Atomic Defects of the Hydrogen-Terminated Silicon Surface Imaged with nc-AFM

The hydrogen-terminated silicon (H:Si) surface has been shown to be a viable candidate for the development of atom-scale devices^1,2; however, the creation of such devices is currently limited by the need for constant user-guided interaction during the fabrication process. Attempts to automate fabrication using deep learning successfully demonstrated the training of a neural network which was able to identify common defects³. Building upon these recent works we present our efforts at creating a comprehensive catalog containing many of the commonly found defects of the H:Si-100(2x1) surface. By imaging the defects using different imaging parameters in STM and two different tip contrasts in non-contact AFM⁴, we determine the structures of the defects, their likely origins and potential removal, and a path to improved accuracy for their automated detection. A deeper understanding will enable the creation of better defect-free samples for atomic fabrication.

1. Huff, T. et al. Nat. Electron. 1, 636–643 (2018)
2. Fuechsle, M. et al. Nat. Nanotechnol. 7, 242–246 (2012).
3. Rashidi, M. et al. arXiv:1902.08818 (2019).
4. Sharp, P. et al. Appl. Phys. Lett. 100, 233120 (2012).