Review of semiconductor flash memory devices for material and process issues

SS Kim, SK Yong, W Kim, S Kang, HW Park… - Advanced …, 2023 - Wiley Online Library
Abstract Vertically integrated NAND (V‐NAND) flash memory is the main data storage in
modern handheld electronic devices, widening its share even in the data centers where …

Recent progress on emerging transistor‐based neuromorphic devices

Y He, L Zhu, Y Zhu, C Chen, S Jiang… - Advanced Intelligent …, 2021 - Wiley Online Library
Human brain outperforms the current von Neumann digital computer in many aspects, such
as energy efficiency and fault‐tolerance. Inspired by human brain, neuromorphic …

[HTML][HTML] Architecture and process integration overview of 3D NAND flash technologies

GH Lee, S Hwang, J Yu, H Kim - Applied Sciences, 2021 - mdpi.com
In the past few decades, NAND flash memory has been one of the most successful
nonvolatile storage technologies, and it is commonly used in electronic devices because of …

On-chip training spiking neural networks using approximated backpropagation with analog synaptic devices

D Kwon, S Lim, JH Bae, ST Lee, H Kim… - Frontiers in …, 2020 - frontiersin.org
Hardware-based spiking neural networks (SNNs) inspired by a biological nervous system
are regarded as an innovative computing system with very low power consumption and …

Digital and analog switching characteristics of InGaZnO memristor depending on top electrode material for neuromorphic system

JT Jang, J Min, Y Hwang, SJ Choi, DM Kim… - IEEE …, 2020 - ieeexplore.ieee.org
In this study, we demonstrate both of digital and analog memory operations in InGaZnO
(IGZO) memristor devices by controlling the electrode materials for neuromorphic …

Voltage-dependent synaptic plasticity: Unsupervised probabilistic Hebbian plasticity rule based on neurons membrane potential

N Garg, I Balafrej, TC Stewart, JM Portal… - Frontiers in …, 2022 - frontiersin.org
This study proposes voltage-dependent-synaptic plasticity (VDSP), a novel brain-inspired
unsupervised local learning rule for the online implementation of Hebb's plasticity …

Neuron circuits for low-power spiking neural networks using time-to-first-spike encoding

S Oh, D Kwon, G Yeom, WM Kang, S Lee… - IEEE …, 2022 - ieeexplore.ieee.org
Hardware-based Spiking Neural Networks (SNNs) are regarded as promising candidates for
the cognitive computing system due to its low power consumption and highly parallel …

Electrolyte‐Gated Transistor Array (20× 20) with Low‐Programming Interference Based on Coplanar Gate Structure for Unsupervised Learning

W Zhang, J Li, M Li, Y Li, H Lian, W Gao, B Sun… - Small …, 2024 - Wiley Online Library
Compute‐in‐memory (CIM) is a pioneering approach using parallel data processing to
eliminate traditional data transmission bottlenecks for faster, energy‐efficient data handling …

Hardware implementation of spiking neural networks using time-to-first-spike encoding

S Oh, D Kwon, G Yeom, WM Kang, S Lee… - arxiv preprint arxiv …, 2020 - arxiv.org
Hardware-based spiking neural networks (SNNs) are regarded as promising candidates for
the cognitive computing system due to low power consumption and highly parallel …