AI-equipped scanning probe microscopy for autonomous site-specific atomic-level characterization at room temperature

TL;DR

This paper introduces an AI-enhanced scanning probe microscopy system capable of autonomous, site-specific atomic-level surface analysis at room temperature, effectively addressing challenges like thermal drift and tip variations.

Contribution

The work presents a novel AI-integrated SPM system that autonomously identifies atomic sites, compensates for thermal effects, and performs precise measurements without human intervention.

Findings

Successfully navigated thermal drift and tip fluctuations at room temperature

Autonomously identified defect-free regions on Si(111)-(7x7) surface

Performed extensive current-voltage spectroscopy at various adatom sites

Abstract

We present an advanced scanning probe microscopy system enhanced with artificial intelligence (AI-SPM) designed for self-driving atomic-scale measurements. This system expertly identifies and manipulates atomic positions with high precision, autonomously performing tasks such as spectroscopic data acquisition and atomic adjustment. An outstanding feature of AI-SPM is its ability to detect and adapt to surface defects, targeting or avoiding them as necessary. It's also engineered to address typical challenges such as positional drift and tip apex atomic variations due to the thermal effect, ensuring accurate, site-specific surface analyses. Our tests under the demanding conditions of room temperature have demonstrated the robustness of the system, successfully navigating thermal drift and tip fluctuations. During these tests on the Si(111)-(7x7) surface, AI-SPM autonomously identified defect-free regions and performed a large number of current-voltage spectroscopy measurements at different adatom sites, while autonomously compensating for thermal drift and monitoring probe health. These experiments produce extensive data sets that are critical for reliable materials characterization and demonstrate the potential of AI-SPM to significantly improve data acquisition. The integration of AI into SPM technologies represents a step toward more effective, precise and reliable atomic-level surface analysis, revolutionizing materials characterization methods.