Artificial Intelligence-Assisted Workflow for Transmission Electron Microscopy
From Data Analysis Automation to Materials Knowledge Unveiling
Marc Botifoll (BIST)
Ivan Pinto-Huguet (BIST)
Enzo Rotunno (Istituto Nanoscienze, Consiglio Nazionale delle Ricerche)
Thomas Galvani (BIST)
Catalina Coll (BIST)
Payam Habibzadeh Kavkani (Istituto Nanoscienze, Consiglio Nazionale delle Ricerche, Università Degli Studi di Modena e Reggio Emilia)
Maria Chiara Spadaro (University of Catania)
Giordano Scappucci (TU Delft - Quantum Circuit Architectures and Technology, TU Delft - QCD/Scappucci Lab, Kavli institute of nanoscience Delft)
Peter Krogstrup (TU Delft - QRD/Kouwenhoven Lab, University of Copenhagen)
undefined More Authors (External organisation)
More Info
expand_more
Other than for strictly personal use, it is not permitted to download, forward or distribute the text or part of it, without the consent of the author(s) and/or copyright holder(s), unless the work is under an open content license such as Creative Commons.
Abstract
(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, an analytical workflow is introduced 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 of systems limited with at least one axis of translational invariance –3D finite element and atomic models of millions of atoms–enabling simulations that provide crucial insights into device behavior 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 the workflow is demonstrated through its application to a wide array of materials systems, device configurations, and sample morphologies.
help_outline