Quantitative contact-resonance AFM reconstruction for artifact-reduced modulus and subsurface imaging on textured...

Contact-resonance atomic force microscopy (CR-AFM) has emerged as a leading technique for nanoscale subsurface detection owing to its noninvasive capability and high sensitivity. However, most applications have been limited to specimens with nearly flat surfaces, since large local inclinations introduce artifacts in the CR-frequency maps. Consequently, CR-based subsurface imaging remains severely constrained by topography-induced artifacts, which hinders its application to structured surfaces such as metasurfaces and microlens arrays. Here, we present a quantitative subsurface reconstruction strategy based on an analytical model that accounts for optical-lever sensitivity, applied force, and normal and lateral contact stiffness under varying local inclinations. This approach reduces topography-induced artifacts and enables high-resolution modulus and subsurface mapping on textured or inclined surfaces. The reconstructed modulus maps cover materials from approximately 5 to 163 GPa, with a propagated Type B standard uncertainty below 4% for the tested specimens. Moreover, it reconstructs subsurface features in specimens with topographically complex, textured surfaces-such as graphene-oxide (GO) membrane covered and single-point diamond turning (SPDT) machined silicon-revealing previously obscured buried structures. These results support the use of the proposed framework for artifact-reduced modulus and subsurface reconstruction on the tested topographically complex specimens, and indicate its potential for subsurface defect detection in structured micro/nano-scale surfaces.