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相比于笨重的台式光谱仪, 集成化的芯片级光谱仪可以应用于便携式的健康监测、环境检测等场景 . 我们设计了一个基于硅光子平台的片上光谱仪 . 该器件由一个透射光谱可重构的硅光滤波器构成 . 通过改变滤波器的透射光谱, 可以实现对输入光谱的多次且不同的采样 . 再结合人工神经网络算法, 从采样后的信号中重建出入射光谱 . 可重构的硅光滤波器由互相耦合的马赫曾德干涉仪和微环谐振腔组成 . 采用集成的热光相移器引入相位变化, 能够对滤波器的透射光谱进行重构 . 通过这种方式, 基于单个可重构滤波器可得到包含宽、窄光谱多样特征的响应函数 . 不需要滤波器阵列, 就可以实现对入射光谱的多样化采样, 能够显著地减小光谱检测器件的面积 . 仿真结果表明, 所设计的器件在1500—1600 nm波长范围内可以实现连续光谱和稀疏光谱的重建, 分辨率约为0.2 nm . 该器件在可穿戴光学传感、便携式光谱仪等场景中具有巨大的应用潜力 .
Spectroscopic analysis technique is an indispensable tool in many disciplines such as biomedical research, materials science, and remote sensing. Traditional benchtop spectrometers have several drawbacks; bulky, complex, and expensive, making them ineffective for emerging applications such as wearable health monitoring and Lab-on-Chip systems. Compared with bulky desktop spectrometers, integrated chip-level spectrometers find many applications in portable health monitoring, environmental sensing, and other scenarios. We design an on-chip spectrometer based on a silicon photonics platform. The device consists of a silicon photonic filter with a reconfigurable transmission spectrum. By changing the transmission spectrum of the filter, the multiple and diverse sampling of the input spectrum can be obtained. Using an artificial neural network algorithm, the incident spectrum is reconstructed from the sampled signals. The reconfigurable silicon photonic filter is composed of intercoupled Mach-Zehnder interferometer and micro-ring resonator. The introduction of thermal-optic phase shifter facilitates the reconstruction of the transmission spectrum of filter. Through this approach, a response function encompassing diverse features of broad and narrow spectra can be obtained from a single reconfigurable filter, eliminating the need for a filter array and significantly reducing the footprint of the spectrometer. Simulation results demonstrate that the designed device can achieve continuous and sparse spectrum reconstruction in a wavelength range of 1500–1600 nm, with a resolution of approximately 0.2 nm. On a test set composed of synthetic spectra, the calculated average RMSE for the reconstructed spectra is 0.0075, with an average relative error of 0.0174. Owing to the reconfigurable nature of the silicon photonic filter, this device exhibits the ability to flexibly adjust the number of sampling channels, thus enabling users to configure the chip according to specific application scenarios. This device possesses significant potential applications such as in wearable optical sensors and portable spectrometers. 
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Keywords:
- computational spectral reconstruction /
- reconfigurable silicon photonic filters /
- deep learning /
- photonic integrated circuits
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图 1 基于可重构硅光滤波器的片上光谱仪结构示意图
Fig. 1. Schematic diagram of the on-chip computational spectrometer based on reconfigurable silicon photonic filters.
图 4 (a) 仿真得到的采样矩阵热图; (b) 4个不同状态的透射光谱
Fig. 4. (a) Heat map of simulated sampling matrix of the devices; (b) transmission spectra of 4 different states.
图 5 片上光谱仪的32个采样状态(64个采样通道)下透射光谱的相关系数
Fig. 5. Correlation coefficient of transmission spectra under 32 sampling states (64 sampling channels) of the on-chip spectrometer.
图 6 不同类型光谱的模拟重建结果 (a) 合成宽光谱; (b) 合成窄光谱; (c), (d) ASE光源光谱
Fig. 6. Simulated reconstruction results of different types of spectra: (a) Synthetic broad spectrum; (b) synthetic narrow spectrum; (c), (d) ASE light source spectra.
图 7 FWHM为1 nm的窄光谱模拟重建结果
Fig. 7. Narrow spectrum reconstruction results with a FWHM of 1 nm
图 8 间隔0.2 nm的窄双峰光谱模拟重建结果
Fig. 8. Simulated reconstruction results of a narrow double peak spectrum separated by 0.2 nm.
图 9 采样矩阵的平均相关系数、重建相对误差随光子滤波器的采样状态数量增大而减小
Fig. 9. The average correlation coefficient of the sampling matrix and the reconstruction relative error decrease as the number of sampling states of the photon filter increases.
表 1 已报道的基于滤波器的计算光谱仪的性能比较
Table 1. Performance comparison of reported filter-based computational spectrometers.
