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Overview of Microscopy Techniques01:22

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Machine Learning-Based Reward-Driven Tuning of Scanning Probe Microscopy: Toward Fully Automated Microscopy.

Yu Liu1, Roger Proksch1,2, Jason Bemis2

  • 1Department of Materials Science and Engineering, University of Tennessee, Knoxville, Tennessee 37996, United States.

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|May 19, 2025
PubMed
Summary
This summary is machine-generated.

Automating scanning probe microscopy (SPM) tapping mode optimization using a reward-driven workflow significantly improves efficiency and reliability. This method ensures consistent, high-quality images across various samples and probes, reducing operator time and potential damage.

Keywords:
Bayesian optimizationSPMactive learningautomated experimentintelligent automationreward-driventapping mode

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Area of Science:

  • Materials Science
  • Nanotechnology
  • Surface Science

Background:

  • Tapping mode (intermittent contact mode) is a widely used imaging technique in scanning probe microscopy (SPM).
  • Manual optimization of tapping mode is time-consuming, operator-dependent, and can lead to sample/probe damage and poor reproducibility.
  • Existing control and machine learning methods struggle with the complex optimization of tapping mode imaging.

Purpose of the Study:

  • To develop a novel reward-driven workflow for automating the optimization of SPM tapping mode.
  • To create a sample-agnostic measure of image quality that mimics human operator decision-making.
  • To enhance the efficiency, consistency, and reliability of tapping mode SPM operation.

Main Methods:

  • A reward-driven workflow was designed to automate SPM tapping mode parameter optimization.
  • A reward function was developed, incorporating physical and empirical knowledge of scan quality.
  • The workflow was tested across diverse probes and sample types.

Main Results:

  • The automated workflow successfully determined optimal scanning parameters for tapping mode.
  • The system produced consistent, high-quality images across various probes and samples.
  • The reward function provided a sample-agnostic measure of image quality.

Conclusions:

  • The developed reward-driven workflow offers an efficient and reliable method for optimizing SPM tapping mode.
  • This automation reduces the need for manual adjustments, saving time and minimizing damage.
  • The approach demonstrates potential for improving SPM usability for new users and complex samples.