Automated extraction of single H atoms with STM: tip state dependency

Abstract

The atomistic structure of the tip apex plays a crucial role in performing reliable atomic-scale surface and adsorbate manipulation using scanning probe techniques. We have developed an automated extraction routine for controlled removal of single hydrogen atoms from the H:Si(100) surface. The set of atomic extraction protocols detect a variety of desorption events during scanning tunneling microscope (STM)-induced modification of the hydrogen-passivated surface. The influence of the tip state on the probability for hydrogen removal was examined by comparing the desorption efficiency for various classifications of STM topographs (rows, dimers, atoms, etc). We find that dimer-row-resolving tip apices extract hydrogen atoms most readily and reliably (and with least spurious desorption), while tip states which provide atomic resolution counter-intuitively have a lower probability for single H atom removal.

Local removal of silicon layers on Si(1 0 0)-2 × 1 with chlorine-resist STM lithography

“…STM lithography on hydrogen-passivated Si(100) is one of well-developed technologies used for the fabrication of nanodevices. This approach assumes the use of hydrogen monolayer as a resist, which can be easily removed with an STM tip [1][2][3][4] . Recently, the hydrogen depassivation lithography has evolved into a wellestablished technique applied with success to the creation of a single atom transistor 5 and elements of the quantum computer 6,7 .…”

Embedding human heuristics in machine-learning-enabled probe microscopy

“…Typically, an operator will want to coerce the tip into producing images with one of these atomistic resolutions visible. (It is also worth noting that the tip apex capable of the highest resolution may not be best suited to other tasks, including, in particular, single atom manipulation [24].) Uncontrolled, and sometimes controlled, tip changes, however, mean that it is possible to produce images of H:Si(100) showing a combination of any of these four states, tip change shears, and other defects.…”

“…Besides its distinctive surface features, H:Si(100) is an ideal test-bed for developing CNN automation techniques. In addition to the relative simplicity of its reconstruction and a wealth of previous literature [25], H:Si(100) is a well understood substrate that has been used in many important advances in single atom technology and atomically precise materials engineering [24,[26][27][28][29][30][31]. Furthermore, because it has been previously studied in the context of machine-learning-enabled SPM [10][11][12], a good comparison can be formed with existing machine learning approaches based on full scans.…”

Corrections

“…Moriarty and others have already taken a step in this direction by developing an automated process for using tips to move hydrogen atoms 5 . Despite slow progress, Moriarty finds cause for optimism in astronomy, a field in which machine learning is helping researchers to judge when data might be meaningful.…”