Identifying surface elements in STM images using neural networks

Ehud Fuchs

doi:10.1117/12.2585276

5 March 2021 Identifying surface elements in STM images using neural networks

Ehud Fuchs

Proceedings Volume 11635, Optical Fibers and Sensors for Medical Diagnostics, Treatment and Environmental Applications XXI; 116350W (2021) https://doi.org/10.1117/12.2585276
Event: SPIE BiOS, 2021, Online Only

Abstract

Scanning Tunneling Microscopy (STM) can easily image hydrogen-passivated silicon(001) with atomic resolution, but images often contain artifacts such as double tips, blunt tips or unstable tips. Nevertheless, a trained human eye can recognize surface details such as dimer rows, step edges, depassivated silicon, etc. A Neural Network could classify the surface better than the human eye. One advantage of a deep learning algorithm is that it can analyze, in parallel, information from multiple channels such as topography, tunneling current, forward and reverse scans. Our scanning software also collects Tunnel Barrier Height information that contributes extra information about the electronic properties of the surface at each point. This identification will be integrated into ZyVector – our STM controller product -- to provide more accurate depiction of the surface than is available in the STM image, and to automate Hydrogen Depassivation Lithography (HDL) patterning.

Conference Presentation

Citation Download Citation

Ehud Fuchs "Identifying surface elements in STM images using neural networks", Proc. SPIE 11635, Optical Fibers and Sensors for Medical Diagnostics, Treatment and Environmental Applications XXI, 116350W (5 March 2021); https://doi.org/10.1117/12.2585276

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