4.7 Article

Mid-Infrared Gas Classification Using a Bound State in the Continuum Metasurface and Machine Learning

期刊

IEEE SENSORS JOURNAL
卷 23, 期 19, 页码 22389-22398

出版社

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/JSEN.2023.3305598

关键词

Optical filters; Sensors; Metasurfaces; Band-pass filters; Detectors; Gratings; Photodetectors; Gas sensors; infrared spectra; machine learning; metasurfaces; optical filters; optical gratings

向作者/读者索取更多资源

Mid-infrared spectroscopy enables nondestructive and real-time chemical identification. To meet the demand for in situ sensing, novel sensing technologies have been developed that minimize size, weight, power, and cost. These technologies include mid-IR microspectrometers that interface a detector array with an array of discrete spectral filters. The study explores the use of all-dielectric, single-layer, coupled waveguide-grating structures as filters in a filter-array detector-array spectrometer, and predicts accurate classification of common acyclic hydrocarbons and detection of single-gas down to low concentrations.
Mid-infrared (mid-IR) spectroscopy enables nondestructive and real-time chemical identification. Emerging demand for in situ sensing has motivated the development of novel sensing technologies that minimize size, weight, power, and cost. These technologies include mid-IR microspectrometers that interface a detector array with an array of discrete spectral filters. This specific approach is compatible with many different filter technologies that provide distinct tradeoffs in spectral selectivity, fabricability, and optical efficiency. A family of all-dielectric, single-layer, coupled waveguide-grating structures excels in all these aspects but has not yet been considered. These filters permit spectrally isolated, linewidth-tunable transmission features via quasi-bound state in the continuum resonances. Here, we study a filter-array detector-array spectrometer to exploit these filters, for the first time to the best of our knowledge. Through simulations that incorporate a machine learning classifier (MLC), we predict accurate classification of common acyclic hydrocarbons down to concentrations of 75 ppm using a 10-cm optical path length, as well as a single-gas limit of detection (LoD) of 32 ppm.

作者

我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。

评论

主要评分

4.7
评分不足

次要评分

新颖性
-
重要性
-
科学严谨性
-
评价这篇论文

推荐

暂无数据
暂无数据