Related papers: Single-electron tunneling PbS/InP neuromorphic com…
The single electron transistor (SET) offers unparalled opportunities as a nano-scale electrometer, capable of measuring sub-electron charge variations. SETs have been proposed for read-out schema in solid-state quantum computing where…
Spin dependent single electron tunneling in a ferromagnetic double junction is investigated theoretically in the limit of incoherent sequential tunneling. The junction consists of a small nonmagnetic metallic grain with discrete energy…
We report energy selective tunneling readout-based Hamiltonian parameter estimation of a two-electron spin qubit in a GaAs quantum dot array. Optimization of readout fidelity enables a single-shot measurement time of 16 on average, with…
In this Letter, we present a theoretical analysis to single-electron pumping operation in a large range of driving frequencies through the time-dependent tunneling barriers controlled by external gate voltages. We show that the…
Electron tunneling in ferromagnetic single-electron transistors is considered theoretically in the sequential tunneling regime. A new formalism is developed, which operates in a two-dimensional space of states, instead of one-dimensiona…
Background charge rearrangements in metallic single-electron transistors are modelled in two-level tunnelling systems as a Poisson process with a scale parameter as only variable. The model explains the recent observation of asymmetric…
We study phonon-assisted electron tunneling in semiconductor quantum dot molecules. In particular, singlet-singlet relaxation in a two-electron doped structure is considered. The influence of Coulomb interaction is discussed via comparison…
Neuromorphic hardware as a non-Von Neumann architecture has better energy efficiency and parallelism than the conventional computer. Here, with numerical modeling spin-orbit torque (SOT) device using current-induced SOT and Joule heating…
Analog electronic non-volatile memories mimicking synaptic operations are being explored for the implementation of neuromorphic computing systems. Compound synapses consisting of ensembles of stochastic binary elements are alternatives to…
We report scanning tunneling microscopy studies of individual adatoms deposited on an InSb(110) surface. The adatoms can be reproducibly dropped off from the STM tip by voltage pulses, and impact tunneling into the surface by up to ~100x.…
We simulate the dynamics of a single-electron source, modeled as a quantum dot with on-site Coulomb interaction and tunnel coupling to an adjacent lead, in time-dependent density functional theory. Based on this system, we develop a…
Spiking Neural Networks (SNNs) have sparse, event driven processing that can leverage neuromorphic applications. In this work, we introduce a multi-threading kernel that enables neuromorphic applications running at the edge, meaning they…
Machine learning applications that are implemented with spike-based computation model, e.g., Spiking Neural Network (SNN), have a great potential to lower the energy consumption when they are executed on a neuromorphic hardware. However,…
The continuous effort in making artificial neural networks more alike to human brain calls for the hardware elements to implement biological synapse-like functionalities. The recent experimental demonstration of ferroelectric-like FETs…
Starting from the Kubo formula for conductance, we calculate the frequency-dependent response of a single-electron transistor (SET) driven by an ac signal. Treating tunneling processes within the lowest order approximation, valid for a wide…
Hardware implementation of neuromorphic computing can significantly improve performance and energy efficiency of machine learning tasks implemented with spiking neural networks (SNNs), making these hardware platforms particularly suitable…
We investigate the tunnel rates and energies of excited states of small numbers of electrons in a quantum dot fabricated in a Si/SiGe heterostructure. Tunnel rates for loading and unloading electrons are found to be strongly energy…
Spiking Neural Networks (SNNs) are efficient computation models to perform spatio-temporal pattern recognition on {resource}- and {power}-constrained platforms. SNNs executed on neuromorphic hardware can further reduce energy consumption of…
The effect of electron-phonon coupling on the current noise in a molecular junction is investigated within a simple model. The model comprises a 1-level bridge representing a molecular level that connects between two free electron…
Ferroelectric tunnel junctions (FTJs) leverage polarization-dependent tunneling through ultrathin barriers to enable two-terminal, non-volatile memory and logic. Although conceptually appealing, the practical implementation of conventional…