Shinhyun Choi

Assistant Professor

Korea Advanced Institute of Science and Technology

South Korea

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About

Shinhyun Choi is an Assistant Professor at the Korea Advanced Institute of Science and Technology in South Korea. His research focuses on neuromorphic computing, memristor technology, and innovative hardware solutions for robotics and edge computing. Recent publications include studies on memristor-based artificial sensory systems, energy-efficient neuromorphic architectures, and the development of reliable dynamic memristors.

Articles (14)

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2025

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2025

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2024

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2024

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2024

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2024

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2023

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2023

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2022

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2022

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2022

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2021

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2021

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2020

Collaborators (2)

Sangtae Kim

Assistant Professor

Hanyang University

SOUTH KOREA

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Jongwon Lee

Hanbat National University

SOUTH KOREA

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Shinhyun Choi

Research Interests

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About

Articles (14)

Experimental demonstration of third-order memristor-based artificial sensory nervous system for neuro-inspired robotics

Decoupling Strategy to Separate Training and Inference with Three-Dimensional Neuromorphic Hardware Composed of Neurons and Hybrid Synapses

Tunable ion energy barrier modulation through aliovalent halide doping for reliable and dynamic memristive neuromorphic systems

Realization of Selector‐Memory Bi‐Functionality with Self‐Current Regulation Utilizing Poly‐Crystalline Based GST Electrolyte for Memristor Hardware Development

Ultra‐Low Power and Reliable Dynamic Memtransistor Based on Charge Storage Junction FET with Step‐Wise Potential Barrier for Energy‐Efficient Edge Computing Framework

Phase-change memory via a phase-changeable self-confined nano-filament

The effect of Schottky barrier modulation on conduction and failure mechanisms of an Ag/WOx/p-Si based memristor

Linear conductance update improvement of CMOS-compatible second-order memristors for fast and energy-efficient training of a neural network using a memristor crossbar array

An Overturned Charge Injection Synaptic Transistor With a Floating-Gate for Neuromorphic Hardware Computing

Experimental demonstration of highly reliable dynamic memristor for artificial neuron and neuromorphic computing

Reliable multilevel memristive neuromorphic devices based on amorphous matrix via quasi-1D filament confinement and buffer layer

Neural Network Physically Unclonable Function: A Trainable Physically Unclonable Function System with Unassailability against Deep Learning Attacks Using Memristor Array

Neural Network Physically Unclonable Function: A Trainable Physically Unclonable Function System with Unassailability against Deep Learning Attacks Using Memristor Array

Conductive-bridging random-access memories for emerging neuromorphic computing

Collaborators (2)

Sangtae Kim

Jongwon Lee

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