Sangbum Kim

Associated Professor

Seoul National University

South Korea

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Dr. Sangbum Kim is an Associate Professor at Seoul National University in South Korea. His research focuses on enhancing the performance and reliability of thin-film transistors, as well as developing innovative solutions for on-chip training in analog in-memory computing systems. Recent publications highlight his work on memristors, neuromorphic devices, and efficient hardware implementations for deep learning applications.

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Collaborators (8)

Ji-Hoon Kang

Assistant Professor

Inha University

SOUTH KOREA

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Taeyoon Kim

Korea Advanced Institute of Science and Technology

SOUTH KOREA

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Eun-Mi Hur

Seoul National University

SOUTH KOREA

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Jae Yong Song

Professor

Pohang University of Science and Technology

SOUTH KOREA

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Jaehyeon Kang

Seoul National University

SOUTH KOREA

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Tae-Hyeon Kim

Seoul National University

SOUTH KOREA

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Seyoung Kim

Associate Professor

Pohang University of Science and Technology

SOUTH KOREA

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Kibum Kang

Assistant Professor

KAIST

SOUTH KOREA

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Sangbum Kim

Research Interests

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Articles (20)

Enhancement of On-Current and Reliability in InGaZnO Thin-Film Transistors for Synaptic Circuit Applications through Gate Insulator Stack Engineering

Selective Update for Hardware‐Friendly On‐Chip Training in Distributed Analog In‐Memory Computing Systems

Disturbance‐Aware On‐Chip Training with Mitigation Schemes for Massively Parallel Computing in Analog Deep Learning Accelerator

Crystallinity‐controlled volatility tuning of <scp>ZrO<sub>2</sub></scp> memristor for physical reservoir computing

An Analog Neuromorphic On-Chip Training System With IGZO TFT-Based 6T1C Synaptic Memory

Enabling Selective and Tunable Weight Updates in All-InGaZnO 3-Transistor 1-Capacitor Synaptic Circuits for On-Chip Training

V-PCMixer: incorporating 3D vertical phase-change memory based analog in-memory computing in mixer architecture

Artificial synaptic devices based on biomimetic electrochemistry: a review

Energy‐Efficient Hardware Implementation of Spiking‐Restricted Boltzmann Machines Using Pseudo‐Synaptic Sampling

Solving Max‐Cut Problem Using Spiking Boltzmann Machine Based on Neuromorphic Hardware with Phase Change Memory

Cluster-type analogue memristor by engineering redox dynamics for high-performance neuromorphic computing

Implementation of convolutional neural network and 8-bit reservoir computing in CMOS compatible VRRAM

Recent Advances in Synaptic Nonvolatile Memory Devices and Compensating Architectural and Algorithmic Methods Toward Fully Integrated Neuromorphic Chips

Vertical Metal‐Oxide Electrochemical Memory for High‐Density Synaptic Array Based High‐Performance Neuromorphic Computing

Pattern Training, Inference, and Regeneration Demonstration Using On‐Chip Trainable Neuromorphic Chips for Spiking Restricted Boltzmann Machine

CRUS: A Hardware-Efficient Algorithm Mitigating Highly Nonlinear Weight Update in CIM Crossbar Arrays for Artificial Neural Networks

Elucidating Ionic Programming Dynamics of Metal-Oxide Electrochemical Memory for Neuromorphic Computing

1/f noise in amorphous Sb2Te3 for energy-efficient stochastic synapses in neuromorphic computing

Simulation-based analysis of novel phase change memory structure with separated program and read paths for low program current and endurance enhancement

Catalyze Materials Science with Machine Learning

Collaborators (8)

Ji-Hoon Kang

Taeyoon Kim

Eun-Mi Hur

Jae Yong Song

Jaehyeon Kang

Tae-Hyeon Kim

Seyoung Kim

Kibum Kang

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