A drift-aware framework for reliability assessment and image-based interpretation of atomic force microscopy...

Drift is one of the primary sources of uncertainty limiting the accuracy, repeatability, and reliability of scanning probe microscopy (SPM) measurements, with a particularly significant effect on the comparison of consecutive surface topography images. In this study, a two-module hybrid physical-computational framework is presented for the drift-aware interpretation and reliability assessment of consecutive AFM topography measurements. The first module integrates deep learning (DL)-based inter-frame correspondence extraction, geometric validation, physical coordinate reconstruction, scan-order-based acquisition-time assignment, inter-scan delay correction, and temperature synchronisation to generate a time-labelled drift representation. The second module uses this structured representation for comparative data-driven modelling and evaluates drift velocity ( $v_d$ ), model performance, and stability through temperature-included and temperature-excluded configurations. This structure enables the joint analysis of validated correspondences in terms of lateral displacement, relative height variation, acquisition time, $v_d$ , drift direction, and synchronously recorded ambient temperature, together with their physical coordinate labels. The results indicate that the proposed framework should be positioned not as a universal drift-correction method, but as a complementary and modular analysis approach that supports the drift-aware interpretation, comparison, and reliability assessment of consecutive AFM topography measurements.