Related papers: Ferroelectrically-enhanced Schottky barrier transi…
In this work, we demonstrate a dual-gated (DG) MoS2 field effect transistors (FETs) in which the degraded switching performance of multilayer MoS2 can be compensated by the DG structure. It produces large current density (>100 {\mu}A/{\mu}m…
Artificial neural networks (ANNs), particularly large language models (LLMs), demonstrate powerful inference capabilities but consume substantial energy. Conversely, spiking neural networks (SNNs) exhibit exceptional energy efficiency due…
Rapid progress of artificial neural network applications in recent years has led to the issue of an unprecedented energy consumption. It can be solved by the implementation of energy efficient hardware based on non-von-Neumann…
The scaling behaviors of graphene nanoribbon (GNR) Schottky barrier field-effect transistors (SBFETs) are studied by solving the non-equilibrium Green's function (NEGF) transport equation in an atomistic basis set self-consistently with a…
We present ab-initio local density FLAPW calculations on non-reactive N-terminated [001] ordered GaN/Ag and GaN/Au interfaces and compare the results (such as metal induced gap states and Schottky barrier heights) with those obtained for…
In this work, we describe the charge transport in two-dimensional (2D) Schottky barrier field-effect transistors (SB-FETs) based on the carrier injection at the Schottky contacts. We first develop a numerical model for thermionic and…
Multilayered artificial neural networks (ANN) have found widespread utility in classification and recognition applications. The scale and complexity of such networks together with the inadequacies of general purpose computing platforms have…
The great potential of memristive devices for real-world applications still relies on overcoming key technical challenges, including the need for a larger number of stable resistance states, faster switching speeds, lower SET/RESET…
Although semiconductor to metal phase transformation of MoTe$_{2}$ by high-density laser irradiation of more than 0.3 MW/cm$^{2}$ has been reported, we reveal that the laser-induced-metal (LIM) phase is not the 1T' structure derived by a…
This work presents a novel approach to configure 2T-nC ferroelectric RAM (FeRAM) for performing single cell logic-in-memory operations, highlighting its advantages in energy-efficient computation over conventional DRAM-based approaches.…
The ability to learn continuously in artificial neural networks (ANNs) is often limited by catastrophic forgetting, a phenomenon in which new knowledge becomes dominant. By taking mechanisms of memory encoding in neuroscience (aka. engrams)…
This work presents the design of beta-Ga2O3 schottky barrier diode using high-k dielectric superjunction to significantly enhance the breakdown voltage vs on-resistance trade-off beyond its already high unipolar figure of merit. The device…
We present an atomistic 3D simulation study of the performance of graphene nanoribbon (GNR) Schottky barrier (SB) FETs and transistors with doped reservoirs (MOSFETs) by means of the self-consistent solution of the Poisson and Schrodinger…
We study the possibility of spin injection from Fe into Si(001), using the Schottky barrier at the Fe/Si contact as tunneling barrier. Our calculations are based on density-functional theory for the description of the electronic structure…
Stochastic magnetic tunnel junctions (sMTJ) using low-barrier nanomagnets have shown promise as fast, energy-efficient, and scalable building blocks for probabilistic computing. Despite recent experimental and theoretical progress, sMTJs…
Large spiking neural networks (SNNs) require ultra-low power and low variability hardware for neuromorphic computing applications. Recently, a band-to-band tunneling-based (BTBT) integrator, enabling sub-kHz operation of neurons with area…
Schottky barriers are often formed at the semiconductor/metal contacts and affect the electrical behaviour of semiconductor devices. In particular, Schottky barriers have been playing a major role in the investigation of the electrical…
Recent breakthroughs in recurrent deep neural networks with long short-term memory (LSTM) units has led to major advances in artificial intelligence. State-of-the-art LSTM models with significantly increased complexity and a large number of…
Computing inspired by the human brain requires a massive parallel architecture of low-power consuming elements of which the internal state can be changed. SrTiO3 is a complex oxide that offers rich electronic properties; here Schottky…
Transformers, while revolutionary, face challenges due to their demanding computational cost and large data movement. To address this, we propose HyFlexPIM, a novel mixed-signal processing-in-memory (PIM) accelerator for inference that…