Smart ring resonator-based sensor for multicomponent chemical analysis via machine learning

被引：29

作者：

Li, Zhenyu ^{[1
,2
]}

Zhang, Hui ^{[2
]}

Binh Thi Thanh Nguyen ^{[2
]}

Luo, Shaobo ^{[2
]}

Liu, Patricia Yang ^{[2
]}

Zou, Jun ^{[2
]}

Shi, Yuzhi ^{[2
]}

Cai, Hong ^{[3
]}

Yang, Zhenchuan ^{[1
]}

Jin, Yufeng ^{[1
]}

Hao, Yilong ^{[1
]}

Zhang, Yi ^{[2
,4
]}

Liu, Ai-Qun ^{[2
]}

机构：

[1] Peking Univ, Inst Microelect, Natl Key Lab Sci & Technol Micro Nano Fabricat, Beijing 100871, Peoples R China

[2] Nanyang Technol Univ, Quantum Sci & Engn Ctr, Singapore 639798, Singapore

[3] ASTAR, Inst Microelect, Singapore 138634, Singapore

[4] Nanyang Technol Univ, Sch Mech & Aerosp Engn, Singapore 639798, Singapore

来源：

PHOTONICS RESEARCH | 2021年 / 9卷 / 02期

基金：

新加坡国家研究基金会;

关键词：

LEAST-SQUARES REGRESSION;

D O I：

10.1364/PRJ.411825

中图分类号：

O43 [光学];

学科分类号：

070207 ; 0803 ;

摘要：

We demonstrate a smart sensor for label-free multicomponent chemical analysis using a single label-free ring resonator to acquire the entire resonant spectrum of the mixture and a neural network model to predict the composition for multicomponent analysis. The smart sensor shows a high prediction accuracy with a low rootmean-squared error ranging only from 0.13 to 2.28 mg/mL. The predicted concentrations of each component in the testing dataset almost all fall within the 95% prediction bands. With its simple label-free detection strategy and high accuracy, the smart sensor promises great potential for multicomponent analysis applications in many fields. (C) 2021 Chinese Laser Press

引用

页码：B38 / B44

页数：7

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