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使用等离子体滤波器阵列和改进的多层残差卷积神经网络的高灵敏度计算小型化太赫兹光谱仪。

High-sensitivity computational miniaturized terahertz spectrometer using a plasmonic filter array and a modified multilayer residual CNN.

作者信息

Liu Mengjuan, Yang Meichen, Zhu Jiaqi, Zhu He, Wang Yao, Ren Ziyang, Zhai Yihui, Zhu Haiming, Shan Yufeng, Qi Hongxing, Duan Junli, Wu Huizhen, Dai Ning

机构信息

Zhejiang Province Key Laboratory of Quantum Technology and Devices, School of Physics, and State Key Laboratory of Silicon and Advanced Semiconductor Materials, Zhejiang University, Hangzhou 310058, China.

Hangzhou Institute for Advanced Study, University of Chinese Academy of Sciences, Hangzhou, Zhejiang 310024, China.

出版信息

Nanophotonics. 2023 Nov 2;12(23):4375-4385. doi: 10.1515/nanoph-2023-0581. eCollection 2023 Nov.

DOI:10.1515/nanoph-2023-0581
PMID:39634719
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC11501244/
Abstract

Spectrometer miniaturization is desired for handheld and portable applications, yet nearly no miniaturized spectrometer is reported operating within terahertz (THz) waveband. Computational strategy, which can acquire incident spectral information through encoding and decoding it using optical devices and reconstruction algorithms, respectively, is widely employed in spectrometer miniaturization as artificial intelligence emerges. We demonstrate a computational miniaturized THz spectrometer, where a plasmonic filter array tailors the spectral response of a blocked-impurity-band detector. Besides, an adaptive deep-learning algorithm is proposed for spectral reconstructions with curbing the negative impact from the optical property of the filter array. Our spectrometer achieves modest spectral resolution (2.3 cm) compared with visible and infrared miniaturized spectrometers, outstanding sensitivity (e.g., signal-to-noise ratio, 6.4E6: 1) superior to common benchtop THz spectrometers. The combination of THz optical devices and reconstruction algorithms provides a route toward THz spectrometer miniaturization, and further extends the applicable sphere of the THz spectroscopy technique.

摘要

对于手持式和便携式应用而言,光谱仪小型化是很有必要的,但几乎没有报道称有在太赫兹(THz)波段工作的小型化光谱仪。随着人工智能的出现,计算策略在光谱仪小型化中得到广泛应用,该策略可分别通过光学器件对入射光谱信息进行编码以及使用重建算法对其进行解码来获取光谱信息。我们展示了一种计算型小型化太赫兹光谱仪,其中一个等离子体滤波器阵列调整了一个阻挡杂质带探测器的光谱响应。此外,还提出了一种自适应深度学习算法用于光谱重建,以抑制滤波器阵列光学特性带来的负面影响。与可见光和红外小型化光谱仪相比,我们的光谱仪实现了适度的光谱分辨率(2.3厘米),具有优于普通台式太赫兹光谱仪的出色灵敏度(例如,信噪比为6.4E6:1)。太赫兹光学器件与重建算法的结合为太赫兹光谱仪小型化提供了一条途径,并进一步扩展了太赫兹光谱技术的应用范围。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3c48/11501244/a1af174370c0/j_nanoph-2023-0581_fig_005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3c48/11501244/a0f079f68d9d/j_nanoph-2023-0581_fig_001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3c48/11501244/68cab2a8b2af/j_nanoph-2023-0581_fig_002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3c48/11501244/d3718a6d5d68/j_nanoph-2023-0581_fig_003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3c48/11501244/0f6861080120/j_nanoph-2023-0581_fig_004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3c48/11501244/a1af174370c0/j_nanoph-2023-0581_fig_005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3c48/11501244/a0f079f68d9d/j_nanoph-2023-0581_fig_001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3c48/11501244/68cab2a8b2af/j_nanoph-2023-0581_fig_002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3c48/11501244/d3718a6d5d68/j_nanoph-2023-0581_fig_003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3c48/11501244/0f6861080120/j_nanoph-2023-0581_fig_004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3c48/11501244/a1af174370c0/j_nanoph-2023-0581_fig_005.jpg

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本文引用的文献

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