Ultrafast neuromorphic computing driven by polariton nonlinearities
Yusong Gan, Ying Shi, Sanjib Ghosh, Haiyun Liu, Huawen Xu, Qihua Xiong
Abstract
Abstract Neuromorphic computing offers a promising approach to artificial intelligence by mimicking biological neural networks to perform complex tasks efficiently. While software-based simulations have demonstrated the potential of neuromorphic architectures, a physical platform is crucial to fully realize its computational advantages. Herein, we present the first demonstration of perovskite microcavity exciton polaritons as a platform for reservoir computing-based artificial neural networks. By leveraging the nonlinear response properties of exciton polaritons, we developed a neuromorphic computing architecture capable of performing classification tasks with single-step training, eliminating the need for iterative algorithms like backpropagation. Applying this system to a handwritten digit recognition task, we achieve 92% classification accuracy at room temperature. Notably, we also show that the system is dynamically nonlinear, further enhancing the potential to improve classification efficiency and address more complex tasks. Our findings advocate the promising capabilities of perovskite exciton polaritons as energy-efficient, ultrafast response platforms for artificial intelligence, paving the way for next-generation computational technologies.