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On-chip Fourier-transform spectrometers and machine learning: a new route to smart photonic sensors

机译：片上傅里叶变换光谱仪和机器学习：智能光子传感器的新路线

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摘要

Miniaturized silicon photonics spectrometers capable of detecting specific absorption features have great potential for mass market applications in medicine, environmental monitoring, and hazard detection. However, state-of-the-art silicon spectrometers are limited by fabrication imperfections and environmental conditions, especially temperature variations, since uncontrolled temperature drifts of only 0.1 degrees C distort the retrieved spectrum precluding the detection and classification of the absorption features. Here we present a new strategy that exploits the robustness of machine learning algorithms to signal imperfections, enabling recognition of specific absorption features in a wide range of environmental conditions. We combine on-chip spatial heterodyne Fourier-transform spectrometers and supervised learning to classify different input spectra in the presence of fabrication errors, without temperature stabilization or monitoring. We experimentally show the differentiation of four different input spectra under an uncontrolled 10 degrees C range of temperatures, about 100x increase in operational range, with a success rate up to 82.5% using state-of-the-art support vector machines and artificial neural networks. (C) 2019 Optical Society of America
机译：能够检测特定吸收特征的小型化硅光子光谱仪具有巨大的药物，环境监测和危害检测中的大众市场应用潜力。然而，最先进的硅光谱仪受到制造缺陷和环境条件的限制，特别是温度变化，因为不受控制的温度漂移仅为0.1摄氏度扭曲的检测和分类吸收特征。在这里，我们提出了一种新的策略，利用机器学习算法的稳健性来信号缺陷，从而能够在各种环境条件下识别特定的吸收特征。我们将片上空间外差傅立叶变换光谱仪结合起来并监督学习在制造错误的情况下对不同的输入光谱进行分类，而无需温度稳定或监测。我们通过在不受控制的10摄氏度范围内进行实验显示四种不同输入光谱的差异，运行范围增加约100倍，使用最先进的支持向量机和人工神经网络的成功率高达82.5％ 。 （c）2019年光学学会

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