Year
Month
(Peer-Reviewed) Benchmarking deep learning-based models on nanophotonic inverse design problems
Taigao Ma 马太高 ¹, Mustafa Tobah ², Haozhu Wang 王浩竹 ³, L. Jay Guo 郭凌杰 ³
¹ Department of Physics, The University of Michigan, Ann Arbor, Michigan, 48109, USA
² Department of Materials Science and Engineering, The University of Michigan, Ann Arbor, Michigan, 48109, USA
³ Department of Electrical Engineering and Computer Science, The University of Michigan, Ann Arbor, Michigan, 48109, USA
Opto-Electronic Science, 2022-01-07
Abstract

Photonic inverse design concerns the problem of finding photonic structures with target optical properties. However, traditional methods based on optimization algorithms are time-consuming and computationally expensive. Recently, deep learning-based approaches have been developed to tackle the problem of inverse design efficiently.

Although most of these neural network models have demonstrated high accuracy in different inverse design problems, no previous study has examined the potential effects under given constraints in nanomanufacturing. Additionally, the relative strength of different deep learning-based inverse design approaches has not been fully investigated.

Here, we benchmark three commonly used deep learning models in inverse design: Tandem networks, Variational Auto-Encoders, and Generative Adversarial Networks. We provide detailed comparisons in terms of their accuracy, diversity, and robustness. We find that tandem networks and Variational Auto-Encoders give the best accuracy, while Generative Adversarial Networks lead to the most diverse predictions.

Our findings could serve as a guideline for researchers to select the model that can best suit their design criteria and fabrication considerations. In addition, our code and data are publicly available, which could be used for future inverse design model development and benchmarking.
Benchmarking deep learning-based models on nanophotonic inverse design problems_1
Benchmarking deep learning-based models on nanophotonic inverse design problems_2
Benchmarking deep learning-based models on nanophotonic inverse design problems_3
Benchmarking deep learning-based models on nanophotonic inverse design problems_4
  • High-resolution tumor marker detection based on microwave photonics demodulated dual wavelength fiber laser sensor
  • Jie Hu, Weihao Lin, Liyang Shao, Chenlong Xue, Fang Zhao, Dongrui Xiao, Yang Ran, Yue Meng, Panpan He, Zhiguang Yu, Jinna Chen, Perry Ping Shum
  • Opto-Electronic Advances
  • 2024-12-16
  • High performance laser induced plasma assisted ablation by GHz burst mode femtosecond pulses
  • Jingbo Yin, Yulong Zhao, Minghui Hong
  • Opto-Electronic Advances
  • 2024-12-16
  • Sequential harmonic spin–orbit angular momentum generation in nonlinear optical crystals
  • Yutao Tang, Zixian Hu, Junhong Deng, Kingfai Li, Guixin Li
  • Opto-Electronic Advances
  • 2024-12-16
  • Design, setup, and facilitation of the speckle structured illumination endoscopic system
  • Elizabeth Abraham, Zhaowei Liu
  • Opto-Electronic Science
  • 2024-12-13
  • Ultra-high-Q photonic crystal nanobeam cavity for etchless lithium niobate on insulator (LNOI) platform
  • Zhi Jiang, Cizhe Fang, Xu Ran, Yu Gao, Ruiqing Wang, Jianguo Wang, Danyang Yao, Xuetao Gan, Yan Liu, Yue Hao, Genquan Han
  • Opto-Electronic Advances
  • 2024-10-31
  • Advanced biological imaging techniques based on metasurfaces
  • Yongjae Jo, Hyemi Park, Hyeyoung Yoon, Inki Kim
  • Opto-Electronic Advances
  • 2024-10-31
  • Orthogonal matrix of polarization combinations: concept and application to multichannel holographic recording
  • Shujun Zheng, Jiaren Tan, Hongjie Liu, Xiao Lin, Yusuke Saita, Takanori Nomura, Xiaodi Tan
  • Opto-Electronic Advances
  • 2024-10-23
  • Streamlined photonic reservoir computer with augmented memory capabilities
  • Changdi Zhou, Yu Huang, Yigong Yang, Deyu Cai, Pei Zhou, Kuenyao Lau, Nianqiang Li, Xiaofeng Li
  • Opto-Electronic Advances
  • 2024-10-22
  • High-precision multi-focus laser sculpting of microstructured glass
  • Kang Xu, Peilin Huang, Lingyu Huang, Li Yao, Zongyao Li, Jiantao Chen, Li Zhang, Shaolin Xu
  • Opto-Electronic Advances
  • 2024-10-09
  • Multi-physical field null medium: new solutions for the simultaneous control of EM waves and heat flow
  • Sailing He, Ruili Zhang, Junbo Liang
  • Opto-Electronic Advances
  • 2024-09-30
  • Adaptive decentralized AI scheme for signal recognition of distributed sensor systems
  • Shixiong Zhang, Hao Li, Cunzheng Fan, Zhichao Zeng, Chao Xiong, Jie Wu, Zhijun Yan, Deming Liu, Qizhen Sun
  • Opto-Electronic Advances
  • 2024-09-29
  • Data-driven polarimetric approaches fuel computational imaging expansion
  • Sylvain Gigan
  • Opto-Electronic Advances
  • 2024-09-28



  • Lymphangiogenesis contributes to exercise-induced physiological cardiac growth                                Two-photon absorption and stimulated emission in poly-crystalline Zinc Selenide with femtosecond laser excitation
    About
    |
    Contact
    |
    Copyright © PubCard