Related papers: Numerical approach for retention characteristics o…
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…
We present a new fully self-aligned single-electron memory with a single pair of nano floating gates, made of different materials (Si and Ge). The energy barrier that prevents stored charge leakage is induced not only by quantum effects but…
Current portable memory device relies heavily on flash memory technology for its implementation. New generation of non-volatile memory is likely to replace floating gates, charge-trapping memory currently still suffering from inadequate…
Systematically studying the crystal, magnetic, and electronic structures of PuGa3 with density functional theory (DFT) reveals the entanglement of the three types of structure. Magnetic structure affects the energy more strongly than…
In this paper, we propose a dual memory structure for reinforcement learning algorithms with replay memory. The dual memory consists of a main memory that stores various data and a cache memory that manages the data and trains the…
In the realm of quantum chemistry, the accurate prediction of electronic structure and properties of nanostructures remains a formidable challenge. Density Functional Theory (DFT) and Density Matrix Renormalization Group (DMRG) have emerged…
Ferroelectric field-effect transistors (Fe-FETs) with ferroelectric hafnium oxide (FE HfO2) as gate insulator are being extensively explored as a promising device candidate for three-dimensional (3D) NAND memory application. FE HfO2…
The fundamental property of most single-electron devices with quasicontinuous quasiparticle spectrum on the island is the periodicity of their transport characteristics in the gate voltage. This property is robust even with respect to…
A new device structure for spin transfer torque based magnetic random access memory is proposed for on-chip memory applications. Our device structure exploits spin Hall effect to create a differential memory cell that exhibits fast and…
We evaluate the effect of the recombination associated with interlayer transitions in ungated and gated double-graphene-layer (GL) structures on the injection of electrons and holes. Using the proposed model, we derive analytical…
The main problem about replacing LTP as a memory mechanism has been to find other highly abstract, easily understandable principles for induced plasticity. In this paper we attempt to lay out such a basic mechanism, namely intrinsic…
We propose a direct experimental extraction technique for trapped charges and quantitative energy band diagrams in the FeFETs with metal-insulator-ferroelectric-insulator-semiconductor (MIFIS) structure, derived from the physical…
The memory window of floating gate (FG) type non-volatile memory (NVM) devices is a fundamental figure of merit used not only to evaluate the performance, such as retention and endurance, but also to discuss the feasibility of advanced…
Modal relaxation dynamics has been observed experimentally to clarify statistical-physical properties of soft-mode turbulence, the spatiotemporal chaos observed in homeotropically aligned nematic liquid crystals. We found a dual structure,…
Resonant tunneling between closely spaced two dimensional electron gases is a single particle phenomenon that has sparked interest for decades. High tunneling conductances at equal electron densities are observed whenever the Fermi levels…
Charge-trap memory with high-\k dielectric materials is considered to be a promising candidate for next-generation memory devices. Ultrathin layered two-dimensional (2D) materials like graphene and MoS2 have been receiving much attention…
In systems with local gauge symmetries, the memory effect corresponds to traces inscribed on a suitable probe when a pure gauge configuration at infinite past dynamically evolves to another pure gauge configuration at infinite future. In…
Recurrent neural networks (RNNs) are powerful dynamical models, widely used in machine learning (ML) and neuroscience. Prior theoretical work has focused on RNNs with additive interactions. However, gating - i.e. multiplicative -…
We present a theory for the memory effect in electron glasses. In fast gate voltage sweeps it is manifested as a dip in the conductivity around the equilibration gate voltage. We show that this feature, also known as anomalous field effect,…
Despite advances in deep learning, neural networks can only learn multiple tasks when trained on them jointly. When tasks arrive sequentially, they lose performance on previously learnt tasks. This phenomenon called catastrophic forgetting…