Stochastic Syncing in Sinusoidally Driven Atomic Orbital Memory.

Stochastically fluctuating multiwell systems are a promising route toward physical implementations of energy-based machine learning and neuromorphic hardware. One of the challenges is finding tunable material platforms that exhibit such multiwell behavior and understanding how complex dynamic input signals influence their stochastic response. One such platform is the recently discovered atomic Boltzmann machine, where each stochastic unit is represented by a binary orbital memory state of an individual atom. Here, we investigate the stochastic response of binary orbital memory states to sinusoidal input voltages. Using scanning tunneling microscopy, we investigated orbital memory derived from individual Fe and Co atoms on black phosphorus. We quantify the state residence times as a function of various input parameters such as frequency, amplitude, and offset voltage. The state residence times for both species, when driven by a sinusoidal signal, exhibit synchronization that can be quantitatively modeled by a Poisson process based on the switching rates in the absence of a sinusoidal signal. For individual Fe atoms, we also observe a frequency-dependent response of the state favorability, which can be tuned by the input parameters. In contrast to Fe, there is no significant frequency dependence in the state favorability for individual Co atoms. Based on the Poisson model, the difference in the response of the state favorability can be traced to the difference in the voltage-dependent switching rates of the two different species. This platform provides a tunable way to induce population changes in stochastic systems and provides a foundation toward understanding driven stochastic multiwell systems.

Magneto-Seebeck tunneling on the atomic scale.

The tunneling of spin-polarized electrons across a magnetic tunnel junction driven by a temperature gradient is a fundamental process for the thermal control of electron spin transport. We experimentally investigated the atomic-scale details of this magneto-Seebeck tunneling by placing a magnetic probe tip in close proximity to a magnetic sample at cryogenic temperature, with a vacuum as the tunneling barrier. Heating the tip and measuring the thermopower of the junction while scanning across the spin texture of the sample lead to spin-resolved Seebeck coefficients that can be mapped at atomic-scale lateral resolution. We propose a spin detector for spintronics applications that is driven solely by waste heat, using magneto-Seebeck tunneling to convert spin information into a voltage that can be used for further data processing.

The surface-to-volume ratio: a key parameter in the thermoelectric transport of topological insulator Bi2Se3 nanowires.

We systematically investigated the role of topological surface states on thermoelectric transport by varying the surface-to-volume ratio (s/v) of Bi2Se3 nanowires. The thermoelectric coefficients of Bi2Se3 nanowires were significantly influenced by the topological surface states with increasing the s/v. The Seebeck coefficient of Bi2Se3 nanowires decreased with increasing the s/v, while the electrical conductivity increased with increasing the s/v. This implies that the influence of metallic surface states become dominant in thermoelectric transport in thin nanowires, and the s/v is a key parameter which determines the total thermoelectric properties. Our measurements were corroborated by using a two-channel Boltzmann transport model.