Korea Advanced Institute of Science and Technology · Engineering
양규철 교수의 연구실은 에너지 수확 및 신재생 에너지 기반의 스마트 센서 기술, 특히 트라이보전기 나노발전기(TENG)와 신경형 컴퓨팅 기반의 뉴모포르픽 하드웨어 설계에 중점을 두고 있습니다. 반도체 공정 기반의 나노구조 소자(예: FinFET, MOSFET)를 활용해 고밀도·저전력 뉴로모픽 칩을 구현하고, 기존의 von Neumann 아키텍처의 한계를 넘어 스파iking 신경망(SNN) 기반의 생체 모방형 인공지능 하드웨어를 개발하고 있습니다. 특히, 센서-신경망 통합형 전자 코(전자 코) 시스템과 CMOS 칩에 통합된 다중상태 단일 트랜지스터 뉴런/시냅스 기반의 스케일러블 뉴모포르픽 아키텍처에 대한 핵심 기술을 확보하고 있습니다.
Figures are computed from collected data and may differ slightly.
With the rapid development of the Internet of Things (IoT), the number of sensors utilized for the IoT is expected to exceed 200 billion by 2025. Thus, sustainable energy supplies without the recharging and replacement of the charge storage device have become increasingly important. Among various energy harvesters, the triboelectric nanogenerator (TENG) has attracted considerable attention due to its high instantaneous output power, broad selection of available materials, eco-friendly and inexpe
A spacer lithography process technology, which uses a sacrificial layer and spacer layer formed by chemical vapor deposition (CVD), has been developed. It has been applied to make a sub-40-nm Si-fin structure for a double-gate FinFET with conventional dry etching for the first time. The minimum-sized features are defined not by the photolithography but by the CVD film thickness. Therefore, this spacer lithography technology yields better critical dimension uniformity than conventional optical or
Abstract A spiking neural network (SNN) inspired by the structure and principles of the human brain can significantly enhance the energy efficiency of artificial intelligence computing by overcoming the bottlenecks of the conventional von Neumann architecture with its massive parallelism and spike transmissions. The construction of artificial neurons is important for the hardware implementation of an SNN, which generates spike signals when enough synaptic signals are gathered. Because circuit‐le
A neuromorphic module of an electronic nose (E-nose) is demonstrated by hybridizing a chemoresistive gas sensor made of a semiconductor metal oxide (SMO) and a single transistor neuron (1T-neuron) made of a metal-oxide-semiconductor field-effect transistor (MOSFET). By mimicking a biological olfactory neuron, it simultaneously detects a gas and encoded spike signals for in-sensor neuromorphic functioning. It identifies an odor source by analyzing the complicated mixed signals using a spiking neu
Cointegration of multistate single-transistor neurons and synapses was demonstrated for highly scalable neuromorphic hardware, using nanoscale complementary metal-oxide semiconductor (CMOS) fabrication. The neurons and synapses were integrated on the same plane with the same process because they have the same structure of a metal-oxide semiconductor field-effect transistor with different functions such as homotype. By virtue of 100% CMOS compatibility, it was also realized to cointegrate the neu
Gate-induced drain leakage (GIDL) current is investigated in single-gate (SG) ultra-thin body field effect transistor (FET), symmetrical double-gate (DG) FinFET, and asymmetrical DG metal oxide semiconductor field effect transistor (MOSFET) devices. Measured reductions in GIDL current for SG and DG thin-body devices are reported for the first time. The thin-body devices exhibit much lower GIDL current than bulk-Si MOSFETs, and the GIDL is found to decrease with decreasing body thickness. These r
An increasing demand for nonvolatile memory has driven extensive research on resistive switching memory because it uses simple structures with high density, fast switching speed, and low power consumption. To improve the storage density, the application of multilevel cells is among the most promising solutions, including three-dimensional cross-point array architectures. Two-dimensional nanomaterials have several advantages as resistive switching media, including flexibility, low cost, and simpl
Realizing a neuromorphic-based artificial visual system with low-cost hardware requires a neuromorphic device that can react to light stimuli. This study introduces a photoresponsive neuron device composed of a single transistor, developed by engineering an artificial neuron that responds to light, just like retinal neurons. Neuron firing is activated primarily by electrical stimuli such as current via a well-known single transistor latch phenomenon. Its firing characteristics, represented by sp
Open papers in the app to read, cite, and organize with AI.