Related papers: GFET Lab: A Graphene Field-Effect Transistor TCAD …
Designing model architectures requires decisions such as selecting operators (e.g., attention, convolution) and configurations (e.g., depth, width). However, evaluating the impact of these decisions on model quality requires costly…
Recent progress in graph signal processing (GSP) has addressed a number of problems, including sampling and filtering. Proposed methods have focused on generic graphs and defined signals with certain characteristics, e.g., bandlimited…
Semiconductor device models are essential to understand the charge transport in thin film transistors (TFTs). Using these TFT models to draw inference involves estimating parameters used to fit to the experimental data. These experimental…
High-fidelity fracture mechanics simulations of multiple microcracks interaction via physics-based models quickly become computationally expensive as the number of microcracks increases. This work develops a Graph Neural Network (GNN) based…
We demonstrate tunable solid state and flexible graphene field effect devices (FEDs) fabricated using a poly(methylmethacrylate) (PMMA) and lithium fluoride (LiF) composite dielectric. Increasing the concentration of LiF in the composite…
We develop an analytical device model for graphene bilayer field-effect transistors (GBL-FETs) with the back and top gates. The model is based on the Boltzmann equation for the electron transport and the Poisson equation in the weak…
The influence of edge vacancies on the working ability of the planar graphene tunnel field-effect transistor (TFET) is studied at various concentrations and distributions (normal, uniform, periodic) of defects. All calculations are…
Gate-layouts of spin qubit devices are commonly adapted from previous successful devices. As qubit numbers and the device complexity increase, modelling new device layouts and optimizing for yield and performance becomes necessary.…
We propose a fiber-based environmental sensor that exploits the reflection phase shift tunability provided by the use of layered coatings composed of dielectric slabs spaced by conducting membranes. A transfer matrix study is done in a…
Commercially available Noisy Intermediate-Scale Quantum (NISQ) devices now make small hybrid quantum-classical experiments practical, but many tools hide configuration or demand ad-hoc scripting. We introduce the Quantum Experiment…
We developed means to produce wafer scale, high-quality graphene films as large as 3 inch wafer size on Ni and Cu films under ambient-pressure and transfer them onto arbitrary substrates through instantaneous etching of metal layers. We…
In this paper, we demonstrate by simulation the general usability of an electrostatically doped and electrically reconfigurable planar field-effect transistor (FET) structure. The device concept is partly based on our already published and…
As the interest to Graph Neural Networks (GNNs) is growing, the importance of benchmarking and performance characterization studies of GNNs is increasing. So far, we have seen many studies that investigate and present the performance and…
With the further scaling of silicon MOSFETs becoming increasingly harder, the search for an alternative material became crucial. The electron device community found many of the answers in two dimensional materials, especially graphene. With…
The Efficient Adaptive Transformer (EAT) framework unifies three adaptive efficiency techniques - progressive token pruning, sparse attention, and dynamic early exiting - into a single, reproducible architecture for input-adaptive…
This paper reviews the experimental and theoretical state of the art in ballistic hot electron transistors that utilize two-dimensional base contacts made from graphene, i.e. graphene base transistors (GBTs). Early performance predictions…
We implemented a nanoelectronic interface between graphene field effect transistors (FETs) and soluble proteins. This enables production of bioelectronic devices that combine functionalities of the biomolecular and inorganic components. The…
The Graph Convolutional Network (GCN) model and its variants are powerful graph embedding tools for facilitating classification and clustering on graphs. However, a major challenge is to reduce the complexity of layered GCNs and make them…
We propose a concept for a graphene tunnel field-effect transistor. The main idea is based on the use of two graphene electrodes with zigzag termination divided by a narrow gap under the influence of the common gate. Our analysis shows that…
It is promising to apply quantum-mechanically confined graphene systems in field-effect transistors. High stability, superior performance, and large-scale integration are the main challenges facing the practical application of graphene…