AEcroscoPy: A software-hardware framework empowering microscopy toward automated and autonomous experimentation

TL;DR

AEcroscoPy is a comprehensive software-hardware framework that automates microscopy experiments, enabling reproducibility, customization, and integration with machine learning for autonomous scientific discovery.

Contribution

The paper introduces a novel coupled FPGA and Python-based platform that automates and standardizes microscopy operations across multiple vendors, facilitating autonomous experimentation.

Findings

Supports multiple microscopy vendors and devices.

Enables fully automated, reproducible experiments.

Integrates with machine learning for decision-making.

Abstract

Microscopy, in particular scanning probe and electron microscopy, has been pivotal in improving our understanding of structure-function relationships at the nanoscale and is by now ubiquitous in most research characterization labs and facilities. However, traditional microscopy operations are still limited largely by a human-centric click-and-go paradigm utilizing vendor-provided software, which necessarily limits the scope, utility, efficiency, effectiveness, and at times reproducibility of microscopy experiments. Here, we develop a coupled hardware-software platform that consists of a field-programmable gate array (FPGA) device, with LabView-built customized acquisition scripts, along with a software package termed AEcroscoPy (short for Automated Experiments in Microscopy driven by Python) that overcome these limitations and provide the necessary abstractions towards full automation of microscopy platforms. The platform works across multiple vendor devices on scanning probe microscopes and scanning transmission 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 as well as simulations, to provide automated decision-making and active theory-experiment optimization loops to turn microscopes from characterization tools to instruments capable of autonomous model refinement and physics discovery.