🤖 AI Summary
This work addresses the low efficiency and high risk of probe and sample damage in large-scale, multimodal scanning probe microscopy by proposing a closed-loop autonomous workflow. The approach leverages multitask Gaussian process modeling to capture both spatial and cross-modal correlations, extending active learning from spatial sampling to dynamic selection of measurement modalities for non-colocated multimodal characterization. By integrating minimally invasive imaging with contact-based measurements within an automated large-sample atomic force microscopy platform, the method enables efficient, simultaneous acquisition of tapping-mode topography and DART (Dual AC Resonance Tracking) response maps across an AlScN composition-gradient wafer. This significantly enhances the throughput and intelligence of large-area multimodal nanoscale characterization.
📝 Abstract
Scanning probe microscopy provides nanoscale access to structural, electrical, electromechanical, magnetic, and mechanical properties of materials. Its increasing use for wafer-scale characterization and combinatorial materials exploration creates a need to distribute measurements efficiently across large spatial domains. This is particularly important when available modalities differ in acquisition time and potential for tip and sample damage, making exhaustive multimodal mapping over spatial grids impractical. Here, we demonstrate multitask scanning probe microscopy, a live, closed-loop workflow in which a multitask Gaussian process learns spatial and cross-modal relationships and autonomously selects both the next measurement location and the next experimental protocol. The approach is implemented on an automated large-sample atomic force microscope and demonstrated on a composition-spread AlScN wafer using tapping-mode and Dual AC Resonance Tracking (DART) measurements. Paired initial measurements establish the relation between the tasks, after which noncoincident measurements are used to update both response landscapes. The resulting workflow extends active learning in scanning probe microscopy from spatial sampling to autonomous allocation of measurement modalities and provides a basis for combining rapid, weakly perturbative imaging with slower contact, electrical, electromechanical, magnetic, or spectroscopic measurements.