Intelligent Design, Characterization and Fabrication of Nanophotonic Devices

Li Gao

State Key Laboratory for Organic Electronics and Information Displays, Institute of Advanced Materials, School of Materials Science and Engineering,

Nanjing University of Posts and Telecommunications, Nanjing 210023, China

Email: iamlgao@njupt.edu.cn

Abstract

Light is the most abundant and sustainable energy resource on earth. Efficient harvest and control of light waves are always the key research focus. In this talk, we will discuss a few examples that aim to achieve sustainable development of nanophotonic devices. Deep learning enabled algorithms can enable direct inverse design of nanophotonics which avoid iterative optimization and save significant amount of computing power. Deep learning algorithms can also be utilized to characterize nanophotonic device parameters that is hard to be probed by traditional bulky, expensive tools, which can partially replace or complement current material characterization techniques. Nanophotonics and algorithms enabled miniaturized computational spectrometer is another good example of spectroscopic technology advancement that enable future light-weight, portable and flexible hardware. This talk will end with illustration of large-area, high-throughput and flexible fabrication of nanophotonic devices with preliminary exploration of dissolvable and transient plasmonic devices.

References

[1] Zhixiao Zhang and Li Gao, 2-bit coding metasurface with a double layer random flip structure for wide band diffuse reflection and reciprocity protected transmission. Optics Express 31(20), 32253-32262, 2023.

[2] Yibo Xiao, Xinyi Cao, Qiao Dong and Li Gao,*All Dissolvable and Transient Plasmonic Device Enabled by Nanoimprint Lithography. Nanotechnology 34, 295301, 2023.

[3] Wenqi Wang, Qiao Dong, Zhixiao Zhang, Hao Cao, Jin Xiang* and Li Gao*, Inverse Design of Photonic Crystal Filters with Arbitrary Correlation and Size for Accurate Spectrum Reconstruction. Applied Optics 62(8), 1907-1914, 2023.

[4] Huijuan Zhao, Xinyi Cao, Qiao Dong, Chunyuan Song, Lianhui Wang* and Li Gao*, Large-Area Silicon Photonic Crystal Supporting Bound States in the Continuum and optical sensing Formed by Nanoimprint Lithography. Nanoscale Advances 5, 1291-1298, 2023.

[5] Qiao Dong, Wenqi Wang, Xinyi Cao, Yibo Xiao, Xiaohan Guo, Jingxuan Ma, Lianhui Wang* and Li Gao*, Plasmonic Nanostructure Characterized by Deep Neural Network Assisted Spectroscopy (invited). Chinese Optics Letter 21(1), 010004, 2023.

[6] Xinyi Cao, Yibo Xiao, Qiao Dong, Shaobo Zhang, Junzhuan Wang, Lianhui Wang* and Li Gao*, Tuning Metasurface Dimensions by Soft Nanoimprint Lithography and Reactive Ion Etching. Advanced Photonics Research 2200127, 2022.

[7] Li Gao*, Yurui Qu, Lianhui Wang* and Zongfu Yu*, Computational Spectrometers Enabled by nanophotonics and Deep Learning. Nanophotonics 11(11), 2507-2929, 2022.

[8] Qingxin Wu, Xiaozhong Li, Wenqi Wang, Qiao Dong, Yibo Xiao, Xinyi Cao, Lianhui Wang*, and Li Gao* Comparison of Different Neural Network Architectures for Plasmonic Inverse Design. ACS Omega 6(36), 23076-2308, 2021.

[9] Qingxin Wu, Xiaozhong Li, Li Jiang, Xiao Xu, Dong Fang, Jingjing Zhang, Chunyuan Song, Zongfu Yu, Lianhui Wang, and Li Gao*, Deep neural network for designing near- and far-field properties in plasmonic antennas. Optical Materials Express 11(7), 1907-1917, 2021.

[10] Jinran Qie, Erfan Khoram, Dianjing Liu, Ming Zhou, and Li Gao*, Real-time Deep Learning Design Tool for Far-Field Radiation Profile. Photonics Research 9(4) B104-B108, 2021.

[11] Li Jiang, Xiaozhong Li, Qingxin Wu, Lianhui Wang, and Li Gao*, Neural Network Enabled Metasurface Design for Phase Manipulation. Optics Express 29(2) 2521-2528, 2021.

[12] Li Gao* Xiaozhong Li, Dianjing Liu, Lianhui Wang, and Zongfu Yu, A Bidirectional Deep Neural Network for Accurate Silicon Color Design, Advanced Materials 31(51) 1905467, 2019.

[13] Xiaozhong Li, Jing Shu, Wenhua Gu, and Li Gao*, Deep neural network for plasmonic sensor Modeling. Optical Materials Express 9(9), 3857-3862, 2019.