Three-Dimensional AI Semiconductor Lab
3차원 인공지능 반도체 연구실
3차원 인공지능 반도체 연구실
Welcome to TAIS Lab!
We aim to pioneer three-dimensional (3D) AI semiconductor technologies that seamlessly integrate materials, devices, and system architectures to enable intelligent, efficient, and energy-autonomous computing. Our goal is to overcome the limitations of conventional computing by developing vertically stacked, low-power, and brain-inspired semiconductor platforms. Through this holistic approach, spanning from fundamental materials and device innovations to large-scale system demonstrations, we envision building scalable and sustainable hardware that empowers diverse AI applications—from deep learning and reinforcement learning to hardware security, medical diagnostics, and edge intelligence.
We adopt a holistic approach that connects materials, devices, and systems into a unified research framework:
Material & Device Co-Optimization: We optimize semiconductor devices from the atomic scale to the device scale, using in-situ, non-destructive low-frequency noise (LFN) spectroscopy to analyze defects and variability, enhancing device performance, scalability, and reliability.
Synapse & Neuron Co-Integration: Building on optimized devices, we realize artificial synapse arrays and neuron circuits that emulate brain-inspired computing primitives, forming the foundation of scalable neuromorphic hardware.
Neuromorphic System Demonstration: These integrated synapse–neuron platforms are further demonstrated in hardware systems for reinforcement learning, spiking neural networks, reservoir computing, and other AI models, showcasing efficient and robust neuromorphic computing.
Monolithic 3D (M3D) Integration: Finally, we incorporate multimodal sensing, in-sensor computing, and energy storage/harvesting into vertically stacked M3D systems, enabling energy-autonomous neuromorphic platforms suitable for next-generation edge AI.
Our research focuses on four interconnected directions:
Ferroelectrics, charge trap flash memory, and CMOS circuits for in-memory computing.
Vertical stacking and M3D integration for high-performance hardware platforms.
Synaptic and neuronal devices co-integrated into scalable neuromorphic platforms.
Multimodal in-sensor computing and energy-autonomous microelectronics for edge AI.
Selected Lead Author Publications
Nature Communications (2024, IF=15.7) [Editor’s Highlight & Featured Article]
Nature Communications (2025, IF=15.7)
Advanced Science (2024, IF=14.1)
Advanced Science (2024, IF=14.1)
Nano Energy (2025, IF=17.1)
IEEE International Electron Devices Meeting (IEDM) (2023) [Semiconductor Device Top Conference]
IEEE Electron Device Letters (2024, IF=4.5). [Editors’ Pick]
We welcome applications from highly motivated postdoctoral researchers, graduate and undergraduate students to join TAIS Lab.
If you are interested, please contact kimjs@sogang.ac.kr with your CV and a research statement.
TAIS Lab에서는 열정적인 박사후 연구원, 대학원생(석·박사 과정) 및 학부 연구생을 상시 모집하고 있습니다.
관심 있으신 분은 kimjs@sogang.ac.kr으로 이력서(CV)와 연구계획서(research statement)를 보내주시기 바랍니다.