Work

Perovskite MAPbBr2I All-Optical Synapses for Dynamic Pattern Recognition and Diffractive Neuromorphic Computing

Year: 2026

Type: article

Abstract: Conventional optoelectronic synapses rely on electrical signals for core operations, resulting in complex circuitry, limited response speed, and energy inefficiency. Herein, an all-optical synapse based on perovskite MAPbBr2I is developed that directly converts optical stimuli into transmittance responses that mimic fundamental synaptic plasticity, including paired-pulse facilitation, short- and long-term memory, and learning. By using the dynamic transmittance response as input to an artificial neural network, high-accuracy dynamic pattern recognition of sequential characters is achieved. Furthermore, the optically controlled transmittance states are successfully integrated as programmable weights into a diffractive neural network, enabling all-optical classification of MNIST handwritten digits with an accuracy of 89%. This fully optical architecture, which eliminates electronic components and complex circuits, offers a promising pathway toward high-speed, energy-efficient vision systems by fundamentally circumventing the von Neumann bottleneck.

Source: Photonics

DOI: 10.3390/photonics13040328

Authors: Yang Fang, Yitong Wu, Qing Hou, Xi Chen

Institution: University of Shanghai for Science and Technology

Language: English

Topic: Advanced Memory and Neural Computing

Subfield: Electrical and Electronic Engineering

Field: Engineering

Domain: Physical Sciences

Open Access status: gold

Estimated APC paid: $2,896

Funder: National Natural Science Foundation of China