Compact, efficient, and scalable nanobeam core for photonic matrix-vector multiplication

Abstract

Optical neural networks have emerged as a promising avenue for implementing artificial intelligence applications, with matrix computations being a crucial component. However, the existing implementations based on microring resonators (MRRs) face bottlenecks in integration, power efficiency, and scalability, hindering the practical applications of wavelength division multiplexing (WDM)-based matrix-vector multiplications at the hardware level. Here we present a photonic crystal nanobeam cavity (PCNC) matrix core. Remarkably compact with dimensions reduced to 20µm×0.5µm, the PCNC unit exhibits a thermal tuning efficiency more than three times that of MRRs. Crucially, it is immune to the free spectral range constraint, thus able to harness the wealth of independent wavelength channels provided by WDM. A 3×3 PCNC core chip is demonstrated for animal face recognition and a six-channel chip is employed for handwritten digit classification to demonstrate the scalability. The PCNC solution holds immense promise, offering a versatile platform for next-generation photonic artificial intelligence chips.