High-Resolution Optical Convolutional Neural Networks Using Phase-Change Material-Based Microring Hybrid Waveguides

Abstract

In the More-than-Moore era, the explosive growth of data and information has driven the exploration of alternative non-von Neumann computational paradigms. Photonic neuromorphic computing has emerged as a promising approach, offering high speed, wide bandwidth, and massive parallelism. Herein, a high-resolution optical convolutional neural network (OCNN) is introduced using phase-change material Ge₂Sb₂Te₅ (GST)-based microring hybrid waveguides. This on-chip optical computing platform integrates GST into photonic devices, enabling versatile programming and in-memory computing capabilities. Central to this platform is a photonic convolutional computational kernel, constructed from photonic switching cells embedded with GST on a microring resonator. This programmable photonic switch leverages the refractive index modulation during the GST phase transition to achieve up to 64 discrete levels of transmission contrast, suitable for representing matrix elements in neural network algorithms with 6-bit resolution. Using these matrix elements, an OCNN capable of performing parallelized image edge detection and digital recognition tasks with high accuracy is demonstrated. The architecture is scalable for large-scale photonic neural networks, offering ultrahigh computational throughput, a compact design, complementary metal-oxide-semiconductor-compatible fabrication, and broad bandwidth.