Emerging integrated photonic neural network technologies for artificial intelligence: from devices to systems

ABSTRACT The rapid expansion of artificial intelligence (AI) has created an explosive demand for computing power, while traditional von Neumann-based electronic computing faces the memory and interconnect bottlenecks. By migrating critical operations to the optical domain, photonic neural networks (PNNs) offer a promising solution to significantly reduce system latency and energy consumption. Driven by silicon photonics, on-chip PNNs achieve high-density integration on scalable platforms, promoting the evolution of optoelectronic integration systems. This paper presents a comprehensive review of emerging integrated PNN technology for AI, employing a cross-layer framework that spans from fundamental physical devices to system-level implementation. Based on the requirements of PNNs, this review analyzes key hardware devices and mainstream computing core architectures, evaluates integration strategies and discusses advanced system packaging. Furthermore, the review provides a prospect on the field by identifying the remaining technical challenges and highlighting the transformative potential of PNNs as the infrastructure for next-generation AI.