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

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

Tiña Negra. Reporte del primer caso en Paraguay

La tiña negra es una micosis superficial causada por la Hortaea werneckiique afecta la capacórnea de palma de manos, rara vez de planta de pies y otros sitios. La distribución es universal,pero es rara; y en el Paraguay no existen comunicaciones previas. En este trabajo se presenta elcaso de un varón de 16 años con mácula pigmentada en palma de mano derecha cuyo estudiomicológico confirmó el diagnóstico clínico

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

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

Measuring personal networks and their relationship with scientific production

The analysis of social networks has remained a crucial and yet understudied aspect of the efforts to measure Triple Helix linkages. The Triple Helix model aims to explain, among other aspects of knowledge-based societies, ¿the current research system in its social context. This paper develops a novel approach to study the research system from the perspective of the individual, through the analysis of the relationships among researchers, and between them and other social actors. We develop a new set of techniques and show how they can be applied to the study of a specific case (a group of academics within a university department). We analyse their informal social networks and show how a relationship exists between the characteristics of an individual¿s network of social links and his or her research output