Related papers: Characterizing Germanium Junction Transistors
Efficient on-device neural network (NN) inference offers predictable latency, improved privacy and reliability, and lower operating costs for vendors than cloud-based inference. This has sparked recent development of microcontroller-scale…
The Turing mechanism describes the emergence of spatial patterns due to spontaneous symmetry breaking in reaction-diffusion processes and underlies many developmental processes. Identifying Turing mechanisms in biological systems defines a…
Gunn (or Gunn-Hilsum) Effect and its associated negative differential resistivity (NDR) emanates from transfer of electrons between two different energy bands in a semiconductor. If applying a voltage (electric field) transfers electrons…
Graphene nanoribbons (GNRs) are one-dimensional nanostructures predicted to display a rich variety of electronic behaviors. Depending on their structure, GNRs realize metallic and semiconducting electronic structures with band gaps that can…
We apply the full power of modern electronic band structure engineering and epitaxial heterostructures to design a transistor that can sense and control a single donor electron spin. Spin resonance transistors may form the technological…
Distribution of a two component electrolyte mixture between the model adsorbent and a bulk aqueous electrolyte solution was studied using the replica Ornstein-Zernike theory and the grand canonical Monte Carlo method. The electrolyte…
Single-electron transistors would represent an approach for less power consuming microelectronic devices if room-temperature operation and industry-compatible fabrication were possible. We present a concept based on stripes of small,…
We report the fabrication and characterization of an electrostatic quantum dot in pure Germanium with an integrated charge measurement transistor. The device uses the Al2O3/Germanium interface for the confinement of carriers in the…
Graph Transformers (GTs) have recently emerged as popular alternatives to traditional message-passing Graph Neural Networks (GNNs), due to their theoretically superior expressiveness and impressive performance reported on standard node…
Unipolar devices constructed from ferromagnetic semiconducting materials with variable magnetization direction are shown theoretically to behave very similarly to nonmagnetic bipolar devices such as the p-n diode and the bipolar (junction)…
Elementary particle detectors fall broadly into only two classes: phase-transformation devices, such as the bubble chamber, and charge-transfer devices like the Geiger-Mueller tube. Quantum measurements are seen to involve transitions from…
The ability to control the spin textures in semiconductors is a fundamental step toward novel spintronic devices, while seeking desirable materials exhibiting persistent spin texture (PST) remains a key challenge. The PST is the property of…
Most detector systems used for positron emission particle tracking (PEPT) are very expensive due to the use of inorganic plastic scintillators combined with a high number of readout electronic channels. This work aims to reduce the overall…
We review recently developed two-step approach for description of electronic and magnetic properties of a new class of materials, the diluted magnetic semiconductors. In the first step we construct, on the basis of the state-of-the-art…
Graph Neural Networks (GNNs) have been widely applied to various fields for learning over graph-structured data. They have shown significant improvements over traditional heuristic methods in various tasks such as node classification and…
We report a bipolar field effect tunneling transistor that exploits to advantage the low density of states in graphene and its one atomic layer thickness. Our proof-of-concept devices are graphene heterostructures with atomically thin boron…
The structural, electronic and dynamical properties of a group of 2D germanium-based compounds, including GeC, GeN, GeO, GeSi, GeS, GeSe, and germanene, are investigated by employing first-principles calculations. The most stable structure…
Designing molecular organic semiconductors with distinct frontier orbitals is key for the development of devices with desirable properties. Generating defined organic nanostructures with atomic precision can be accomplished by on-surface…
Group equivariant convolutional networks (GCNNs) endow classical convolutional networks with additional symmetry priors, which can lead to a considerably improved performance. Recent advances in the theoretical description of GCNNs revealed…
Transition-metal-based Heusler semiconductors are promising materials for a variety of applications ranging from spintronics to thermoelectricity. Employing the $GW$ approximation within the framework of the FLAPW method, we study the…