A simple and controlled single electron transistor based on doping modulation in silicon nanowires

A simple and highly reproducible single electron transistor (SET) has been fabricated using gated silicon nanowires. The structure is a metal-oxide-semiconductor field-effect transistor made on silicon-on-insulator thin films. The channel of the transistor is the Coulomb island at low temperature. Two silicon nitride spacers deposited on each side of the gate create a modulation of doping along the nanowire that creates tunnel barriers. Such barriers are fixed and controlled, like in metallic SETs. The period of the Coulomb oscillations is set by the gate capacitance of the transistor and therefore controlled by lithography. The source and drain capacitances have also been characterized. This design could be used to build more complex SET devices.Comment: to be published in Applied Physics Letter

Individual charge traps in silicon nanowires: Measurements of location, spin and occupation number by Coulomb blockade spectroscopy

We study anomalies in the Coulomb blockade spectrum of a quantum dot formed in a silicon nanowire. These anomalies are attributed to electrostatic interaction with charge traps in the device. A simple model reproduces these anomalies accurately and we show how the capacitance matrices of the traps can be obtained from the shape of the anomalies. From these capacitance matrices we deduce that the traps are located near or inside the wire. Based on the occurrence of the anomalies in wires with different doping levels we infer that most of the traps are arsenic dopant states. In some cases the anomalies are accompanied by a random telegraph signal which allows time resolved monitoring of the occupation of the trap. The spin of the trap states is determined via the Zeeman shift.Comment: 9 pages, 8 figures, v2: section on RTS measurements added, many improvement

Experimental approval of the extended flat bands and gapped subbands in rhombohedral multilayer graphene

Graphene layers are known to stack in two stable configurations, namely ABA or ABC stacking, with drastically distinct electronic properties. Unlike the ABA stacking, little has been done to experimentally investigate the electronic properties of ABC graphene multilayers. Here, we report the first magneto optical study of a large ABC domain in a graphene multilayers flake, with ABC sequences exceeding 17 graphene sheets. The ABC-stacked multilayers can be fingerprinted with a characteristic electronic Raman scattering response, which persists even at room temperatures. Tracing the magnetic field evolution of the inter Landau level excitations from this domain gives strong evidence to the existence of a dispersionless electronic band near the Fermi level, characteristic of such stacking. Our findings present a simple yet powerful approach to probe ABC stacking in graphene multilayer flakes, where this highly degenerated band appears as an appealing candidate to host strongly correlated states.Comment: 8 pages, 4 figure

Background charges and quantum effects in quantum dots transport spectroscopy

We extend a simple model of a charge trap coupled to a single-electron box to energy ranges and parameters such that it gives new insights and predictions readily observable in many experimental systems. We show that a single background charge is enough to give lines of differential conductance in the stability diagram of the quantum dot, even within undistorted Coulomb diamonds. It also suppresses the current near degeneracy of the impurity charge, and yields negative differential lines far from this degeneracy. We compare this picture to two other accepted explanations for lines in diamonds, based respectively on the excitation spectrum of a quantum dot and on fluctuations of the density-of-states in the contacts. In order to discriminate between these models we emphasize the specific features related to environmental charge traps. Finally we show that our model accounts very well for all the anomalous features observed in silicon nanowire quantum dots.Comment: 7 pages, 6 figure

A self-adaptive hardware with resistive switching synapses for experience-based neurocomputing

: Neurobiological systems continually interact with the surrounding environment to refine their behaviour toward the best possible reward. Achieving such learning by experience is one of the main challenges of artificial intelligence, but currently it is hindered by the lack of hardware capable of plastic adaptation. Here, we propose a bio-inspired recurrent neural network, mastered by a digital system on chip with resistive-switching synaptic arrays of memory devices, which exploits homeostatic Hebbian learning for improved efficiency. All the results are discussed experimentally and theoretically, proposing a conceptual framework for benchmarking the main outcomes in terms of accuracy and resilience. To test the proposed architecture for reinforcement learning tasks, we study the autonomous exploration of continually evolving environments and verify the results for the Mars rover navigation. We also show that, compared to conventional deep learning techniques, our in-memory hardware has the potential to achieve a significant boost in speed and power-saving

Hardware calibrated learning to compensate heterogeneity in analog RRAM-based Spiking Neural Networks

Spiking Neural Networks (SNNs) can unleash the full power of analog Resistive Random Access Memories (RRAMs) based circuits for low power signal processing. Their inherent computational sparsity naturally results in energy efficiency benefits. The main challenge implementing robust SNNs is the intrinsic variability (heterogeneity) of both analog CMOS circuits and RRAM technology. In this work, we assessed the performance and variability of RRAM-based neuromorphic circuits that were designed and fabricated using a 130 nm technology node. Based on these results, we propose a Neuromorphic Hardware Calibrated (NHC) SNN, where the learning circuits are calibrated on the measured data. We show that by taking into account the measured heterogeneity characteristics in the off-chip learning phase, the NHC SNN self-corrects its hardware non-idealities and learns to solve benchmark tasks with high accuracy. This work demonstrates how to cope with the heterogeneity of neurons and synapses for increasing classification accuracy in temporal tasks

Effects of oxygen partial pressure and annealing temperature on the formation of sputtered tungsten oxide films

Thin films of tungsten oxide were deposited on silicon substrates using reactive radio frequency sputtering. The structure of the films strongly depends on the conditions of deposition and post-treatment. Important issues are the influences of oxygen pressure during deposition and annealing temperature on the morphology. Atomic force microscopy and scanning electron microscopy revealed that films were formed by grains. The sample deposited with an Ar:O(2) partial pressure ratio of 1: 1 showed the highest roughness and the smallest grains when annealed at 350degrees C. X-ray photoelectron spectroscopy analysis revealed that the films were close to their stoichiometric formulation irrespective of the oxygen partial pressure used during film deposition. The number of W=O bonds at the grain boundaries was found to be dependent on the oxygen partial pressure. Analysis by Raman spectroscopy suggested that the structure of the films was monoclinic. On the basis of these results, an annealing temperature of 350degrees C was selected as post-treatment for the fabrication of WO(3) gas sensors. These sensors were highly sensitive, highly selective to ammonia vapors, and moderately responsive to humidity. (C) 2002 The Electrochemical Society.1493H81H8