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We experimentally demonstrate DC functionality of graphene-based hot electron transistors, which we call Graphene Base Transistors (GBT). The fabrication scheme is potentially compatible with silicon technology and can be carried out at the…
A graphene field-effect-transistor (GFET) model calibrated with extracted device parameters and a commercial 65 nm silicon MOSFET model are compared with respect to their radio frequency behavior. GFETs slightly lag behind CMOS in terms of…
The main target of this article is to propose for the first time a physics-based continuous and symmetric compact model that accurately captures IV experimental dependencies induced by geometrical scaling effects for graphene transistor…
Reliable fault detection is an essential requirement for safe and efficient operation of complex mechanical systems in various industrial applications. Despite the abundance of existing approaches and the maturity of the fault detection…
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 propose a new regression-based algorithm to compute Graph Fourier Transform (GFT). Our algorithm allows different regularizations to be included when computing the GFT analysis components, so that the resulting components…
Straightforward contact resistance extraction methods based on electrical device characteristics are described and applied here to graphene field-effect transistors from different technologies. The methods are an educated adaptation of…
Graph Transformers (GTs) have demonstrated great effectiveness across various graph analytical tasks. However, the existing GTs focus on training and testing graph data originated from the same distribution, but fail to generalize under…
While tokenized graph Transformers have demonstrated strong performance in node classification tasks, their reliance on a limited subset of nodes with high similarity scores for constructing token sequences overlooks valuable information…
Graphene field effect transistors (G-FETs) have appeared as suitable candidates for sensing charges and have thus attracted large interest for ion and chemical detections. In particular, their high sensitivity, chemical robustness,…
Hysteresis and memory effects in graphene field-effect transistors (GFETs) offer unique opportunities for neuromorphic computing, sensing, and memory applications, yet their physical origins remain debated due to competing volatile and…
The use of two truly two-dimensional gapless semiconductors, monolayer and bilayer graphene, as current-carrying components in field-effect transistors (FET) gives access to new types of nanoelectronic devices. Here, we report on the…
Graphene has attracted enormous interests due to its unique physical, mechanical, and electrical properties. Specially, graphene-based field-effect transistors (FETs) have evolved rapidly and are now considered as an option for conventional…
Quantum random number generators are a burgeoning technology used for a variety of applications, including modern security and encryption systems. Typical methods exploit an entropy source combined with an extraction or bit generation…
Hydrodynamic electrons in high-mobility graphene devices have demonstrated great potential in establishing an electronic analogue of relativistic quantum fluid in solid-state systems. One of the key requirements for observing viscous…
Graphene has emerged as a novel platform for opto-electronic applications and photodetector, but the inefficient conversion from light to current has so far been an important roadblock. The main challenge has been to increase the light…
Graph signal processing has become an essential tool for analyzing data structured on irregular domains. While conventional graph shift operators (GSOs) are effective for certain tasks, they inherently lack flexibility in modeling…
The field effect transistors (FETs) exhibited ultrahigh responsivity (107 A/W) to infrared light with great improvement of mobility in graphene / PbS quantum dot (QD) hybrid. These reported transistors are either unipolar or depletion mode…
Graph neural networks (GNNs) are often trained on individual datasets, requiring specialized models and significant hyperparameter tuning due to the unique structures and features of each dataset. This approach limits the scalability and…
Transistors operating at high frequencies are the basic building blocks of millimeter-wave communication and sensor systems. The high velocity and mobility of carriers in graphene can open way for ultra-fast group IV transistors with…