The Jeong Research Group investigates the physics and technology of nanoelectronic devices. The group focuses on the intersection of physical modeling and machine learning to address complex engineering problems. This interdisciplinary team of experimentalists and theorists is developing and applying novel approaches to explore emerging materials and devices for applications in electronics, energy conversion, and optoelectronics. Research also aims to improve the design and manufacture of semiconductor materials and devices.

정 연구 그룹은 나노 전자 장치의 물리학 및 기술을 연구한다. 물리학적 모델링과 머신 러닝의 융합을 통해 복잡한 엔지니어링 문제 해결에 집중하며, 전자, 에너지 변환, 광전자 분야에 적용될 새로운 소재와 장치 개발을 목표로 한다. 또한, 복합적인 조합 탐색 공간에서 반도체 재료 및 장치의 설계 및 제조 개선을 추구한다.

Ml-driven compact models for rrams: addressing variability and simulation efficiency

Deep learning for semiconductor materials and devices design

Realistic and Scalable TCAD for Yield-Aware Full-Chip DTCO

Pac-net: A model pruning approach to inductive transfer learning

Context-Informed Neural ODEs Unexpectedly Identify Broken Symmetries: Insights from the Poincaré–Hopf Theorem

Isometric quotient variational auto-encoders for structure-preserving representation learning

Highly reliable 50nm contact cell technology for 256Mb PRAM

Highly manufacturable high density phase change memory of 64Mb and beyond

Full integration of highly manufacturable 512Mb PRAM based on 90nm technology

Co‐percolating graphene‐wrapped silver nanowire network for high performance, highly stable, transparent conducting electrodes

Thermal conductivity of bulk and thin-film silicon: A Landauer approach

On Landauer versus Boltzmann and full band versus effective mass evaluation of thermoelectric transport coefficients

Switching current scaling and reliability evaluation in PRAM

Artificial neural network-based compact modeling methodology for advanced transistors

Near-equilibrium transport: fundamentals and applications

Prospects for nanowire-doped polycrystalline graphene films for ultratransparent, highly conductive electrodes

Semiconductor devices having phase change memory cells, electronic systems employing the same and methods of fabricating the same

Full integration and cell characteristics for 64Mb nonvolatile PRAM

Full dispersion versus Debye model evaluation of lattice thermal conductivity with a Landauer approach

Plasma-assisted atomic layer growth of high-quality aluminum oxide thin films

Learning student-friendly teacher networks for knowledge distillation

Highly reliable 256Mb PRAM with advanced ring contact technology and novel encapsulating technology

IEDM Tech. Dig.

Phase-change random access memory device and method of operating the same

Prediction and interpretation of polymer properties using the graph convolutional network

Si FinFET based 10nm technology with multi Vt gate stack for low power and high performance applications

Hongsik Jeong

Mark Lundstrom

Suyoun Lee

Young-Tae Kim

Muhammad Ashraful Alam

Jisu Ryu

Sanghoon Myung

In Huh

Dae-Hwan Kang

Jae Myung Choe

Supriyo Datta

Wonik Jang

RASEONG KIM

Suprem R. Das

Mohammad Ryyan Khan

Seonghoon Jin

Jang-Sik Lee

Hyun Cheol Koo

Bohyung Han

Mathieu Luisier

Changwook Jeong

undergraduate degree in Material Science and Engineering

master degrees in Material Science and Engineering

PhD in Electrical and Computer Engineering

investigated physics/technology of advanced logic devices to support commercial foundry business, and has recently explored bridging physical simulation and AI to overcome challenges of conventional physical simulation

Associate Professor

Changwook Jeong Research Group

교수님 소개

정창욱

연구실 소개

연구실 키워드

알럼나이가 간 회사

연구실 지원 방법

대우 조건

연도별 논문 인용수

저널 & 학회 논문 출판 수

연구실 논문 목록

Ml-driven compact models for rrams: addressing variability and simulation efficiency

Realistic and Scalable TCAD for Yield-Aware Full-Chip DTCO

Context-Informed Neural ODEs Unexpectedly Identify Broken Symmetries: Insights from the Poincaré–Hopf Theorem

연구실 보유 특허

대학별 입시끈

우리 연구실 모집

교수님 소개

정창욱

연구실 키워드

알럼나이가 간 회사

연구실 지원 방법

대우 조건

연도별 논문 인용수

저널 & 학회 논문 출판 수

연구실 논문 목록

Ml-driven compact models for rrams: addressing variability and simulation efficiency

Realistic and Scalable TCAD for Yield-Aware Full-Chip DTCO

Context-Informed Neural ODEs Unexpectedly Identify Broken Symmetries: Insights from the Poincaré–Hopf Theorem

연구실 보유 특허

최근 3년간 연구실적 높은 연구실

대학별 입시끈

우리 연구실 모집