Neuromorphic Device With WO<i>x</i>/TiNO<i>y</i> Heterojunction for Airline Review Sentiment Prediction

Neuromorphic device shows great potential in brain-like computing and is considered the hardware basis for breaking through the Von Neumann system of conventional computers. This article explores the application of WOx/TiNOy heterojunction neuromorphic devices in simulating biological synapse. The device successfully simulates a series of features of biological synapse, including paired-pulse facilitation (PPF), paired-pulse depression (PPD), long-term potential (LTP), long-time-range depression (LTD), continuous adjustable weights, and learning–forgetting–relearning based on the Schottky emission and Ag-conducting filament mechanisms. Using the device weights generated by the electrical impulses in conjunction with the long short-term memory (LSTM) network, the accuracy of the mood prediction task for the airline review dataset reached 99.23%, and the results demonstrated the potential of neuromorphic devices for application in the field of mood prediction.

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Publication Details

Journal
IEEE Transactions on Electron Devices
Published
2025-02-10
DOI
https://doi.org/10.1109/ted.2025.3537999
Citations
1
Primary Topic
Neural Networks and Applications
Type
article
Field-Weighted Citation Impact
2.48

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article

Neuromorphic Device With WOx/TiNOy Heterojunction for Airline Review Sentiment Prediction

Shi‐Xiong Liu, Fan Yang, Cong Wang, Yang Li
1 citations
IEEE Transactions on Electron Devices
Neural Networks and Applications
2.48
article

Neuromorphic Device With WOx/TiNOy Heterojunction for Airline Review Sentiment Prediction

Shi‐Xiong Liu, Fan Yang, Cong Wang, Yang Li
article en
1 citations

Abstract

Neuromorphic device shows great potential in brain-like computing and is considered the hardware basis for breaking through the Von Neumann system of conventional computers. This article explores the application of WOx/TiNOy heterojunction neuromorphic devices in simulating biological synapse. The device successfully simulates a series of features of biological synapse, including paired-pulse facilitation (PPF), paired-pulse depression (PPD), long-term potential (LTP), long-time-range depression (LTD), continuous adjustable weights, and learning–forgetting–relearning based on the Schottky emission and Ag-conducting filament mechanisms. Using the device weights generated by the electrical impulses in conjunction with the long short-term memory (LSTM) network, the accuracy of the mood prediction task for the airline review dataset reached 99.23%, and the results demonstrated the potential of neuromorphic devices for application in the field of mood prediction.

IEEE Transactions on Electron DevicesVol. 72(4)
Shandong University (CN), Harbin Institute of Technology (CN), University of Jinan (CN)
National Natural Science Foundation of China
Openalex Percentile: Top 12%
Neural Networks and Applications
2.48
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