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Review of semiconductor flash memory devices for material and process issues
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 …
modern handheld electronic devices, widening its share even in the data centers where …
Recent progress on emerging transistor‐based neuromorphic devices
Human brain outperforms the current von Neumann digital computer in many aspects, such
as energy efficiency and fault‐tolerance. Inspired by human brain, neuromorphic …
as energy efficiency and fault‐tolerance. Inspired by human brain, neuromorphic …
[HTML][HTML] Architecture and process integration overview of 3D NAND flash technologies
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 …
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
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 …
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
In this study, we demonstrate both of digital and analog memory operations in InGaZnO
(IGZO) memristor devices by controlling the electrode materials for neuromorphic …
(IGZO) memristor devices by controlling the electrode materials for neuromorphic …
Voltage-dependent synaptic plasticity: Unsupervised probabilistic Hebbian plasticity rule based on neurons membrane potential
This study proposes voltage-dependent-synaptic plasticity (VDSP), a novel brain-inspired
unsupervised local learning rule for the online implementation of Hebb's plasticity …
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
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 …
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 …
eliminate traditional data transmission bottlenecks for faster, energy‐efficient data handling …
Hardware implementation of spiking neural networks using time-to-first-spike encoding
Hardware-based spiking neural networks (SNNs) are regarded as promising candidates for
the cognitive computing system due to low power consumption and highly parallel …
the cognitive computing system due to low power consumption and highly parallel …