Photonics Research, 2021, 9 (2): 02000B38, Published Online: Jan. 22, 2021   

Smart ring resonator–based sensor for multicomponent chemical analysis via machine learning Download: 722次

Author Affiliations
1 National Key Laboratory of Science and Technology on Micro/Nano Fabrication, Institute of Microelectronics, Peking University, Beijing 100871, China
2 Quantum Science and Engineering Centre, Nanyang Technological University, Singapore 639798, Singapore
3 Institute of Microelectronics, A*STAR (Agency for Science, Technology and Research), Singapore 138634, Singapore
4 School of Mechanical and Aerospace Engineering, Nanyang Technological University, Singapore 639798, Singapore
5 e-mail: haoyl@pku.edu.cn
6 e-mail: yi_zhang@ntu.edu.sg
7 e-mail: eaqliu@ntu.edu.sg
Abstract

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 root-mean-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.

Zhenyu Li, Hui Zhang, Binh Thi Thanh Nguyen, Shaobo Luo, Patricia Yang Liu, Jun Zou, Yuzhi Shi, Hong Cai, Zhenchuan Yang, Yufeng Jin, Yilong Hao, Yi Zhang, Ai-Qun Liu. Smart ring resonator–based sensor for multicomponent chemical analysis via machine learning[J]. Photonics Research, 2021, 9(2): 02000B38.

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