Optical microspectrometers and chemical classifiers based on silicon nanowires, plasmonic metasurfaces and machine learning

© 2021 Jiajun MengSpectrometers are a workhorse tool of optics, with applications ranging from scientific research to industrial process monitoring, remote sensing, and medical diagnostics. Although benchtop systems offer high performance and stability, alternative platforms offering reduced size, weight and cost could enable a host of new applications, e.g. in consumer personal electronics and field-deployable diagnostic platforms. To contribute to this trend towards miniaturised optical systems including spectrometers, this thesis presents the realisation of a visible spectrum microspectrometer using structurally coloured silicon nanowires and a reconstruction algorithm. We also experimentally demonstrate a plasmonic mid-infrared filter array-detector array microspectrometer that uses machine learning to determine the chemical compositions of a variety of liquids and solids. In this dissertation, we first present a reconstructive microspectrometer based on vertical silicon nanowire photodetectors. The nanowire photodetectors are designed to have absorption peaks across the visible spectrum. The spectral positions of these peaks are controlled by the radii of the nanowires. The nanowire detectors sit on substrate mesas that also serve as photodetectors for the light transmitted through the nanowires. We demonstrate the fabrication of this device, which has a footprint of a few millimetres. We use it as a spectrometer for the visible spectrum by implementing reconstructive algorithms. The identification of chemicals from their mid-infrared spectra has applications that include the industrial production of chemicals, food production, pharmaceutical manufacturing, and environmental monitoring. This is generally done using laboratory tools such as the Fourier transform infrared spectrometer. To address the need for fast and portable chemical sensing tools, we demonstrate the concept of a chemical classifier based on a filter array-detector array mid-infrared microspectrometer and a machine learning classification algorithm. Our device consists of a thermal camera onto which we have added an array of plasmonic filters. We perform simulations to find design parameters to enable the filters to have spectral features covering the wavelength range of interest. We first investigate this concept via a simulation study. We simulate the data that the device would generate when subjected to different chemicals, including noise. The simulated data is collated to train machine learning classification models. Our model predicts that this approach would be able to classify liquid and gas chemicals with very high accuracy. We later verify this concept by experimentally demonstrating a liquid chemical classifier. We design and fabricate a gold plasmonic filter chip containing 20 filters. The chip is integrated into a thermal camera to realise the mid-infrared microspectrometer platform. We train classifiers using the collected readout data of liquid analytes. The trained liquid classifier can accurately identify each type of analyte