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[1] Manley M 2014 Chem. Soc. Rev. 43 8200
Google Scholar
[2] Bacon C P, Mattley Y, DeFrece R 2004 Rev. Sci. Instrum. 75 1
Google Scholar
[3] Clark R N, Roush T L 1984 J. Geophys. Res. Solid Earth 89 6329
Google Scholar
[4] Gao L, Qu Y, Wang L, Yu Z 2022 Nanophotonics 11 2507
Google Scholar
[5] Wang J, Zheng B, Wang X 2021 J. Phys. Photonics 3 012006
Google Scholar
[6] Redding B, Liew S F, Sarma R, Cao H 2013 Nat. Photonics 7 746
Google Scholar
[7] Hartmann W, Varytis P, Gehring H, Walter N, Beutel F, Busch K, Pernice W 2020 Adv. Opt. Mater. 8 1901602
Google Scholar
[8] Kwak Y, Park S M, Ku Z, Urbas A, Kim Y L 2021 Nano Lett. 21 921
Google Scholar
[9] Hartmann W, Varytis P, Gehring H, Walter N, Beutel F, Busch K, Pernice W 2020 Nano Lett. 20 2625
Google Scholar
[10] Hadibrata W, Noh H, Wei H, Krishnaswamy S, Aydin K 2021 Laser Photonics Rev. 15 2000556
Google Scholar
[11] Xiong J, Cai X S, Cui K Y, Huang Y D, Yang J W, Zhu H B, Li W Z, Hong B, Rao S J, Zheng Z K, Xu S, He Y H, Liu F, Feng X, Zhang W 2022 Optica 9 461
Google Scholar
[12] Craig B, Shrestha V R, Meng J, Cadusch J J, Crozier K B 2018 Opt. Lett. 43 4481
Google Scholar
[13] Wang Z, Yi S, Chen A, Zhou M, Luk T S, James A, Nogan J, Ross W, Joe G, Shahsafi A, Wang K X, Kats M A, Yu Z 2019 Nat. Commun. 10 1020
Google Scholar
[14] Zhu Y B, Lei X, Wang K X Z, Yu Z F 2019 Photonics Res. 7 961
Google Scholar
[15] Bao J, Bawendi M G 2015 Nature 523 67
Google Scholar
[16] Zhu X, Bian L, Fu H, Wang L, Zou B, Dai Q, Zhang J, Zhong H 2020 Light Sci. Appl. 9 73
Google Scholar
[17] Piels M, Zibar D 2017 Sci. Rep. 7 43454
Google Scholar
[18] Redding B, Liew S F, Bromberg Y, Sarma R, Cao H 2016 Optica 3 956
Google Scholar
[19] Kim C, Ni P, Lee K R, Lee H N 2022 Sci. Rep. 12 4053
Google Scholar
[20] Zhang Z, Li Y, Wang Y, Yu Z, Sun X, Tsang H K 2021 Laser Photonics Rev. 15 2100039
Google Scholar
[21] Wen J, Hao L, Gao C, Wang H, Mo K, Yuan W, Chen X, Wang Y, Zhang Y, Shao Y, Yang C, Shen W 2023 ACS Photonics 10 225
Google Scholar
[22] Li A, Fainman Y 2021 Nat. Commun. 12 2704
Google Scholar
[23] Xu H, Qin Y, Hu G, Tsang H K 2023 Light Sci. Appl. 12 64
Google Scholar
[24] Yuan S, Naveh D, Watanabe K, Taniguchi T, Xia F 2021 Nat. Photonics 15 601
Google Scholar
[25] Guo L, Sun H, Wang M, Wang M, Min L, Cao F, Tian W, Li L 2022 Adv. Mater. 34 2200221
Google Scholar
[26] Yao C, Chen M, Yan T, Ming L, Cheng Q, Penty R 2023 Light Sci. Appl. 12 156
Google Scholar
[27] Yao C, Xu K, Zhang W, Chen M, Cheng Q, Penty R 2023 Nat. Commun. 14 6376
Google Scholar
[28] Zhang S, Dong Y, Fu H, Huang S L, Zhang L 2018 Sensors 18 644
Google Scholar
[29] Kim C, Park D, Lee H N 2020 Sensors 20 594
Google Scholar
[30] Zhang W, Song H, He X, Huang L, Zhang X, Zheng J, Shen W, Hao X, Liu X 2021 Light Sci. Appl. 10 108
Google Scholar
[31] 涂鑫, 陈震旻, 付红岩 2019 68 104210
Google Scholar
Tu X, Chen Z M, Fu H Y 2019 Acta Phys. Sin. 68 104210
Google Scholar
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