Spectroscopy is a technique that analyzes the interaction between matter and light as a function of wavelength. It is the most convenient method for obtaining qualitative and quantitative information about an unknown sample with reasonable accuracy. However, traditional spectroscopy is reliant on bulky and expensive spectrometers, while emerging applications of portable, low-cost and lightweight sensing and imaging necessitate the development of miniaturized spectrometers. In this study, we have developed a computational spectroscopy method that can provide single-shot operation, sub-nanometer spectral resolution, and direct materials characterization. This method is enabled by a metasurface integrated computational spectrometer and deep learning algorithms. The identification of critical parameters of optical cavities and chemical solutions is demonstrated through the application of the method, with an average spectral reconstruction accuracy of 0.4 nm and an actual measurement error of 0.32 nm. The mean square errors for the characterization of cavity length and solution concentration are 0.53 % and 1.21 %, respectively. Consequently, computational spectroscopy can achieve the same level of spectral accuracy as traditional spectroscopy while providing convenient, rapid material characterization in a variety of scenarios.

中文翻译：

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深度学习支持的快照计算光谱学


光谱学是一种分析物质与光之间的相互作用作为波长函数的技术。它是以合理的精度获取未知样品的定性和定量信息的最便捷方法。然而，传统光谱学依赖于笨重且昂贵的光谱仪，而便携式、低成本、轻量级传感和成像的新兴应用则需要开发小型化光谱仪。在这项研究中，我们开发了一种计算光谱方法，可以提供单次操作、亚纳米光谱分辨率和直接材料表征。该方法由超表面集成计算光谱仪和深度学习算法实现。应用该方法验证了光学腔和化学溶液关键参数的识别，平均光谱重建精度为0.4 nm，实际测量误差为0.32 nm。腔长度和溶液浓度表征的均方误差分别为 0.53% 和 1.21%。因此，计算光谱可以实现与传统光谱相同水平的光谱精度，同时在各种情况下提供方便、快速的材料表征。