It has long been anticipated that the ultimate in miniature circuitry will be crafted of single atoms. Despite many advances made in scanned probe microscopy studies of molecules and atoms on surfaces, challenges with patterning and limited thermal stability have remained.Here we make progress toward those challenges and demonstrate rudimentary circuit elements through the patterning of dangling bonds on a hydrogen-terminated silicon surface. Dangling bonds sequester electrons both spatially and energetically in the bulk band gap, circumventing short-circuiting by the substrate. We deploy paired dangling bonds occupied by one moveable electron to form a binary electronic building block.Inspired by earlier quantum dot-based approaches, binary information is encoded in the electron position allowing demonstration of a "binary wire" and an OR gate.The prospect of atom scale computing was initially indicated by "molecular cascades"where sequentially toppling molecules were arranged in precise configurations to achieve binary logic functions 1 . Many notable approaches toward molecular electronics 2-7 , atomic electronics 8,9 , and quantum-dot-based electronics 10-15 have also been explored.The quantum dot based approaches [16][17][18][19][20] are particularly attractive, as they provide a low power yet fast basis 21 to go beyond today's CMOS technology 22 . These approaches, however, require cryogenic temperatures to minimize the population of thermally-excited states and achieve the desired functionality. Variability among quantum dots and sensitivity to uncontrolled fields are

Atomic-scale characterization and manipulation with scanning probe microscopy rely upon the use of an atomically sharp probe. Here we present automated methods based on machine learning to automatically detect and recondition the quality of the probe of a scanning tunneling microscope. As a model system, we employ these techniques on the technologically relevant hydrogen-terminated silicon surface, training the network to recognize abnormalities in the appearance of surface dangling bonds. Of the machine learning methods tested, a convolutional neural network yielded the greatest accuracy, achieving a positive identification of degraded tips in 97% of the test cases. By using multiple points of comparison and majority voting, the accuracy of the method is improved beyond 99%.

At the atomic scale, there has always been a trade-off between the ease of fabrication of structures and their thermal stability. Complex structures that are created effortlessly often disorder above cryogenic conditions. Conversely, systems with high thermal stability do not generally permit the same degree of complex manipulations. Here, we report scanning tunneling microscope (STM) techniques to substantially improve automated hydrogen lithography (HL) on silicon, and to transform state-of-the-art hydrogen repassivation into an efficient, accessible error correction/editing tool relative to existing chemical and mechanical methods. These techniques are readily adapted to many STMs, together enabling fabrication of error-free, room-temperature stable structures of unprecedented size. We created two rewriteable atomic memories (1.1 petabits per in2), storing the alphabet letter-by-letter in 8 bits and a piece of music in 192 bits. With HL no longer faced with this trade-off, practical silicon-based atomic-scale devices are poised to make rapid advances towards their full potential.

We report the study of single dangling bonds (DBs) on a hydrogen-terminated silicon (100) surface using a low-temperature scanning tunneling microscope. By investigating samples prepared with different annealing temperatures, we establish the critical role of subsurface arsenic dopants on the DB electronic properties. We show that when the near-surface concentration of dopants is depleted as a result of 1250°C flash anneals, a single DB exhibits a sharp conduction step in its I(V) spectroscopy that is not due to a density of states effect but rather corresponds to a DB charge state transition. The voltage position of this transition is perfectly correlated with bias-dependent changes in the STM images of the DB at different charge states. Density functional theory calculations further highlight the role of subsurface dopants on DB properties by showing the influence of the DB-dopant distance on the DB state. We discuss possible theoretical models of electronic transport through the DB that could account for our experimental observations.