Deep‐learning powered whispering gallery mode sensor based on multiplexed imaging at fixed frequency

作     者：Anton V.Saetchnikov Elina A.Tcherniavskaia Vladimir A.Saetchnikov Andreas Ostendorf Anton V. Saetchnikov;Elina A. Tcherniavskaia;Vladimir A. Saetchnikov;Andreas Ostendorf

作者机构：Applied Laser TechnologiesRuhr University BochumBochum 44801Germany Radio Physics DepartmentBelarusian State UniversityMinsk 220064Belarus Physics DepartmentBelarusian State UniversityMinsk 220030Belarus 

出 版 物：《Opto-Electronic Advances》 (光电进展（英文）)

年 卷 期：2020年第3卷第11期

页      面：1-9页

核心收录：

学科分类：12[管理学] 1201[管理学-管理科学与工程(可授管理学、工学学位)] 080202[工学-机械电子工程] 081104[工学-模式识别与智能系统] 08[工学] 0835[工学-软件工程] 0802[工学-机械工程] 0811[工学-控制科学与工程] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

主　　题：optical microresonator sensing machine learning whispering gallery mode multiplexing 

摘      要：During the last decades the whispering gallery mode based sensors have become a prominent solution for label-free sensing of various physical and chemical *** the same time,the widespread utilization of the approach is hindered by the restricted applicability of the known configurations for ambient variations quantification outside the laboratory conditions and their low affordability,where necessity on the spectrally-resolved data collection is among the main limiting *** this paper we demonstrate the first realization of an affordable whispering gallery mode sensor powered by deep learning and multi-resonator imaging at a fixed *** has been shown that the approach enables refractive index unit(RIU)prediction with an absolute error at 3×10^(-6) level for dynamic range of the RIU variations from 0 to 2×10^(-3) with temporal resolution of several milliseconds and instrument-driven detection limit of 3×10−*** sensing accuracy together with instrumental affordability and production simplicity places the reported detector among the most cost-effective realizations of the whispering gallery mode *** proposed solution is expected to have a great impact on the shift of the whole sensing paradigm away from the model-based and to the flexible self-learning solutions.