Resolving the subsurface structure and mechanical property of layered films via contact-resonance atomic force microscopy
Gheorghe Stan, Cristian Ciobanu, Sean King
As an important development of the last decades, mechanical properties of nanoscale materials have been extracted from nanoindentation and atomic force microscopy. These techniques rely on contact mechanics models to convert the measured probe response into mechanical quantities associated with a sample material or surface, either at specific locations or over mapped regions. However, because an entire volume underneath the surface contributes to the mechanical response acquired, we surmise that a detailed 3D subsurface characterization can emerge from the collected response. In contrast to routine subsurface imaging that have shown capabilities for qualitative feature detection, we present here a methodology that correlated measurements and modeling to provide quantitative detection of mechanical properties and layer depth. We have carried out stringent tests of the methodology using flat layered films and demonstrating quantitative sensing of depth and layer properties, using load-dependent contact resonance atomic force microscopy (CR-AFM). The only features of our samples come from the subsurface, in the form of flat layers with different thickness and elastic moduli. While our correlative methodology is capable of detecting layer depth and elastic modulus, we have also investigated the limits of depth and mechanical property sensing, and revealed their interdependence. The methodology and results presented in this article can be useful in semiconductor electronics, additive manufacturing, paints, composites, and biological materials, especially in cases where layered materials are to be tested via non-destructive means.
, Ciobanu, C.
and King, S.
Resolving the subsurface structure and mechanical property of layered films via contact-resonance atomic force microscopy, ACS Applied Materials and Interfaces, [online], https://doi.org/10.1021/acsami.2c17962, https://tsapps.nist.gov/publication/get_pdf.cfm?pub_id=934802
(Accessed December 10, 2023)