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Semiconductor nanowires (NWires) experience stress and charge transfer by their environment and impurity atoms. In return, the environment of NWires experiences a NWire stress response which may lead to propagated strain and change in shape…
Nowadays, Au dimer-based nanostructures are exhaustively studied due to their outstanding potential as plasmonic nanoantennas for future applications in high-sensitivity molecular sensing by Surface-Enhanced Raman Spectroscopy (SERS). In…
There is a huge demand to accurately determine the magneto-electrical properties of particles in the nano sized regime due to the modern IC technology revolution and biomedical application science. In this paper, we present a microwave…
Recently, analog compute-in-memory (CIM) architectures based on emerging analog non-volatile memory (NVM) technologies have been explored for deep neural networks (DNN) to improve energy efficiency. Such architectures, however, leverage…
Resistive random-access memories, also known as memristors, whose resistance can be modulated by the electrically driven formation and disruption of conductive filaments within an insulator, are promising candidates for neuromorphic…
Ternary content addressable memory (TCAM), widely used in network routers and high-associativity caches, is gaining popularity in machine learning and data-analytic applications. Ferroelectric FETs (FeFETs) are a promising candidate for…
Quantum devices based on InSb nanowires (NWs) are a prime candidate system for realizing and exploring topologically-protected quantum states and for electrically-controlled spin-based qubits. The influence of disorder on achieving reliable…
First-principles investigations on the edge energies and edge stresses of single-layer hexagonal boron-nitride (BN) are presented. The armchair edges of BN nanoribbons (BNNRs) are more stable in energy than zigzag ones. Armchair BNNRs are…
Atomistic quantum transport simulation of realistically large devices is computationally very demanding. The widely used mode space (MS) approach can significantly reduce the numerical cost but good MS basis is usually very hard to obtain…
With the fast evolvement of embedded deep-learning computing systems, applications powered by deep learning are moving from the cloud to the edge. When deploying neural networks (NNs) onto the devices under complex environments, there are…
The non-destructive characterization of nanoscale devices, such as those based on semiconductor nanowires, in terms of functional potentials is crucial for correlating device properties with their morphological/materials features, as well…
In the recent past, the success of Neural Architecture Search (NAS) has enabled researchers to broadly explore the design space using learning-based methods. Apart from finding better neural network architectures, the idea of automation has…
Recent works propose neural network- (NN-) inspired analog-to-digital converters (NNADCs) and demonstrate their great potentials in many emerging applications. These NNADCs often rely on resistive random-access memory (RRAM) devices to…
The performance limits of the multilayer graphene nanoribbon (GNR) field-effect transistor (FET) are assessed and compared to those of monolayer GNR FET and carbon nanotube (CNT) FET. The results show that with a thin high-k gate insulator…
We present a comprehensive computational study of the electronic, thermal, and thermoelectric (TE) properties of gallium nitride nanowires (NWs) over a wide range of thicknesses (3--9 nm), doping densities ($10^{18}$--$10^{20}$ cm$^{-3}$),…
For the reliable fabrication of the current and next generation of nanostructures it is essential to be able to determine their material composition and dimensional parameters. Using the grazing incidence X-ray fluoresence technique, which…
Understanding the interfaces of layered nanostructures is key to optimizing their structural and magnetic properties for the desired functionality. In the present work, the two interfaces of a few nm thick Fe layer in Ag-57Fe-Ag trilayers…
Nanometer circuits are becoming increasingly susceptible to soft-errors due to alpha-particle and atmospheric neutron strikes as device scaling reduces node capacitances and supply/threshold voltage scaling reduces noise margins. It is…
Spiking Neural Networks (SNNs), inspired by biological intelligence, have long been considered inherently energy-efficient, making them attractive for resource-constrained domains such as space applications. However, recent comparative…
Process simulation are performed in order to simulate the full fabrication process of an alternative nano-flash memory in order to optimise it and to improve the understanding of the dot storage formation. The influence of various…