Deep learning accelerated discovery of photonic power dividers

Author:

Alagappan Gandhi1,Png Ching Eng1

Affiliation:

1. Agency for Science, Technology, and Research (A-STAR) , Institute of High-Performance Computing, Fusionopolis , 1 Fusionopolis Way, #16-16 Connexis , Singapore 138632 , Singapore

Abstract

Abstract This article applies deep learning-accelerated inverse design algorithms and discovers a spectrum of photonic power dividers with exceptional performance metrics despite the simplicity in the design geometry. The deep learning models exhibit high precisions on the order of 10−6 to 10−8 for both TE and TM polarizations of light. These models enable ultrafast search for an empirically describable subspace that simultaneously satisfy compact footprints, ultralow losses, ultrawide bandwidth, and exceptional robustness against fabrication randomness. We demonstrate a spectrum of devices for silicon photonics with programmable power splitting ratios, excess losses as small as 0.14 dB, to the best of our knowledge, the smallest footprints on the scale of sub-λ 2, and low loss bandwidths covering the whole telecommunication spectrum of O, S, E, C, L and U-bands. The robustness of the devices is statistically checked against the fabrication randomness and are numerically verified using the full three-dimensional finite difference time domain calculation.

Publisher

Walter de Gruyter GmbH

Subject

Electrical and Electronic Engineering,Atomic and Molecular Physics, and Optics,Electronic, Optical and Magnetic Materials,Biotechnology

Cited by 2 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Advances in machine learning optimization for classical and quantum photonics;Journal of the Optical Society of America B;2024-02-01

2. Dimensionality Reduction via Geometric Modeling of Topology-Optimized Devices for Machine Learning-based Inverse Design;2024 4th International Conference on Neural Networks, Information and Communication (NNICE);2024-01-19

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