Building Scalable Silicon Microring Resonator‐Based Neuromorphic Photonic Circuits Using Post‐Fabrication Processing with Photochromic Material

作者：Lei Xu, Jiawei Zhang, Eli A. Doris, Simon Bilodeau, Jesse A. Wisch, Manting Gui, Yusuf O. Jimoh, Bhavin J. Shastri, Barry P. Rand, Paul R. Prucnal · 发表于：Advanced Optical Materials · 年份：2025 · DOI：10.1002/adom.202402706 · 被引用次数：9 · 研究领域：Neural Networks and Reservoir Computing、Photonic and Optical Devices、Advanced Memory and Neural Computing

Abstract Neuromorphic photonics has become one of the research forefronts in photonics, with its benefits in low‐latency signal processing and potential in significant energy consumption reduction when compared with digital electronics. With artificial intelligence (AI) computing accelerators in high demand, one of the high‐impact research goals is to build scalable neuromorphic photonic integrated circuits which can accelerate the computing of AI models at high energy efficiency. A complete neuromorphic photonic computing system comprises seven stacks: materials, devices, circuits, microarchitecture, system architecture, algorithms, and applications. Here, we consider microring resonator (MRR)‐based network designs toward building scalable silicon integrated photonic neural networks (PNN), and variations of MRR resonance wavelength from the fabrication process and their impact on PNN scalability. Further, post‐fabrication processing using organic photochromic layers over the silicon platform is shown to be effective for trimming MRR resonance wavelength variation, which can significantly reduce energy consumption from the MRR‐based PNN configuration. Post‐fabrication processing with photochromic materials to compensate for the variation in MRR fabrication will allow a scalable silicon system on a chip without sacrificing today's performance metrics, which will be critical for the commercial viability and volume production of large‐scale silicon photonic circuits.