Optical Neuromorphic Computing Based on Reconfigurable Excitonic Devices

Optical neuromorphic computing offers promising avenues for real-time image processing and low-power artificial intelligence. Here, we introduce and experimentally validate a fundamentally new computing paradigm that exploits optical exciton dynamics in two-dimensional van der Waals heterostructures. The type-II band alignment and high permittivity (ε ≈ 19) enable exciton control, achieving an exciton-to-trion ratio of ∼7 under electric field modulation at room temperature. Quasi-linear trion photoluminescence acts as an optical synaptic response, with weights dynamically tuned by substrate voltage. By leveraging these programmable optical responses, we have achieved neuromorphic functions, including convolutional filtering for image denoising and fully connected networks for pattern recognition, achieving a classification accuracy rate of 98.7% even under noisy conditions. This work establishes β-TeO 2 as a key material for optical neural networks and adaptive vision systems, redefining intelligent photonic processing.