Related papers: GFET Lab: A Graphene Field-Effect Transistor TCAD …
The analytical model of the small-signal current and capacitance characteristics of RF graphene FET is presented. The model is based on explicit distributions of chemical potential in graphene channels (including ambipolar conductivity at…
Several types of detectors are used to detect charged particles in particle and nuclear physics experiments. Since the semiconductor detector has superior spatial and kinematic resolutions as well as good response time than other types of…
FeFETs hold strong potential for advancing memory and logic technologies, but their inherent randomness arising from both operational cycling and fabrication variability poses significant challenges for accurate and reliable modeling.…
Electrostatically tunable Josephson field-effect transistors (JoFETs) are one of the most desired building blocks of quantum electronics. JoFET applications range from parametric amplifiers and superconducting qubits to a variety of…
The growing penetration of inverter-based resources and associated controls necessitates system-wide electromagnetic transient (EMT) analyses. EMT tools and methods today were not designed for the scale of these analyses. In light of the…
In this article, we present a configurable field-effect transistor (FET), where not only polarity (n- and p-type), but the conduction mechanism of a FET can also be configured dynamically. As a result, we can have both types of devices,…
Graphene nanoribbons (GNRs) have been proposed as potential building blocks for field effect transistor (FET) devices due to their quantum confinement bandgap. Here, we propose a novel GNR device concept, enabling the control of both charge…
We present an analytical device model for a graphene bilayer field-effect transistor (GBL-FET) with a graphene bilayer as a channel, and with back and top gates. The model accounts for the dependences of the electron and hole Fermi energies…
Thin-film transistors (TFTs) represent a wide-spread tool to determine the charge-carrier mobility of materials. Mobilities and further transistor parameters like contact resistances are commonly extracted from the electrical…
Bilayer graphene has the very interesting property of an energy gap tunable with the vertical electric field. We propose an analytical model for a bilayer-graphene field-effect transistor, suitable for exploring the design parameter space…
We have investigated the non-quasi-static (NQS) effects in graphene field-effect transistors (GFETs), which are relevant for GFET operation at high frequencies as a result of significant carrier inertia. A small-signal NQS model is derived…
Machine learning algorithms are being used more frequently in the first-level triggers in collider experiments, with Graph Neural Networks pushing the hardware requirements of FPGA-based triggers beyond the current state of the art. To meet…
The ion-sensitive field-effect transistor (ISFET) is an emerging technology that has received much attention in numerous research areas, including biochemistry, medicine, and security applications. However, compared to other types of…
A spin field effect transistor (FET) is proposed by utilizing a graphene nanoribbon as the channel. Similar to the conventional spin FETs, the device involves ferromagnetic metals as a source and drain; they, in turn, are connected to the…
Computer models (simulators) are vital tools for investigating physical processes. Despite their utility, the prohibitive run-time of simulators hinders their direct application for uncertainty quantification. Gaussian process emulators…
We directly image hot spot formation in functioning mono- and bilayer graphene field effect transistors (GFETs) using infrared thermal microscopy. Correlating with an electrical-thermal transport model provides insight into carrier…
Graphene-based devices have shown great promise for several applications. For graphene devices to be used in real-world systems, it is necessary to demonstrate competitive device performance, repeatability of results, reliability, and a…
Recently, machine learning approaches on Graph data have become very popular. It was observed that significant results were obtained by including implicit or explicit logical connections between data samples that make up the data to the…
This work presents a novel general compact model for 7nm technology node devices like FinFETs. As an extension of previous conventional compact model that based on some less accurate elements including one-dimensional Poisson equation for…
In this work, a modified, lumped element graphene field effect device model is presented. The model is based on the "Top-of-the-barrier" approach which is usually valid only for ballistic graphene nanotransistors. Proper modifications are…