🟩 Join Us: Explore the Future of Semiconductors

 DANIEL invites dedicated and passionate students and researchers to join us in pushing the frontiers of semiconductor technology. We work on cutting-edge topics such as Low-k materials for next-gen mixed-die packages, GAA process innovations, Phase Transition in Inorganic/Organic/Hybrid Materials & Ultra-fast infrared/Circularly Polarized Light photoresponse. Our collaborative and innovative environment offers the opportunity to work on technologies that will shape industries decades from now.

 우리는 열정적이고 새로운 것을 배우려는 여러분을 기다리고 있습니다. 연구와 도전에 대한 열정을 가진 분들이라면, DANIEL에서 함께 성장하며 미래를 만들어갈 기회를 잡으세요! 연구실 진학에는 특별한 조건이 없지만, 서로를 존중하고 함께하는 것의 가치를 진심으로 이해하는 학생이라면 언제나 환영합니다. 

🟩 Benefits

 끈끈하고 평생 지속되는 연구 family 형성

 연구/학업을 위한 편안한 환경

 자유로운 진로 및 전공 공부 상담

 연구 및 실험에 필요한 재료 및 장비 사용 지원

 국내외 논문 지도 (대학원생 SCI급 1편 이상, 학부 연구생은 선택 사항이지만 1편 이상 쓸 수 있도록 최대한 서포팅!)

 국내/국제 학술 대회, 전시회 및 대회 참가 지원

 연구비 지급 (프로젝트 참여 기여에 따른 인센티브 인건비 지급 / 연구실적에 따른 인센티브 지급)

 국내외 반도체 연구 타기관 협력 프로젝트 연구 참여 

🟩Join the Future of Research

If you’re passionate about innovation and excited to collaborate on groundbreaking research, we’d love to have you on our team!
For inquiries, please contact Prof. Min-Woo Kim with your CV and research interests.

 함께 연구하며 반도체 기술의 미래를 만들어 갈 연구 family를 기다리고 있습니다.

 학부 연구생 참여, 대학원 진학 (연구 내용), 진로에 관한 상담에 대하여 자유롭게 문의하세요~!

✉️ mwkim@mju.ac.kr   |   📍 Y19502(5th), Engineering Bldg. 3, 116 Myongji-ro, Cheoin-gu, Yongin-si, Gyeonggi-do 17058, Korea  |   📞 +82-31-324-1255

🟩Our Key Research Areas

1. Low-k Materials for Mixed-Die Packages
We design and analyze the reliability of low-k materials to reduce parasitic capacitance and enhance signal integrity, essential for next-generation mixed-die packages. Our work ensures that future microelectronics achieve both performance and longevity.

2. Innovative Process Engineering for N+5 Generation
Our research targets semiconductor manufacturing breakthroughs expected two decades from now. We address atomic-scale patterning and quantum effects, shaping processes that will define the future of nanoelectronics.

(1) Channel Mobility & Gate EOT Scaling
We optimize high-mobility materials and high-k dielectrics to enhance channel mobility and minimize gate leakage, crucial for sub-3nm nodes.

(2) Contact Process Innovations
Reducing contact resistance in GAA technology is essential for efficient current flow. Our work focuses on interface engineering to lower resistivity while maintaining channel mobility.

(3) BEOL Interconnect Innovations
We explore materials like graphene and carbon nanotubes to solve resistivity issues, enabling ultra-fast, low-power devices for future technologies.

3. Phase Transition in Inorganic/Organic and Hybrid Materials for N+6 Generation
Our research into phase transitions in hybrid materials paves the way for adaptable, high-performance semiconductors that will meet the demands of future optoelectronics and energy-efficient systems.

4. Advanced Light Detection Materials Design

(1) Helical Polymers for Circularly Polarized Light Imaging
We investigate chiral polymers for their unique interactions with circularly polarized light, opening new possibilities in bioimaging and quantum computing.

(2) Twisted Crystalline Organic Semiconductors for IR Detection
Our research on twisted organic semiconductors aims to develop tunable and sensitive IR sensors, providing flexible, low-cost solutions for optoelectronics.

(3) Ultra-Fast Infrared Photoresponse
Delving into femtosecond-scale photon-electron interactions,our work is critical for advancements in high-speed optoelectronics and quantum technologies.

     5. Physics-Informed AI & Digital Twins for Materials and Devices

         (1) AI-Driven Materials Discovery & Inverse Design
          We employ machine learning and deep learning to uncover hidden relationships within multidimensional materials data and inversely design materials and      device structures with targeted electrical, optical, and mechanical properties.

         (2) Neuromorphic Electronics & Intelligent Memory Devices
          We investigate memristive and ferroelectric devices for brain-inspired computing, in-memory computing, and adaptive electronics. Our research focuses   on analog switching, synaptic plasticity, multilevel memory, and energy-efficient AI hardware.

          (3) Predictive Digital Twins for Semiconductor Materials and Devices
          By integrating experimental measurements, multiphysics simulations, and AI models, we construct digital twins capable of predicting phase evolution,       device characteristics, reliability, and process–property relationships before extensive experimental iteration.

      6. Autonomous Semiconductor Manufacturing & AI-Driven Process Intelligence

         (1) Intelligent Plasma Processing & Endpoint Prediction
          We combine plasma diagnostics, sensor data, and machine learning to understand complex etching and deposition processes, enabling accurate endpoint   prediction and real-time identification of process deviations.

        (2) Virtual Metrology & Autonomous Process Control
          We develop data-driven virtual metrology and predictive process-control systems that estimate critical material and device parameters without relying     solely on conventional post-process measurements, providing a foundation for adaptive and autonomous fabrication.

        (3) AI-Enabled Heterogeneous Integration & Advanced Packaging
          We apply AI-assisted optimization and reliability modeling to hybrid bonding, through-silicon vias, chiplets, and heterogeneous integration, enabling      high-density, high-performance packaging architectures for future semiconductor systems.