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Artificial neural network with broad application prospect has attracted particular attention due to the promise of solving the memory wall bottleneck. The neural devices that mix light and electricity provide more degrees of freedom for the design of artificial neural network, but they still do not get rid of the shackles that the response signal needs circuit to transmission. The exploration of all-optical neural devices (optical signal input and output) is expected to solve this problem. Here, an all-optical synaptic device simply based on a long-afterglow material is reported. The optical properties of the all-optical synaptic device are similar to the responses in biological synapses. Unique image displays and memory functions can be achieved by combining all-optical synaptic arrays with synaptic memory behavior. Furthermore, the optical summation of all-optical synaptic array pixels can be completed by combining the focusing characteristics of convex lens, which realizes the photon transmission after preprocessing multiple input signals. Particularly, the simple single-layer structure of all-optical synapses with polydimethylsiloxane (PDMS) as the carrier has high plasticity and is expected to achieve large-scale preparation. This work enriches the diversity of artificial synapses and shows the huge development potential of photoelectric artificial neural networks.

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Publication history
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Acknowledgements

Publication history

Received: 13 December 2022
Revised: 07 February 2023
Accepted: 09 February 2023
Published: 18 March 2023
Issue date: July 2023

Copyright

© Tsinghua University Press 2023

Acknowledgements

Acknowledgements

The authors are grateful for financial support from the National Natural Science Foundation of China (No. U21A20497), the Natural Science Foundation for Distinguished Young Scholars of Fujian Province (No. 2020J06012), the Fujian Science & Technology Innovation Laboratory for Optoelectronic Information of China (No. 2021ZZ129), and the Joint Funds for the innovation of science and Technology, Fujian province (No. 2021Y9074).

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