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AEcroscopy: A Software–Hardware Framework Empowering Microscopy Toward Automated and Autonomous Experimentation.

Authors :
Liu, Yongtao
Roccapriore, Kevin
Checa, Marti
Valleti, Sai Mani
Yang, Jan‐Chi
Jesse, Stephen
Vasudevan, Rama K.
Source :
Small Methods. Apr2024, p1. 11p. 11 Illustrations.
Publication Year :
2024

Abstract

Microscopy has been pivotal in improving the understanding of structure‐function relationships at the nanoscale and is by now ubiquitous in most characterization labs. However, traditional microscopy operations are still limited largely by a human‐centric click‐and‐go paradigm utilizing vendor‐provided software, which limits the scope, utility, efficiency, effectiveness, and at times reproducibility of microscopy experiments. Here, a coupled software–hardware platform is developed that consists of a software package termed AEcroscopy (short for Automated Experiments in Microscopy), along with a field‐programmable‐gate‐array device with LabView‐built customized acquisition scripts, which overcome these limitations and provide the necessary abstractions toward full automation of microscopy platforms. The platform works across multiple vendor devices on scanning probe microscopes and electron microscopes. It enables customized scan trajectories, processing functions that can be triggered locally or remotely on processing servers, user‐defined excitation waveforms, standardization of data models, and completely seamless operation through simple Python commands to enable a plethora of microscopy experiments to be performed in a reproducible, automated manner. This platform can be readily coupled with existing machine‐learning libraries and simulations, to provide automated decision‐making and active theory‐experiment optimization to turn microscopes from characterization tools to instruments capable of autonomous model refinement and physics discovery. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
23669608
Database :
Academic Search Index
Journal :
Small Methods
Publication Type :
Academic Journal
Accession number :
176665293
Full Text :
https://doi.org/10.1002/smtd.202301740