相关论文: Gate-controlled neuromorphic functional transition…

Magnetic domain wall motion has recently garnered significant interest as a physical mechanism to enable energy-efficient, next-generation brain-inspired computing architectures. However, realizing all behaviors required for neuromorphic…

I review the advancements of atomic scale nanoelectronics towards quantum neuromorphics. First, I summarize the key properties of elementary combinations of few neurons, namely long-- and short--term plasticity, spike-timing dependent…

Neuromorphic architectures are ideally suited for the implementation of smart sensors able to react, learn, and respond to a changing environment. Our work uses the insect brain as a model to understand how heterogeneous architectures,…

Here we theoretically show, by designing and manipulating spatially inhomogeneous, non-uniform conductivity patterns across a single flake of graphene, that this single-atom-layered material can serve as a "one-atom-thick" platform for…

Neuro-inspired computing architectures are one of the leading candidates to solve complex, large-scale associative learning problems. The two key building blocks for neuromorphic computing are the synapse and the neuron, which form the…

Truly polymorphic circuits, whose functionality/circuit behavior can be altered using a control variable, can provide tremendous benefits in multi-functional system design and resource sharing. For secure and fault tolerant hardware designs…

We explore the chiral transmission of electrons across graphene heterojunctions for electronic switching using gate geometry alone. A sequence of gates is used to collimate and orthogonalize the chiral transmission lobes across multiple…

Using computational and theoretical approaches, we investigate the snap-through transition of buckled graphene membranes. Our main interest is related to the possibility of using the buckled membrane as a plate of capacitor with memory…

Atomically-thin 2D semiconducting materials integrated into van der Waals heterostructures have enabled architectures that hold great promise for next generation nanoelectronics. However, challenges still remain to enable their full…

Inspired by the dendritic integration and spiking operation of a biological neuron, flexible oxide-based neuron transistors gated by solid-state electrolyte films are fabricated on flexible plastic substrates for biochemical sensing…

Neuromorphic hardware facilitates rapid and energy-efficient training and operation of neural network models for artificial intelligence. However, existing analog in-memory computing devices, like memristors, continue to face significant…

We present a hybrid device platform for creating an electrically reconfigurable metasurface formed by the integration of plasmonic nanostructures with phase-change material germanium antimony telluride (GST). By changing the phase of GST…

Novel materials are in great demand for future applications. The discovery of graphene, a one atom thick carbon layer, holds the promise for unique device architectures and functionalities exploiting unprecedented physical phenomena. The…

Neuromorphic computing is poised to further the success of software-based neural networks by utilizing improved customized hardware. However, the translation of neuromorphic algorithms to hardware specifications is a problem that has been…

We investigate spin transport through ferromagnetic graphene vertical heterostructures where a sandwiched tunneling layer is either a normal or ferroelectric insulator. We show that the spin-polarization of the tunneling current is…

Graphene is a sturdy and chemically inert material exhibiting an exposed two-dimensional electron gas of high mobility. These combined properties enable the design of graphene composites either based on covalent or non- covalent coupling of…

Neuromorphic photonics has recently emerged as a promising hardware accelerator, with significant potential speed and energy advantages over digital electronics, for machine learning algorithms such as neural networks of various types.…

Electrons in graphene are described by relativistic Dirac-Weyl spinors with a two-component pseudospin1-12. The unique pseudospin structure of Dirac electrons leads to emerging phenomena such as the massless Dirac cone2, anomalous quantum…

Using the non-equilibrium Green's functions formalism in a tight binding model, the spin-dependent transport in armchair graphene nanoribbon (GNR) structures controlled by a ferromagnetic gate is investigated. Beyond the oscillatory…

We propose and numerically simulate novel reconfigurable logic gates employing spin metal-oxide-semiconductor field-effect transistors (spin MOSFETs). The output characteristics of the spin MOSFETs depend on the relative magnetization…