2024 APS March Meeting
Monday–Friday, March 4–8, 2024;
Minneapolis & Virtual
Session F04: Advances in Moiré Assembly
8:00 AM–10:24 AM,
Tuesday, March 5, 2024
Room: L100D
Chair: Hugh Churchill, University of Arkansas
Abstract: F04.00010 : 2D Quantum Materials Pipelines for automated production of 2D heterostructures at the MonArk NSF Quantum Foundry*
10:12 AM–10:24 AM
Abstract
Presenter:
Nicholas Borys
(Montana State University and MonArk NSF Quantum Foundry)
Author:
Nicholas Borys
(Montana State University and MonArk NSF Quantum Foundry)
Layered two-dimensional (2D) materials enable seemingly endless opportunities to engineer 2D heterostructures by assembling stacks of atomically thin 2D sheets with distinct structural, optical, electronic, and magnetic properties. With interlayer van der Waals bonding, the interfaces between the layers can be pristine, and the selection of the atomic sheets to be integrated as well as their assembly do not need to account for chemical and structural compatibility between the individual layers. Untethered from such limitations and equipped with a vast library of 2D materials, the design and fabrication of novel 2D heterostructures to explore new emergent phenomena or precisely tailor functionalities for technological applications can be intensely pursued with unprecedented freedom. However, the current procedures for fabricating 2D heterostructures are manual, heavily relying on humans to perform the critical processes of mechanically exfoliating bulk crystals, identifying single layers, and stacking individual layers on top of one another. The reliance on manual processes makes 2D heterostructure fabrication tedious, plagues it with low yields, and limits the rate at which results can be reproduced and confirmed. In this talk, I will provide an overview of the 2D Quantum Materials Pipelines (2D-QMaPs) of the MonArk NSF Quantum Foundry that aim to overcome these challenges by automating the assembly of 2D heterostructures with robotic devices for mechanical exfoliation, optical identification of single layers, and stacking of layers into an assembled heterostructures. By leveraging industrial automation technologies and implementing machine learning and artificial intelligence, the 2D-QMaPs significantly accelerate the rate at which these key steps are performed. Select use cases demonstrate how substantially improved reliability and repeatability facilitate faster and higher-quality production of 2D heterostructures as well as more rapid exploration of new 2D material systems. I will discuss how the 2D-QMaPs are integrated with nanofabrication capabilities to realize an end-to-end assembly line for 2D quantum devices that is ultimately intended to provide samples and devices to the overall community of 2D materials researchers.
*Supported by the NSF under award DMR-1906383