Integrated photonic synapses, neurons, memristors, and neural networks for photonic neuromorphic computing
用于光子神经形态计算的光子突触、神经元、忆阻器和神经网络集成
光子ニューロモルフィックコンピューティングのための統合光子シナプス、ニューロン、メムリスタ、およびニューラルネットワーク
통합 광전자 시냅스, 뉴런, 메모리스터 및 광전자 뉴로모픽 컴퓨팅을 위한 신경망
Sinapsis fotónicas integradas, neuronas, memristores y redes neuronales para la computación neuromórfica fotónica
Synapses photoniques intégrées, neurones, memristors et réseaux neuronaux pour le calcul neuromorphique photonique
Интегрированные фотонные синапсы, нейроны, мемристоры и нейронные сети для фотонных нейроморфных вычислений
Shufei Han 韩书菲 ¹ ², Weihong Shen 沈微宏 ¹ ², Min Gu 顾敏 ¹ ², Qiming Zhang 张启明 ¹ ²
¹ School of Artificial Intelligence Science and Technology, University of Shanghai for Science and Technology, Shanghai 200093, China
中国 上海 上海理工大学智能科技学院
² Institute of Photonic Chips, University of Shanghai for Science and Technology, Shanghai 200093, China
中国 上海 上海理工大学光子芯片研究院
Rising demands for bandwidth, speed, and energy efficiency are reshaping the landscape of computing beyond the limits of von Neumann electronics. Neuromorphic photonics—using light to emulate neural computation—offers ultrafast, massively parallel, and low-energy information processing, positioning integrated photonic neural networks (IPNNs) as promising hardware for next-generation artificial intelligence (AI).
By combining the architectural efficiency of neuromorphic models with the physical advantages of integrated photonics, IPNNs enable high-speed and programmable linear operations during the in-plane optical transmission, while leaving room for compact and reconfigurable on-chip optical nonlinearities and memory functions. Firstly, we review the concepts and principles of key building blocks in IPNN, that are photonic synapses, neurons, and photonic memristors which offer optical memory and storage capabilities.
And then, we summarize the representative IPNN architectures and their recent advances, including coherent, parallel, diffractive, and reservoir computing, for photonic neuromorphic computing with high throughput and high efficiency. Finally, we outline practical considerations—calibration and stability of large-scale networks, routes toward co-integration with electronics, diffractive–interferometric hybrid architectures, and programmable photonic architectures for general AI purposes.
We highlight a forward outlook on enabling IPNN with low energy consumption, robust photonic operations, and efficient training strategies, aiming to guide the maturation of general-purpose, low-power photonic AI.
