Related papers: Ultra-low Power Nanoelectromechanical Memory Based…
We present a systematic study of the electronic and magnetic properties of transition-metal (TM) atomic chains adsorbed on the zigzag single-wall carbon nanotubes (SWNTs). We considered the adsorption on the external and internal wall of…
Binary stochastic neurons (BSNs) are excellent hardware accelerators for machine learning. A popular platform for implementing them are low- or zero-energy barrier nanomagnets possessing in-plane magnetic anisotropy (e.g. circular disks or…
The demand for cryogenic memory components is driven by the need for ultra-fast, low-power, and highly reliable computing systems. Phase slip-based devices promise to fulfill all these requirements, with potential applications in both…
One-dimensional nanoscale devices, such as semiconductor nanowires (NWs) and single- walled carbon nanotubes (SWNTs), have been intensively investigated because of their potential application of future high-speed electronic, optoelectronic,…
In this work, an optimized method was implemented for attaining stable multibit operation with low energy consumption in a two-terminal memory element made from the following layers: Ag/Pt nanoparticles (NPs)/SiO2/TiN in a…
In recent years, the energy consumption of computing systems has increased and a large fraction of this energy is consumed in main memory. Towards this, researchers have proposed use of non-volatile memory, such as phase change memory…
Atomically thin layered two-dimensional materials, including transition-metal dichacolgenide (TMDC) and black phosphorus (BP), (1) have been receiving much attention, because of their promising physical properties and potential applications…
We use impedance spectroscopy to measure the high frequency properties of single-walled carbon nanotube field effect transistors (swCN-FETs). Furthermore, we extend Scanning Gate Microscopy (SGM) to frequencies up to 15MHz, and use it to…
Nanoscale devices are being extensively studied for their tunable electronic and optical properties, but the influence of impurities and defects is amplified at these length scales and can lead to poorly understood variations in…
On-device learning has emerged as a promising direction for AI development, particularly because of its potential to reduce latency issues and mitigate privacy risks associated with device-server communication, while improving energy…
In laboratories, ultrahigh magnetic fields are usually produced with very large currents through superconducting, resistive or hybrid magnets, which require extreme conditions, such as low temperature, huge cooling water or tens of…
We have calculated the key characteristics of associative (content-addressable) spatial-temporal memories based on neuromorphic networks with restricted connectivity - "CrossNets". Such networks may be naturally implemented in…
Single electron transistors (SETs) made from single wall carbon nanotubes (SWCNTs) are promising for quantum electronic devices operating with ultra-low power consumption and allow fundamental studies of electron transport. We report on…
We present a new fully self-aligned single-electron memory with a single pair of nano floating gates, made of different materials (Si and Ge). The energy barrier that prevents stored charge leakage is induced not only by quantum effects but…
The inherent stochasticity in many nano-scale devices makes them prospective candidates for low-power computations. Such devices have been demonstrated to exhibit probabilistic switching between two stable states to achieve stochastic…
We present artificial neural network design using spin devices that achieves ultra low voltage operation, low power consumption, high speed, and high integration density. We employ spin torque switched nano-magnets for modelling neuron and…
Reconfigurable memristors featuring neural and synaptic functions hold great potential for neuromorphic circuits by simplifying system architecture, cutting power consumption, and boosting computational efficiency. Their additive…
We demonstrate wafer-scale integration of single electron memories based on carbon nanotube field effect transistors (cnfets) by a complete self assembly process. First, a dry self assembly based on a Hot Filament assisted Chemical Vapor…
Recent advances in nanoscale science and technology provide possibilities to directly self-assemble and integrate functional circuit elements within the wiring scheme of devices with potentially unique architectures. Electroionic resistive…
We report specifically two things, one the phenomenon of optically induced motion in pristine single walled carbon nanotubes (SWNT) on the macro-scale and other the consequent separation of metallic and semiconducting enriched SWNT…