Related papers: Ferroelectrically-enhanced Schottky barrier transi…
Ferroelectric memristors have attracted much attention as a type of nonvolatile resistance switching memories in neuromorphic computing, image recognition, and information storage. Their resistance switching mechanisms have been studied…
Memristors are considered key building blocks for the development of neuromorphic computing hardware. For ferroelectric memristors with a capacitor-like structure, the polarization direction modulates the height of the Schottky barriers --…
As a promising alternative to the Von Neumann architecture, in-memory computing holds the promise of delivering high computing capacity while consuming low power. Content addressable memory (CAM) can implement pattern matching and distance…
Schottky barrier field-effect transistors (SBFETs) based on few and mono layer phosphorene are simulated by the non-equilibrium Green's function formalism. It is shown that scaling down the gate oxide thickness results in pronounced…
A new Schottky-gate Bipolar Mode Field Effect Transistor (SBMFET) is proposed and verified by two-dimensional simulation. Unlike in the case of conventional BMFET, which uses deep diffused p+-regions as the gate, the proposed device uses…
The potential barrier height at the interface formed by a metal contact and multiple one-dimensional (1D) quasi-ballistic channels in field-effect transistors (FETs) is evaluated across different carbon nanotube and nanowire device…
Using metal-ferroelectric junctions as switchable diodes was proposed several decades ago. This was shown to actually work in PbZr(1-x)TixO3 (PZT) by Blom et al. [P.W. M. Blom et al., Phys. Rev. Lett. 73, 2107 (1994)], who reported…
Two-dimensional ferroelectric materials are beneficial for power-efficient memory devices and transistor applications. Here, we predict out-of-plane ferroelectricity in a new family of buckled metal oxide (MO; M: Ge, Sn, Pb) monolayers with…
In-memory computing on a reconfigurable architecture is the emerging field which performs an application-based resource allocation for computational efficiency and energy optimization. In this work, we propose a Ferroelectric…
Understanding of the electrical contact properties of semiconductor nanowire (NW) field effect transistors (FETs) plays a crucial role in employing semiconducting NWs as building blocks for future nanoelectronic devices and in the study of…
Neuromorphic computing architectures enable the dense co-location of memory and processing elements within a single circuit. This co-location removes the communication bottleneck of transferring data between separate memory and computing…
Bottom-up synthesized GNRs and GNR heterostructures have promising electronic properties for high performance field effect transistors (FETs) and ultra-low power devices such as tunnelling FETs. However, the short length and wide band gap…
We report the first observation of ferroelectric gating in AlScN barrier wide-bandgap nitride transistors. These FerroHEMT devices realized by direct epitaxial growth represent a new class of ferroelectric transistors in which the…
Achieving brain-like density and performance in neuromorphic computers necessitates scaling down the size of nanodevices emulating neuro-synaptic functionalities. However, scaling nanodevices results in reduction of programming resolution…
A low Schottky barrier height (SBH) at source/drain contact is essential for achieving high drive current in atomic layer MoS2 channel based field-effect transistors. Approaches such as choosing metals with appropriate work functions and…
In this paper, we present experimental results and simulation data of an electrostatically doped and therefore voltage-programmable, planar, CMOS-compatible field-effect transistor (FET) structure. This planar device is based on our…
Energy-efficient real-time synapses and neurons are essential to enable large-scale neuromorphic computing. In this paper, we propose and demonstrate the Schottky-Barrier MOSFET-based ultra-low power voltage-controlled current source to…
Graphene based transistors relying on a conventional structure cannot switch properly because of the absence of an energy gap in graphene. To overcome this limitation, a barristor device was proposed, whose operation is based on the…
Monolithic three-dimensional integration of memory and logic circuits could dramatically improve performance and energy efficiency of computing systems. Some conventional and emerging memories are suitable for vertical integration,…
Online training of deep neural networks (DNN) can be significantly accelerated by performing in-situ vector matrix multiplication in a crossbar array of analog memories. However, training accuracies often suffer due to device non-idealities…