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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
Figure 1. Schematic diagram of the on-chip computational spectrometer based on reconfigurable silicon photonic filters.
图 2 光谱重建过程示意图
Figure 2. Spectral reconstruction procedure.
图 3 光谱重建算法示意图
Figure 3. The schematic of the spectrum reconstruction algorithm.
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
Figure 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
Figure 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
Figure 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
Figure 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
Figure 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
Figure 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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