Fully Photon Controlled Synaptic Memristor for Neuro‐Inspired Computing

Saransh Shrivastava; Lai Boon Keong; Sparsh Pratik; Albert S. Lin; Tseung‐Yuen Tseng

doi:10.1002/aelm.202201093

journal article Open Access Jan 04, 2023

Fully Photon Controlled Synaptic Memristor for Neuro‐Inspired Computing

Saransh Shrivastava

Lai Boon Keong

Sparsh Pratik

Albert S. Lin

Tseung‐Yuen Tseng

Advanced Electronic Materials Vol. 9 No. 3 · Wiley

View at Publisher Save 10.1002/aelm.202201093

Abstract

AbstractThe emerging optoelectronic memristive synapses having the advantages of both optics and electronics exhibit a great potential in neuro‐inspired computing, which is a new generation of artificial intelligence. Herein, a light stimulated synaptic memristor (LSSM) based on ZnO/Zn2SnO4 heterostructure is prepared with the characteristics of reversibly tunable conductance states by varying the wavelength of the incident light. The synaptic feature of this fully photon controlled memristive synapse is revealed by potentiation and depression behaviors stimulated by violet and red light pulses, respectively. Similar to biological brain, the device demonstrates the dynamic learning and forgetting behavior. All‐optically driven and bio‐vision inspired image processing function such as contrast enhancement is exemplified. The international Morse code for Arabic numerals (0–9) is also successfully conveyed by patterned light pulses and suggests the device's potential in the field of optical wireless communication for human–machine interface. Classical Pavlovian conditioning (associative learning) is successfully demonstrated through visible light induction. Finally, the device can realize the recognition application of Zalando's article image through the simulation based on Hopfield neural network (HNN). This work provides a promising approach toward optoelectronic neural systems and human–machine interaction technologies.

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Metrics

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Citations

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References

Details

Published: Jan 04, 2023
Vol/Issue: 9(3)
License: View

Authors

S

Saransh Shrivastava

Department of Electrical Engineering and Computer Science National Yang Ming Chiao Tung University Hsinchu 30010 Taiwan

L

Lai Boon Keong

Institute of Electronics National Yang Ming Chiao Tung University Hsinchu 30010 Taiwan

S

Sparsh Pratik

Department of Electrical Engineering and Computer Science National Yang Ming Chiao Tung University Hsinchu 30010 Taiwan

A

Albert S. Lin

Institute of Electronics National Yang Ming Chiao Tung University Hsinchu 30010 Taiwan

T

Tseung‐Yuen Tseng

Institute of Electronics National Yang Ming Chiao Tung University Hsinchu 30010 Taiwan

Funding

Ministry of Science and Technology, Taiwan Award: 109‐2221‐E‐009‐034‐MY3

Cite This Article

Saransh Shrivastava, Lai Boon Keong, Sparsh Pratik, et al. (2023). Fully Photon Controlled Synaptic Memristor for Neuro‐Inspired Computing. Advanced Electronic Materials, 9(3). https://doi.org/10.1002/aelm.202201093

Fully Photon Controlled Synaptic Memristor for Neuro‐Inspired Computing

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