Kavli Affiliate: Giordano Scappucci
| First 5 Authors: Marc Botifoll, Ivan Pinto-Huguet, Enzo Rotunno, Thomas Galvani, Catalina Coll
| Summary:
(Scanning) transmission electron microscopy ((S)TEM) has significantly
advanced materials science but faces challenges in correlating precise atomic
structure information with the functional properties of devices due to its
time-intensive nature. To address this, we introduce an analytical workflow for
the holistic characterization, modelling, and simulation of device
heterostructures. This workflow automates the experimental (S)TEM data
analysis, providing an in-depth characterization of crystallographic
information, 3D orientation, elemental composition, and strain distribution. It
reduces a process that typically takes days for a trained human into an
automatic routine solved in minutes. Utilizing a physics-guided artificial
intelligence model, it generates representative descriptions of materials and
samples. The workflow culminates in creating digital twins, 3D finite element
and atomic models of millions of atoms, enabling simulations that provide
crucial insights into device behaviour in practical applications. Demonstrated
with SiGe planar heterostructures for scalable spin qubits, the workflow links
digital twins to theoretical properties, revealing how atomic structure impacts
materials and functional properties such as spatially-resolved phononic or
electronic characteristics, or (inverse) spin orbit lengths. The versatility of
our workflow is demonstrated through its application to a wide array of
materials systems, device configurations, and sample morphologies.
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