Authors:

Manaswini Singh

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About Paper:

Atomic-scale quantum defects in solid-state platforms hold immense promise for the next generation technologies in quantum sensing, communication, and computation. Exploring their full potential demands exceptional control over defect creation, placement, and coherent behavior of individual defects. Two dimensional materials, especially hexagonal boron nitride(hBN), provide a compelling platform for quantum defects due to their atomically smooth surface, wide bandgap, and tunable heterointerfaces. We plan to utilize Scanning Tunneling Microscopy (STM) to probe engineered quantum defects in hBN with atomic precision, and this presentation will focus on implementing the technique to fabricate hBN/graphite heterostructures compatible with STM measurements. We acquire isolated few-layer graphene and hBN through scotch-tape exfoliation and assemble them into heterostructures using a Polypropylene Carbonate (PPC) stamp. We employ PPC films on a cushion-like PDMS to pick up and assemble layers of thin hBN and graphite over a thick sample of hBN over a silicon wafer, achieving flat and atomically clean interfaces essential for STM measurement. Notably, the number of hBN layers determines its dielectric and electronic landscape, which can directly impact our STM measurement. To accelerate and standardize the process of picking optimal hBN samples, I am developing a machine learning model that predicts the number of hBN layers from optical images and AFM data. By integrating all aspects of our experimental journey, our work will establish a pathway for exploring engineered quantum defects in 2D systems with atomic precision, ultimately enabling quantum sensing applications. Keywords: Quantum Sensing; 2D Materials; Machine Learning; Scanning Tunneling Microscopy (STM); Quantum Defects

Source:

Purdue University / 2025

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Co-authors:

Manaswini Singh