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Abstract

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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Corresponding author:Huang Bei-Ju,bjhuang@semi.ac.cn

Funds:Project supported by the National Natural Science Foundation of China (Grant No. 62341508) and the Youth Innovation Promotion Association of Chinese Academy of Sciences (Grant No. Y2022045).

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[1]
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[3]
[4]
[5]
[6]
[7]
[8]
[9]
[10]
[11]
[12]
[13]
[14]
[15]
[16]
[17]
[18]
[19]
[20]
[21]
[22]
[23]
[24]
[25]
[26]
[27]
[28]
[29]
[30]
[31]

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文献	器件面积 /μm²	分辨率 /nm	光学带宽 /nm	采样通道数
[20]	220×520	0.02	12 (2条窄光谱)	64
[22]	35×260	0.45	180	32
[23]	60×60	0.04	100	2501
[26]	2000×7600	0.03	125	256
[27]	1900×3700	0.01	200	729
本文	100×500 (估计值)	0.20	100	64

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[3]
[4]
[5]
[6]
[7]
[8]
[9]
[10]
[11]
[12]
[13]
[14]
[15]
[16]
[17]
[18]
[19]
[20]
[21]
[22]
[23]
[24]
[25]
[26]
[27]
[28]
[29]
[30]
[31]

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Vol. 73, Issue 14 - 2024-07-20

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