Condensed Matter/Materials & Biological Physics Seminar with Mihir Pendharkar on Enabling Predictable Twistronics

Mihir Pendharkar is a Q-FARM Bloch Postdoctoral Fellow at Stanford University working in the group of Prof. Dave Schuster and Prof. David Goldhaber-Gordon focusing on materials for solid-state superconducting-qubit based quantum computing and on making 2D materials stacking more uniform, reproducible and repeatable. Mihir was previously a postdoctoral researcher at UC Santa Barbara where he also received his PhD and MS in Electrical and Computer Engineering specializing in molecular beam epitaxy of superconductor-semiconductor heterostructures for applications in topological quantum computation using Majorana Zero Modes.

Moire superlattices formed in dissimilar or twisted 2D materials (a.k.a. twistronics) have been central to the observation of various novel phenomena in recent years - unconventional superconductivity, orbital ferromagnetism, ferroelectricity, to name a few. Yet, techniques to predictably fabricate and rapidly image moire superlattices in twisted Van der Waals (VdW) materials remain scarce. A change as small as a hundredth of a degree in interlayer twist angle, or fractions of a percent in hetero-strain, can lead to a measurable change in the moire wavelength and consequently their electronic properties and yet, samples where twist angle varies dramatically every few hundred nanometers are common.
In this talk, I will introduce a tool for robotic assembly of 2D materials heterostructures in vacuum, removing the uncertainty of manual operation. To rapidly characterize the samples prepared, we developed an AFM based technique - Torsional Force Microscopy (TFM), which relies on dynamic friction at the tip-sample interface to reveal moire superlattices. TFM operates in air, at room temperature without the need for any complex sample preparation and was also found to image atomic lattices of graphene and hBN as well as reveal sub-surface moires. TFM enables determination of precise structural information and coupled with robotic stacking, is expected to enable predictable fabrication of VdW heterostructures.