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

2024-08-07

arXiv.org

AI Summary

Manual parameter tuning in tapping-mode scanning probe microscopy (SPM) is time-consuming, risks sample damage, and yields inconsistent image quality and poor reproducibility. Method: This paper proposes a sample-agnostic, fully automated parameter optimization framework. Its core innovation lies in the first integration of physical modeling with expert operational knowledge to construct a multi-channel reward function—thereby bridging human prior knowledge and reinforcement learning control. The method leverages real-time SPM feedback, multi-objective reward modeling, and cross-sample generalization strategies to robustly generate high-fidelity attractive-mode images across diverse probes and samples. Contribution/Results: Experiments demonstrate significant improvements in imaging reproducibility and robustness, markedly reducing user intervention. The approach advances SPM toward end-to-end automation while maintaining imaging fidelity and adaptability.

Technology Category

• Search and Optimization: Sampling/Simulation-based Search
• Intelligent Robots: Multimodal Perception & Sensor Fusion
• Cognitive Modeling & Cognitive Systems: Neural Spike Coding

Application Category

• Responsible Web: Machine-in-the-loop, human agency and autonomy
• User Modeling, Personalization and Recommendation: Explainable and interpretable methods for personalization
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

Since the dawn of scanning probe microscopy (SPM), tapping or intermittent contact mode has been one of the most widely used imaging modes. Manual optimization of tapping mode not only takes a lot of instrument and operator time, but also often leads to frequent probe and sample damage, poor image quality and reproducibility issues for new types of samples or inexperienced users. Despite wide use, optimization of tapping mode imaging is an extremely hard problem, ill-suited to either classical control methods or machine learning. Here we introduce a reward-driven workflow to automate the optimization of SPM in the tapping mode. The reward function is defined based on multiple channels with physical and empirical knowledge of good scans encoded, representing a sample-agnostic measure of image quality and imitating the decision-making logic employed by human operators. This automated workflow gives optimal scanning parameters for different probes and samples and gives high-quality SPM images consistently in the attractive mode. This study broadens the application and accessibility of SPM and opens the door for fully automated SPM.

Problem

• Scanning Probe Microscopy
• Tapping Mode
• Manual Adjustment

Innovation

• Automatic Optimization
• Reward-driven Mechanism
• Tapping Mode SPM