Automatic 2D material detection and quantum emission prediction using deep learning-based models

Abstract

The 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.