A drift‐aware framework for reliability assessment and image‐based interpretation of atomic force microscopy measurements

Abstract 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 (), 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, , 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.