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| Auteurs principaux: | , , , , , , , , , , , , , , , , , , , |
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| Format: | Preprint |
| Publié: |
2024
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| Accès en ligne: | https://arxiv.org/abs/2411.01024 |
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| _version_ | 1866912671230590976 |
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| author | Botifoll, Marc Pinto-Huguet, Ivan Rotunno, Enzo Galvani, Thomas Coll, Catalina Kavkani, Payam Habibzadeh Spadaro, Maria Chiara Niquet, Yann-Michel Eriksen, Martin Borstad Marti-Sanchez, Sara Katsaros, Georgios Scappucci, Giordano Krogstrup, Peter Isella, Giovanni Cabot, Andreu Merino, Gonzalo Ordejon, Pablo Roche, Stephan Grillo, Vincenzo Arbiol, Jordi |
| author_facet | Botifoll, Marc Pinto-Huguet, Ivan Rotunno, Enzo Galvani, Thomas Coll, Catalina Kavkani, Payam Habibzadeh Spadaro, Maria Chiara Niquet, Yann-Michel Eriksen, Martin Borstad Marti-Sanchez, Sara Katsaros, Georgios Scappucci, Giordano Krogstrup, Peter Isella, Giovanni Cabot, Andreu Merino, Gonzalo Ordejon, Pablo Roche, Stephan Grillo, Vincenzo Arbiol, Jordi |
| contents | (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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2411_01024 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Artificial Intelligence-Assisted Workflow for Transmission Electron Microscopy: From Data Analysis Automation to Materials Knowledge Unveiling Botifoll, Marc Pinto-Huguet, Ivan Rotunno, Enzo Galvani, Thomas Coll, Catalina Kavkani, Payam Habibzadeh Spadaro, Maria Chiara Niquet, Yann-Michel Eriksen, Martin Borstad Marti-Sanchez, Sara Katsaros, Georgios Scappucci, Giordano Krogstrup, Peter Isella, Giovanni Cabot, Andreu Merino, Gonzalo Ordejon, Pablo Roche, Stephan Grillo, Vincenzo Arbiol, Jordi Materials Science (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. |
| title | Artificial Intelligence-Assisted Workflow for Transmission Electron Microscopy: From Data Analysis Automation to Materials Knowledge Unveiling |
| topic | Materials Science |
| url | https://arxiv.org/abs/2411.01024 |