Neuromorphic computing, which mimics biological neural networks, offers a promising approach to artificial intelligence. While software-based artificial neural networks (ANNs) have demonstrated the potential of neuromorphic architectures, a physical platform is crucial to fully realize its computational advantages. Among various physical systems, microcavity exciton polaritons have attracted attention for neuromorphic computing due to their ultrafast dynamics, strong nonlinearities, and light-based architecture, which naturally align with the requirements of brain-inspired computation. However, their practical use has been hampered by the need for cryogenic operation and intricate fabrication processes.
The operating process of polariton neuromorphic computing on image recognition task. The image information is first encoded by an SLM into an intensity-modulated laser beam. This laser beam then non-resonantly excites the exciton polaritons, leading to polariton condensation, which serves as the output information. Finally, a linear regression scheme is applied in the output layer to obtain the desired results. Image from: eLight
In a recent study, researchers from Tsinghua University and Beijing Academy of Quantum Information Sciences have demonstrated perovskite microcavity exciton polaritons operating at room temperature as a platform for reservoir computing-based artificial neural networks. This novel system displayed high-speed digit recognition with 92% accuracy using only single-step training and could open new opportunities for scalable, light-driven neural hardware.