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Article Dans Une Revue Nanoscale Horizons Année : 2023

Bringing ultimate depth to scanning tunnelling microscopy: deep subsurface vision of buried nano-objects in metals

Oleg Kurnosikov
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Muriel Sicot
Emilie Gaudry
Yuan Lu
Stéphane Mangin

Résumé

A method for subsurface visualization and characterization of hidden subsurface nano-structures based on scanning tunelling microscopy/spectroscopy (STM/STS) has been developed. Nano-objects buried under a metal surface up to several tens of nanometers can be visualized through the metal surface and characterized with STM without destroying the sample. This non-destructive method exploits quantum well (QW) states formed by partial electron confinement between the surface and buried nano-objects. The specificity of STM allows for nano-objects to be singled out and easily accessed. Their burial depth can be determined by analysing the oscillatory behaviour of the electron density at the surface of the sample, while the spatial distribution of electron density can give additional information about their size and shape. The proof of concept was demonstrated with different materials such as Cu, Fe, and W in which the nanoclusters of Ar, H, Fe and Co were buried. For each material, the maximal depth of subsurface visualisation is determined by the material parameters and ranges from several nanometers to several tens of nanometers. To demonstrate the ultimate depth of subsurface STM-vision as the principal limit of our approach, the system of Ar nanoclusters embedded into a single-crystalline Cu(110) matrix has been chosen since it represents the best combination of the mean free path, smooth interface and inner electron focusing. With this system we experimentally demonstrated that Ar nanoclusters of several nanometers large buried as deep as 80 nm can still be detected, characterized and imaged. The ultimate depth of this ability is estimated to be 110 nm. This approach using QW states paves the way for enhanced 3D characterization of nanostructures hidden well below a metallic surface.
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Origine : Publication financée par une institution
Licence : CC BY - Paternité

Dates et versions

hal-04102036 , version 1 (22-05-2023)

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Paternité

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Oleg Kurnosikov, Muriel Sicot, Emilie Gaudry, Danielle Pierre, Yuan Lu, et al.. Bringing ultimate depth to scanning tunnelling microscopy: deep subsurface vision of buried nano-objects in metals. Nanoscale Horizons, 2023, 8 (7), pp.900-911. ⟨10.1039/D3NH00052D⟩. ⟨hal-04102036⟩
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