Kavli Affiliate: Giordano Scappucci
| First 5 Authors: Marc Botifoll, Ivan Pinto-Huguet, Enzo Rotunno, Thomas Galvani, Catalina Coll
| Summary:
This article introduces a groundbreaking analytical workflow designed for the
holistic characterisation, modelling and physical simulation of device
heterostructures. Our innovative workflow autonomously, comprehensively and
locally characterises the crystallographic information and 3D orientation of
the crystal phases, the elemental composition, and the strain maps of devices
from (scanning) transmission electron microscopy data. It converts a manual
characterisation process that traditionally takes days into an automatic
routine completed in minutes. This is achieved through a physics-guided
artificial intelligence model that combines unsupervised and supervised machine
learning in a modular way to provide a representative 3D description of the
devices, materials structures, or samples under analysis. To culminate the
process, we integrate the extracted knowledge to automate the generation of
both 3D finite element and atomic models of millions of atoms acting as digital
twins, enabling simulations that yield essential physical and chemical insights
crucial for understanding the device’s behaviour in practical applications. We
prove this end-to-end workflow with a state-of-the-art materials platform based
on SiGe planar heterostructures for hosting coherent and scalable spin qubits.
Our workflow connects representative digital twins of the experimental devices
with their theoretical properties to reveal the true impact that every atom in
the structure has on their electronic properties, and eventually, into their
functional quantum performance. Notably, the versatility of our workflow is
demonstrated through its successful application to a wide array of materials
systems, device configurations and sample morphologies.
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