Researches on Whispering Gallery Microcavities and Machine Learning

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The nature of the proposed work is to develop a research program where two seemingly unrelated fields, whispering gallery microcavities and machine learning, are connected. On one side of the technology spectrum, whispering gallery microcavities are micron sized optical cavities capable of single molecule detection and nonlinear optical actuation. On the other side, machine learning algorithms are invaluable for discovering data patterns in high dimensional data generated from highly nonlinear systems despite their high power consumption and large demand on computation resources. Our proposed interdisciplinary research program contains three inter-related research sub-programs: a) develop a frequency microcomb and use it for ultra high resolution single molecule precision spectroscopy, b) single molecule spectral analysis using machine learning algorithms and c) develop a compact, fast and energy efficient learning machine for general purpose machine learning tasks. In sub-program a), we will develop a frequency microcomb using a lithium niobate whispering gallery microcavity and operate it in aqueous environments. This microcomb will be immersed in liquid suspension with protein molecules. When a single molecule lands on the microcomb surface, spectral change of the comb line will be recorded. Through electro-optical tuning, ultra-high spectral resolution will be achieved. In sub-program b), utilizing the power of machine learning, we will analyze the spectra obtained from sub-program a). We will use deep learning technologies such as feedforward, convolutional and recurrent neural networks, autoencoder, generative adversarial networks, etc., to identify molecules, recognize their physical and chemical composition, structural folding as well as understand and predict the interactions between single molecules. In sub-program c), we will utilize the unique properties of lithium niobate and the frequency microcomb to develop micron sized integrated photonic learning machines that can compute various machine learning models in a fast but energy efficient way. The proposed research will bring significant value to fundamental research on single molecules, which is a key element in many areas such as early cancer diagnosis and drug engineering. Our research is also important to environmental protection as it enables the monitoring of ultra low concentrations of chemical components in waste water bodies from mining sites or oil production. The novel photonic learning machine will result in enormous energy savings considering the currently prevalent usage of machine learning. Its unbeatable fast processing speed is critical in the upcoming 5G and internet of things revolution where ultra low lantency of machine learning is required in areas such as autonomous driving. The training of HQP through this program will bring to the related industry highly qualified personnels and thus contribute significantly to the Canadian economy.