Theses and Dissertations at Montana State University (MSU)
Permanent URI for this collectionhttps://scholarworks.montana.edu/handle/1/733
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Item Automatic 2D material detection and quantum emission prediction using deep learning-based models(Montana State University - Bozeman, College of Engineering, 2023) Ramezani, Fereshteh; Chairperson, Graduate Committee: Brad WhitakerThe realm of quantum engineering holds immense promise for revolutionizing technological landscapes, particularly with the advent of 2D materials in quantum device applications. The fundamental properties of these materials make them pivotal in various quantum applications. However, the progress in quantum engineering faces significant roadblocks, primarily centered around two challenges: accurate 2D material detection and understanding the random nature of quantum fluctuations. In response to the first challenge, I have successfully implemented a new deep learning pipeline to identify 2D materials in microscopic images. I have used a state-of-the-art two-stage object detector and trained it on images containing flakes of varying thickness of hexagonal boron nitride (hBN, a 2D material). The trained model achieved a high detection accuracy for the rare category of thin flakes (< or = 50 atomic layers thick). My further analysis shows that this proposed pipeline is robust against changes in color or substrate background, and could be generalized to various microscope settings. As an achievement, I have integrated my proposed method to the 2D quantum material pipeline (2D-QMaP), that has been under development by the MonArk Quantum Foundry, to provide automated capabilities that unite and accelerate the primary stages of sample preparation and device fabrication for 2D quantum materials research. My proposed algorithm has given the 2D-QMaP fully automatic real-time 2D flake detection capabilities, which has never been done effectively before. To address the second challenge, I assessed the random nature of quantum fluctuations, and I developed time series forecasting deep learning models to analyze and predict quantum emission fluctuations for the first time. My trained models can roughly follow the actual trend of the data and, under certain data processing conditions, can predict peaks and dips of the fluctuations. The ability to anticipate these fluctuations will allow physicists to harness quantum fluctuation characteristics to develop novel scientific advances in quantum computing that will greatly benefit quantum technologies. The automated 2D material identification, addressing the laborious process of flake detection, and the introduction of innovative quantum fluctuations analysis with predictive capabilities not only streamline research processes but also hold the promise of creating more stable and dependable quantum emission devices, thus significantly advancing the broader field of quantum engineering.Item Investigation of nanoscale etching and poling of lithium niobate(Montana State University - Bozeman, College of Engineering, 2014) Smith, Stacie Elizabeth; Chairperson, Graduate Committee: Wataru NakagawaThe capabilities of some nonlinear optical devices can be improved through approaches such as nano-optics. Two methods, in particular, that can enhance the wavelength conversion efficiency and versatility of current second harmonic generation (SHG) devices are creating nanoscale domain inversions (to make for efficient quasi-phase matched SHG devices at various wavelengths) and gratings in lithium niobate (to potentially achieve exact-phase matching). This thesis explores these options, creating nanoscale domain inversions and nanostructuring lithium niobate, in order to enhance current SHG devices. First, an in-depth literature survey is provided detailing the current research regarding structuring lithium niobate. Next, a description and analysis of the inductively coupled plasma reactive ion etch (ICP-RIE) etching procedures used are provided, followed by a discussion of the poling of lithium niobate using an all optical poling technique. Suggestions for continued development are presented based on the successes and failures of the procedures used for this work. The goal of this thesis is to show that lithium niobate can be nanostructured using ICP-RIE etching techniques and optical poling methods. This goal is a foundation towards the long-term goal of building more efficient nonlinear optical devices. Nanostructuring lithium niobate suggests that improved nonlinear optical devices can be made in the future, by means of nanoscale domain inversions for quasi-phase matching or nanoengineered gratings intended for exact-phase matching.