Detection of Low Concentration NO2 gas Using Si FET-type Gas Sensor with Localized Micro-heater for Low Power Consumption. - Gyuweon Jung; Yoonki Hong; Seongbin Hong; Yujeong Jeong; Wonjun Shin; Jinwoo Park; Jungkyu Lee; Dongkyu Jang; Jong-Ho Lee 002 (IEEE SENSORS, 2019)

Accurate identification of gas type and concentration using DNN reflecting the sensing properties of MOSFET-type gas sensor. - Gyuweon Jung; Hyeongsu Kim; Yujeong Jeong; Yoonki Hong; Meile Wu; Seongbin Hong; Wonjun Shin; Dongkyu Jang; Jong-Ho Lee 002 (ISOEN, 2019)

Response Analysis of Resistor-type Gas Sensor with Bias Voltage Condition. - Kangwook Choi; Gyuweon Jung; Seongbin Hong; Yujeong Jeong; Wonjun Shin; Jinwoo Park; Chang-Yong Lee; Donghee Kim; Jong-Ho Lee 002 (ISOEN, 2022)

Optimal Bias Conditions for FET-type Gas Sensors to Minimize Current Fluctuations. - Gyuweon Jung; Jaehyeon Kim; Wonjun Shin; Seongbin Hong; Yujeong Jeong; Jinwoo Park; Donghee Kim; Kangwook Choi; Jong-Ho Lee 002 (ISOEN, 2022)

1/f Noise in Synaptic Ferroelectric Tunnel Junction: Impact on Convolutional Neural Network. - Wonjun Shin; Kyung Kyu Min; Jong-Ho Bae; Jaehyeon Kim; Ryun-Han Koo; Dongseok Kwon; Jae-Joon Kim; Daewoong Kwon; Jong-Ho Lee 002 (Adv. Intell. Syst., 2023)

Analog Synaptic Devices Based on IGZO Thin-Film Transistors with a Metal-Ferroelectric-Metal-Insulator-Semiconductor Structure for High-Performance Neuromorphic Systems. - Dongseok Kwon; Eun Chan Park; Wonjun Shin; Ryun-Han Koo; Joon Hwang; Jong-Ho Bae; Daewoong Kwon; Jong-Ho Lee 002 (Adv. Intell. Syst., 2023)

Toward Optimized In-Memory Reinforcement Learning: Leveraging 1/f Noise of Synaptic Ferroelectric Field-Effect-Transistors for Efficient Exploration. - Jangsaeng Kim; Wonjun Shin; Jiyong Yim; Dongseok Kwon; Daewoong Kwon; Jong-Ho Lee 002 (Adv. Intell. Syst., 2024)

Efficient Hybrid Training Method for Neuromorphic Hardware Using Analog Nonvolatile Memory. - Dongseok Kwon; Sung Yun Woo; Joon Hwang; Hyeongsu Kim; Jong-Ho Bae; Wonjun Shin; Byung-Gook Park; Jong-Ho Lee 002 (IEEE Trans. Neural Networks Learn. Syst., 2025)

Data-driven prediction of the mechanical behavior of nanocrystalline graphene using a deep convolutional neural network with PCA. - Wonjun Shin; Seongwoo Jang; Yunhyoung Hwang; Jihoon Han (Eng. Comput., 2025)