Related papers: GFET Lab: A Graphene Field-Effect Transistor TCAD …
Graph foundation models face several fundamental challenges including transferability across datasets and data scarcity, which calls into question the very feasibility of graph foundation models. However, despite similar challenges, the…
A grid-feeding converter system is added to a novel power system transient simulation scheme based on frequency response optimized integrators considering second order derivative. The converter system and its implementation in the…
We propose Graphene Klein tunnel transistors (GKTFET) as a way to enforce current saturation while maintaining large mobility for high speed radio frequency (RF) applications. The GKTFET consists of a sequence of angled graphene p-n…
In this work, we propose the Bilayer Graphene Tunnel Field Effect Transistor (BG-TFET) as a device suitable for fabrication and circuit integration with present-day technology. It provides high Ion/Ioff ratio at ultra-low supply voltage,…
We report a method of fabricating self-aligned, top-gated graphene field-effect transistors (GFETs) employing polyethyleneimine spin-on-doped source/drain access regions, resulting in a 2X reduction of access resistance and a 2.5X…
Steep transistors are crucial in lowering power consumption of the integrated circuits. However, the difficulties in achieving steepness beyond the Boltzmann limit experimentally have hindered the fundamental challenges in application of…
For the growth of emerging graphene field-effect transistor (GFET) technologies, a thorough characterization of on-wafer variability is required. Here, we report for the first time a physics-based compact model, which precisely describes…
GENFIT is an experiment-independent track-fitting toolkit that combines fitting algorithms, track representations, and measurement geometries into a modular framework. We report on a significantly improved version of GENFIT, based on…
In recent part extensive simulation work has already been done on TFETs. However this is limited to device performance analysis. Evaluation of circuit performance is a topic that is very little touched. This is due to the non availability…
A fitting model is developed for accounting the asymmetric ambipolarities in the I-V characteristics of graphene field-effect transistors (G-FETs) with doped channels, originating from the thermionic emission and interband tunneling at the…
In this work, we investigate the transport phenomena in compound semiconductor material based buried channel Quantum Well MOSFET with a view to developing a simple and effective model for the device current. Device simulation has been…
We present a TCAD-based simulation framework established for quantum dot spin qubits in a silicon FinFET platform with all-electrical control of the spin state. The framework works down to 1K and consists of a two-step simulation chain,…
We report the batch fabrication of graphene field-effect-transistors (GFETs) with nanoperforated graphene as channel. The transistors were cut and encapsulated. The encapsulated GFETs display saturation regions that can be tuned by…
Graphene has been proposed for use in various nanodevice designs, many of which harness emergent quantum properties for device functionality. However, visualization, measurement, and manipulation become non-trivial at nanometer and atomic…
Monte Carlo (MC) simulations are powerful computational tools for investigating thermodynamic behavior and validating analytical approaches in complex physical systems. Here we present ETHER (Efficient Tool for THermodynamics Exploration…
An analytical circuit model able to predict the input impedance of reconfigurable graphene plasmonic dipoles is presented. A suitable definition of plasmonic characteristic impedance, employing natural currents, is used to for consistent…
In order to support the design and study of sophisticated large scale transition edge sensor (TES) circuits, we use basic SPICE elements to develop device models for TESs based on the superfluid-normal fluid theory. In contrast to previous…
Two-dimensional (2D) materials are particularly attractive to build the channel of next-generation field-effect transistors (FETs) with gate lengths below 10-15 nm. Because the 2D technology has not yet reached the same level of maturity as…
Implicit models, which allow for the generation of samples but not for point-wise evaluation of probabilities, are omnipresent in real-world problems tackled by machine learning and a hot topic of current research. Some examples include…
Transformers have revolutionized machine learning, yet their inner workings remain opaque to many. We present Transformer Explainer, an interactive visualization tool designed for non-experts to learn about Transformers through the GPT-2…