Pattern Recognition Accuracy Optimization of Unsupervised Spiking Neural Network Using Y-Doped AlN Memristors-Reference-Cited by-同舟云学术

Pattern Recognition Accuracy Optimization of Unsupervised Spiking Neural Network Using Y-Doped AlN Memristors

Published:2023-08 Issue:8 Volume:70 Page:4473-4478
ISSN:0018-9383
Container-title:IEEE Transactions on Electron Devices
language:
Short-container-title:IEEE Trans. Electron Devices

Author:

Fu Yi¹,Huang Chu-Chun¹,Lin Zi-Yi²,Lee Chi-Ching³,Wang Jer-Chyi⁴^ORCID

Affiliation:

1. Department of Electronic Engineering, Chang Gung University, Guishan, Taoyuan, Taiwan

2. Department of Computer Science and Information Engineering, Chang Gung University, Guishan, Taoyuan, Taiwan

3. Department of Computer Science and Information Engineering and Artificial Intelligence Research Center, Chang Gung University, Guishan, Taoyuan, Taiwan

4. Department of Electronic Engineering and Green Technology Research Center, Chang Gung University, Guishan, Taoyuan, Taiwan

Funder

National Science and Technology Council, Taiwan

Chang Gung Memorial Hospital, Linkou, Taiwan

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Electrical and Electronic Engineering,Electronic, Optical and Magnetic Materials

Link

http://xplorestaging.ieee.org/ielx7/16/10194493/10155463.pdf?arnumber=10155463

Reference44 articles.

1. Tuning tunnel barrier in Si3N4-based resistive memory embedding SiO2 for low-power and high-density cross-point array applications

2. Theoretical predicted high-thermal-conductivity cubic Si3N4 and Ge3N4: promising substrate materials for high-power electronic devices

3. Stable Bipolar Resistive Switching Characteristics and Resistive Switching Mechanisms Observed in Aluminum Nitride-based ReRAM Devices

4. Thermal transport in tantalum oxide films for memristive applications

5. Resistive-switching behavior and mechanism in copper-nitride thin films prepared by DC magnetron sputtering

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