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The reliability of analysis is becoming increasingly important as point-of-care diagnostics are transitioning from single analyte detection towards multiplexed multianalyte detection. Multianalyte detection benefits greatly from…
We present an atomistic three-dimensional simulation of graphene nanoribbon field effect transistors (GNR-FETs), based on the self-consistent solution of the 3D Poisson and Schroedinger equation with open boundary conditions within the…
This paper aims to provide a novel design of a multiscale framelet convolution for spectral graph neural networks (GNNs). While current spectral methods excel in various graph learning tasks, they often lack the flexibility to adapt to…
Ternary logic system is the most promising and pursued alternate to the prevailing binary logic systems due to the energy efficiency of circuits following reduced circuit complexity and chip area. In this paper, we have proposed a ternary…
Bottom-up assembled nanomaterials and nanostructures allow for the studies of rich and unprecedented quantum-related and mesoscopic transport phenomena. However, it can be difficult to quantify the correlations between the geometrical or…
Neural fields have emerged as a powerful framework for representing continuous multidimensional signals such as images and videos, 3D and 4D objects and scenes, and radiance fields. While efficient, achieving high-quality representation…
In this letter, we demonstrate the first BN/Graphene/BN field effect transistor for RF applications. The BN/Graphene/BN structure can preserve the high mobility of graphene, even when it is sandwiched between a substrate and a gate…
Graphene nanoribbons (GNRs) are atomically precise stripes of graphene with tunable electronic properties, making them promising for room-temperature switching applications like field-effect transistors (FETs). However, challenges persist…
We introduce a new method for hardware non-uniform random number generation based on the transfer characteristics of graphene field-effect transistors (GFETs) which requires as few as two transistors and a resistor (or transimpedance…
A fresh look on carbon-based transistor channel materials like single-walled carbon nanotubes (CNT) and graphene nanoribbons (GNR) in future electronic applications is given. Although theoretical predictions initially promised that GNR…
Advanced electronic device technologies require a faster operation and smaller average power consumption, which are the most important parameters in very large scale integrated circuit design. The conventional Complementary Metal-Oxide…
In modern communication system, modulation is a key function that embeds the baseband signal (information) into a carrier wave so that it can be successfully broadcasted through a medium such as air or cables. A flexible signal modulation…
Graph Neural Networks (GNNs) are widely used today in recommendation systems, fraud detection, and node/link classification tasks. Real world GNNs continue to scale in size and require a large memory footprint for storing graphs and…
We simulate quantum transport between a graphene nanoribbon (GNR) and a single-walled carbon nanotube (CNT) where electrons traverse vacuum gap between them. The GNR covers CNT over a nanoscale region while their relative rotation is 90…
Designing nanoscale electronic devices such as the currently manufactured nanoribbon field-effect transistors (NRFETs) requires advanced modeling tools capturing all relevant quantum mechanical effects. State-of-the-art approaches combine…
Mapping neuro-inspired algorithms to sensor backplanes of on-chip hardware require shifting the signal processing from digital to the analog domain, demanding memory technologies beyond conventional CMOS binary storage units. Using…
Graph representation learning has attracted a surge of interest recently, whose target at learning discriminant embedding for each node in the graph. Most of these representation methods focus on supervised learning and heavily depend on…
In this paper, we propose a machine learning (ML) based physical layer receiver solution for demodulating OFDM signals that are subject to a high level of nonlinear distortion. Specifically, a novel deep learning based convolutional neural…
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…
Complementary metal oxide semiconductor technology (CMOS) has been faced critical challenges in nano-scale regime. CNTFET (Carbon Nanotube Field effect transistor) technology is a promising alternative for CMOS technology. In this paper, we…