신소자 기술

녹색기술분류

  • 고효율화기술
  • 전력효율성 향상
  • 323. 그린 IT기술

6T분류

  • IT 분야
  • 핵심부품
  • 010114. 고밀도 정보저장장치 기술