Related papers: Exploiting Dual-Gate Ambipolar CNFETs for Scalable…

We approach the problem of improving robustness of deep learning algorithms in the presence of label noise. Building upon existing label correction and co-teaching methods, we propose a novel training procedure to mitigate the memorization…

We propose and analyze a novel dual-gate Spin Field Effect Transistor (SpinFET) with half-metallic ferromagnetic source and drain contacts. The transistor has two gate pads that can be biased independently. It can be switched ON or OFF with…

A simple scalable scheme is reported for fabricating suspended carbon nanotube field effect transistors (CNT-FETs) without exposing pristine as-grown carbon nanotubes to subsequent chemical processing. Versatility and ease of the technique…

Molecular level components, like carbon multiwalled nanotubes (MWNT), show great potential for future nanoelectronics. At low frequencies, only the outermost carbon layer determines the transport properties of the MWNT. Due to the…

Mixed-precision quantization has received increasing attention for its capability of reducing the computational burden and speeding up the inference time. Existing methods usually focus on the sensitivity of different network layers, which…

Artificial Neural Networks (ANNs) are prevalent machine learning models that are applied across various real-world classification tasks. However, training ANNs is time-consuming and the resulting models take a lot of memory to deploy. In…

Electroencephalogram-based motor imagery (MI) classification is an important paradigm of non-invasive brain-computer interfaces. Common spatial pattern (CSP), which exploits different energy distributions on the scalp while performing…

Distantly supervised named entity recognition (DS-NER) has been proposed to exploit the automatically labeled training data by external knowledge bases instead of human annotations. However, it tends to suffer from a high false negative…

We probed the charge transfer interaction between the amine-containing molecules: hydrazine, polyaniline and aminobutyl phosphonic acid, and carbon nanotube field effect transistors (CNTFETs). We successfully converted p-type CNTFETs to…

Even if Application-Specific Integrated Circuits (ASIC) have proven to be a relevant choice for integrating inference at the edge, they are often limited in terms of applicability. In this paper, we demonstrate that an ASIC neural network…

We report a method for probing electromechanical properties of multiwalled carbon nanotubes(CNTs). This method is based on AFM measurements on a doubly clamped suspended CNT electrostatically deflected by a gate electrode. We measure the…

Audio classification is considered as a challenging problem in pattern recognition. Recently, many algorithms have been proposed using deep neural networks. In this paper, we introduce a new attention-based neural network architecture…

Multi-label (ML) classification is an actively researched topic currently, which deals with convoluted and overlapping boundaries that arise due to several labels being active for a particular data instance. We propose a classifier capable…

Non-negative matrix factorization (NMF) is widely used for dimensionality reduction and interpretable analysis, but standard formulations are unsupervised and cannot directly exploit class labels. Existing supervised or semi-supervised…

Deep neural networks (DNNs) have revolutionized the field of artificial intelligence and have achieved unprecedented success in cognitive tasks such as image and speech recognition. Training of large DNNs, however, is computationally…

We present a data-calibrated compact model of carbon nanotube (CNT) field-effect transistors (CNFETs) including contact resistance, direct source-to-drain and band-to-band tunneling currents. The model captures the effects of dimensional…

The development of high-performance multifunctional polymer-based electronic circuits is a major step towards future flexible electronics. Here, we demonstrate a tunable approach to fabricate such devices based on rationally designed…

This paper introduces a new efficient autoprecoder (AP) based deep learning approach for massive multiple-input multiple-output (mMIMO) downlink systems in which the base station is equipped with a large number of antennas with…

We integrate ambipolar quantum dots in silicon fin field-effect transistors using exclusively standard complementary metal-oxide-semiconductor fabrication techniques. We realize ambipolarity by replacing conventional highly-doped source and…

Self-assembled functionalized nano particles are at the focus of a number of potential applications, in particular for molecular scale electronics devices. Here we perform experiments of self-assembly of 10 nm Au nano particles (NPs),…