Integrated Linear Variable Filter/InGaAs Focal Plane Array Spectral Micro-module and Its Wavelength Calibration

Xu-quan, 王绪泉 Wang; Song-lei, 黄松垒 Huang; Yue-hua, 于月华 Yu; Jie-min, 叶捷敏 Ye; Xiumei, 邵秀梅 Shao; Jiaxiong, 方家熊 Fang

doi:10.3788/gzxb20184705.0530001

Cited by 4 publications

(1 citation statement)

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“…Continuously variable filter is an optical thin film device whose spectral characteristics gradually change with spatial position 1 – 4 As a spectroscopic element, it has the advantages of small volume, wide working range, and the passband wavelength can be designed arbitrarily 5 , 6 . At present, spectral detection instruments with continuously variable filter as the core spectroscopic element have been successfully applied in various fields such as aerospace, field exploration, atmospheric monitoring, food safety testing, biofluid analysis, and hyperspectral imaging 7 …”

Section: Introductionmentioning

confidence: 99%

Design of mask for depositing continuously variable filter

Gao,

Wei

2024

J. Nanophoton.

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Section: Introductionmentioning

confidence: 99%

Design of mask for depositing continuously variable filter

Gao,

Wei

2024

J. Nanophoton.

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Near‐Infrared InGaAs Intelligent Spectral Sensor by 3D Heterogeneous Hybrid Integration

Wang

Dong

Huang

et al. 2023

Advanced Photonics Research

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The applications of near‐infrared spectroscopy (NIRS) are limited due to the bulky size, low integration, and poor intelligence in edge computing of traditional spectrometers. In this work, the authors develop an on‐chip InGaAs intelligent spectral sensor, which consists of a linear variable filter for wavelength selection, a linear focal plane array as detector and a chip processor for neural‐network inferring, based on an advanced 3D heterogeneous hybrid‐integration. More than 200 spectral channels with a spectral resolution of 1.25% central wavelength are acquired in a wide waveband from 900 to 1700 nm. Immediate results are provided onsite for users, especially applicable to nonspecialists, owing to the embedded algorithmic model in‐sensor. As a proof of applications, the authors experimentally detect adulterating green tea and achieve a real‐time identification with accuracy better than 90%. This spectral sensor with edge‐artificial‐intelligence breaks the dependence on external algorithms and paves the way for NIRS into miniaturization, integration, and intelligence, which brings more possibilities in incorporating with the consumer devices and the Internet of Things.

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