Multidimensional and reconfigurable optical neuromorphic computing using perovskite-based all-photonic synapses

Optical neuromorphic computing utilizes light-based neural networks for efficient AI processing. Conventional optoelectronic synapses are limited by single-dimensional perception and electrical weight modulation. This study overcomes these constraints by developing all-photonic artificial synapses using water-mediated phase transitions in MAPbI₃ perovskite. These synapses exhibit reversible light-driven optical memory capabilities, achieving a broad transmittance modulation via precisely controlled crystal deformation mechanisms. The high-stability synapses successfully replicate neurobiological functions, including paired-pulse facilitation, short-term to long-term memory transition, and humidity-dependent plasticity. Implemented within a recurrent neural network, the synapses achieve 100% classification accuracy for multidimensional optical stimuli encompassing power, duration, and environmental humidity parameters. Furthermore, integration with a diffractive deep neural network enables reconfigurable computing with 80 distinct programmable transmittance states, achieving remarkable classification accuracies on the MNIST handwritten digits and Fashion-MNIST datasets without hardware modifications. This work establishes a paradigm for developing intelligent systems that adapt to complex environmental changes, demonstrating potential for applications in dynamic visual perception and multi-task processing environments. Optical neuromorphic computing faces challenges with single-dimensional perception and electrical weight modulation in traditional synapses. Here, the authors develop all-photonic artificial synapses using MAPbI₃ perovskites, achieving reversible optical memory and high classification accuracy, paving the way for adaptive intelligent systems in dynamic environments and enhancing multi-task processing capabilities.