Electrical and Electronic Engineering - Theses
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ItemOptical microspectrometers and chemical classifiers based on silicon nanowires, plasmonic metasurfaces and machine learning( 2021)Spectrometers 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.
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ItemDevelopment of Multispectral Image Sensors by Exploring Nanophotonics( 2020)A multispectral image camera system captures image data within specific wavelength ranges in narrow spectral bands across the electromagnetic spectrum. In the recent years, image sensors integrated with multiple optical filters with narrow spectral width have been widely used for most of the multispectral imaging across multiple applications, such as area imaging, medical detection, object identification, remote sensing and so on. There were two kinds of multispectral imaging system reported before. The first one is multispectral image cameras that combines multiple cameras mounted with optical bandpass filters and optics with different peak wavelengths and their spectral width depends on applications. The second imaging system is a single sensor based multispectral camera that integrates multiple filters (called filter mosaic) on a single image sensor. The existing filter mosaic fabrication technology disclosed so far is using multilayer coating technique and requires highly accurate alignment with micro-lithography facility. Based on this manufacturing process, each filter has to be fabricated separately with multiple steps, such as baking, exposure, development which significantly increases the fabrication complication and cost. This limits the wide use of this promising multispectral imaging in many applications. This thesis investigates the development of new low-cost single sensor based multispectral cameras using different filter mosaic technologies exploring plasmonics, multilayer coating based on heterostructured dielectrics or hybrid metal-dielectric structures. The thesis starts with an introduction, Chapter 1 presenting the filter technologies, simulation techniques and fabrication technologies. This is followed by presenting a novel technique to enhance the transmission efficiency of plasmonic colour filters based on the coaxial hole array in Chapter 2. Chapter 3 demonstrates CMY camera (cyan, magenta and yellow) using subtractive colour mixing. A colour filter mosaic made of metal-dielectric-metal nanorods is developed and then integrated on a MT9P031 CMOS image sensor to demonstrate its performance. In Chapter 4, the multispectral image camera based on a single sensor is developed using a hybrid filter mosaic integrated onto a Sony monochrome image sensor. Moreover, the multispectral imaging algorithm is used to reconstruct a colour image of a 24 - patch Macbeth Chart. Later, this image sensor was integrated with a DJI drone for the area imaging application. Chapter 5 presents new multispectral filter technologies which is polarization and incident angle independent. Lastly, Chapter 6 presents conclusions and discusses the future research directions. Appendix presents an optical bandpass filter mosaic and multispectral camera based on a mass producible filter technology with spectral width of only 17nm in the near IR wavelength and this technology is confidential and licensed as a trade secret to the University of Melbourne. Therefore, only parts of the technology is disclosed in the appendix due to a company formation (PIXsensor).