Light coupling to photonic integrated circuits using optimized lensed fibers

作者
Dengke Chen,Zhenyang Zhong,Sanli Huang,Jiahao Sun,Sicheng Zeng,Baoqi Shi,Yihan Luo,Junqiu Liu
出处
期刊:Cornell University - arXiv [Cornell University]
标识
DOI:10.48550/arxiv.2510.10635
摘要

Efficient and reliable light coupling between optical fibers and photonic integrated circuits has arguably been the most essential issue in integrated photonics for optical interconnects, nonlinear signal conversion, neuromorphic computing, and quantum information processing. A commonly used approach is to use inverse tapers interfacing with lensed fibers, particularly for waveguides of relatively low refractive index, such as silicon nitride (Si3N4), silicon oxynitride, and lithium niobate. This approach simultaneously enables broad operation bandwidth, high coupling efficiency, and simplified fabrication. Although diverse taper designs have been invented and characterized to date, lensed fibers play equally important roles here, yet their optimization has long been underexplored. Here, we fill this gap and introduce a comprehensive co-optimization strategy that synergistically refines the geometries of the taper and the lensed fiber. By incorporating the genuine lensed fiber's shape into the simulation, we accurately capture its non-Gaussian emission profile, thereby nullifying the widely accepted approximation based on a paraxial Gaussian mode. We further characterize many lensed fibers and Si3N4 tapers of varying shapes using different fabrication processes. Our experimental and simulation results show remarkable agreement, both achieving maximum coupling efficiencies exceeding 80% per facet. Finally, we summarize the optimal choices of lensed fibers and Si3N4 tapers that can be directly deployed in modern CMOS foundries for scalable manufacturing of Si3N4 photonic integrated circuits. Our study not only contributes to light-coupling solutions but is also critical for photonic packaging and optoelectronic assemblies that are currently revolutionizing data centers and AI.
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