Programmable Threshold Logic Implementations in a Memristor Crossbar Array-Reference-Cited by-同舟云学术

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Programmable Threshold Logic Implementations in a Memristor Crossbar Array

Published:2024-03-12 Issue:12 Volume:24 Page:3581-3589
ISSN:1530-6984
Container-title:Nano Letters
language:en
Short-container-title:Nano Lett.

Author:

Youn Sangwook¹,Lee Jungjin²,Kim Sungjoon³,Park Jinwoo¹,Kim Kyuree¹,Kim Hyungjin¹⁴^ORCID

Affiliation:

1. Division of Materials Science and Engineering, Hanyang University, Seoul 04763, Korea

2. Department of Electrical and Computer Engineering, Inha University, Incheon 22212, Korea

3. Department of Electrical and Computer Engineering, Seoul National University, Seoul 08826, Korea

4. Department of Electronic Engineering, Hanyang University, Seoul 04763, Korea

Funder

National Research Foundation of Korea

Hanyang University

Institute for Information and Communications Technology Promotion

Publisher

American Chemical Society (ACS)

Link

https://pubs.acs.org/doi/pdf/10.1021/acs.nanolett.3c04073

Reference68 articles.

1. Can programming be liberated from the von Neumann style?

2. 4K-memristor analog-grade passive crossbar circuit

3. Resistive Random Access Memory (ReRAM) Based on Metal Oxides

4. Multibit, Lead‐Free Cs₂SnI₆ Resistive Random Access Memory with Self‐Compliance for Improved Accuracy in Binary Neural Network Application

5. Metal–Oxide RRAM

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1. Coupling-Free Readout Scheme for Memcapacitors With NAND Flash Structure;IEEE Transactions on Electron Devices;2024-08

2. Complex Dynamical Behavior of Locally Active Discrete Memristor-Coupled Neural Networks with Synaptic Crosstalk: Attractor Coexistence and Reentrant Feigenbaum Trees;Electronics;2024-07-15

3. Neuron Circuit Based on a Split-gate Transistor with Nonvolatile Memory for Homeostatic Functions of Biological Neurons;Biomimetics;2024-05-31

4. Memristor Crossbar Array with Enhanced Device Yield for In-Memory Vector–Matrix Multiplication;ACS Applied Electronic Materials;2024-05-30

5. Dynamic memristor array with multiple reservoir states for training efficient neuromorphic computing;Journal of Materials Chemistry C;2024

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