Publications related to Analysis of Quantized Electrical Characteristics of Microscale TiO2 Ink-Jet Printed Memristor

Emulating Multimemristive Behavior of Silicon Nanowire-Based Biosensors by Using CMOS-Based Implementations

Sandro Carrara, Junrui Chen, Kapil Bhardwaj

The research presented in this article draws inspiration from previous efforts aimed at replicating the functions of various solid-state memristors using a variety of materials. The memristor circuit emulator serves as a cost-effective tool for circuit des ...

Ieee-Inst Electrical Electronics Engineers Inc2024

3D trigonal FAPbI(3)-based multilevel resistive switching nonvolatile memory for artificial neural synapse

Mohammad Khaja Nazeeruddin, Paul Joseph Dyson, Yong Ding, Bowen Jiang, Li Tao, Jun Zhang, Yao Zhang

Hybrid perovskites have attracted enormous attention in the next generation resistive switching (RS) memristor for the artificial synapses, owing to their ambipolar charge transport, long diffusion length, and tunable visible bandgap. However, the variable ...

WILEY2023

Scaling Description of Dynamical Heterogeneity and Avalanches of Relaxation in Glass-Forming Liquids

Ali Tahaei, Matthieu Wyart

We provide a theoretical description of dynamical heterogeneities in glass-forming liquids, based on the premise that relaxation occurs via local rearrangements coupled by elasticity. In our framework, the growth of the dynamical correlation length e and o ...

American Physical Society2023

Scrutinizing thermally stimulated current transients originating from trapped charges in organic semiconductors: A drift-diffusion study

Frank Nüesch, Beat Ruhstaller

Thermally stimulated current (TSC) is a widely used technique to assess trap states and extract their density, energy, and capture rate using analytical expressions. In many cases, the latter are derived from physical models pertaining to inorganic semicon ...

AIP Publishing2022

Multi-memristive synaptic architectures for training neural networks

Irem Boybat Kara

Highly data-centric AI workloads require that new computing paradigms be adopted because the performance of traditional CPU- and GPU-based systems are limited by data access and transfer. Training deep neural networks with millions of tunable parameters ta ...

EPFL2020

First demonstration of real-time kinetic equilibrium reconstruction on TCV by coupling LIUQE and RAPTOR

Jean-Marc Moret, Olivier Sauter, Federico Alberto Alfredo Felici, Antoine Pierre Emmanuel Alexis Merle, Cristian Galperti, Francesco Carpanese

Kinetic equilibrium reconstruction (KER) in tokamaks is the solution of the free-boundary MHD equilibrium that best fits the external magnetic measurements and the internal plasma profiles as imposed from modeling and/or available internal measurements. In ...

IOP Publishing Ltd2020

Thermal coarsening of individual titanate nanowires and their assemblies: Surface vs. bulk diffusion

Luca Rossi, László Forró, Xavier Berdat, Massimo Spina, Endre Horvath, Laurent Syavoch Bernard, Sergiy Katrych

The thermal coarsening of titanate nanowires was investigated from room temperature to 1000 degrees C by AFM, SEM, XRD and Raman spectroscopy. Phase transformation kinetics and the external morphology were studied in two configurations: i) as individual an ...

2020

Reliable and Robust Cyber-Physical Systems for Real-Time Control of Electric Grids

Wajeb Saab

Real-time control of electric grids is a novel approach to handling the increasing penetration of distributed and volatile energy generation brought about by renewables. Such control occurs in cyber-physical systems (CPSs), in which software agents maintai ...

EPFL2019

Oxygen diffusion through air–water free surfaces in a pump–turbine operating in condenser mode

François Avellan, Elena Vagnoni, Loïc Andolfatto, Pierre Leroy

The present research aims at understanding the mass transfer of air into water in a reversible pump turbine operating in condenser mode. In particular, the diffusion of oxygen through the water free-surface both in the vaneless gap between the impeller bla ...

2019

Truncated Levy motion through path integrals and applications to nondiffusive suprathermal ion transport

Ambrogio Fasoli, Ivo Furno, Paolo Ricci, Oulfa Chellaï, Marcelo Baquero Ruiz, Fabian Manke

Fractional Levy motion has been derived from its generalized Langevin equation via path integrals in earlier works and has since proven to be a useful model for nonlocal and non-Markovian processes, especially in the context of nondiffusive transport. Here ...

AMER PHYSICAL SOC2019

Dissolution-reprecipitation reaction of dolomite: investigation of self- and tracer-diffusion using SIMS mini-spot and NanoSIMS profiles

Lukas Baumgartner, Anders Meibom, Stéphane Laurent Escrig

Recrystallization of minerals due to fluid infiltration modifies their original chemical and isotopic signature. Such processes are sought after as they give access to timescales of geologic processes, which are commonly quantified using diffusion chronome ...

2019

Observation of enhanced ion particle transport in mixed H/D isotope plasmas on JET

Patrick Blanchard, Basil Duval, Ambrogio Fasoli, Yves Martin, Richard Pitts, Olivier Sauter, Henri Weisen, Robin Humphry-Baker, Stefano Coda, Martinus Gijs, Partha Dutta, Marco Wischmeier, Jan Horacek, Vlad Trifa, Yann Camenen, Jonathan Graves, Duccio Testa, Miguel Fernández Ruiz, Sun Hee Kim, Mikhail Maslov, Javier García Hernández, David Pfefferlé, José Pedro Rebelo Ferreira Marques, Julio Rodriguez, Pedro Camilo de Oliveira e Silva, Rebecca Hill, Daniel Scott Alessi, Jonnathan Cesar Hidalgo Acosta, Ana Francisca Leal Silva Soares, Dalziel Joseph Wilson, Pierre-Thomas Paul Brun, Jonathan Marc Philippe Faustin, Federico Nespoli, Alberto Hernando de Castro, Hamish William Patten, Bruno Emanuel Ferreira De Sousa Correia, Li Shuai, Nicola Vianello, Antonio José Pereira de Figueiredo, , , , , , , , , ,

Unsupervised Learning of Phase-Change-Based Neuromorphic Systems

Stanislaw Andrzej Wozniak

Neuromorphic systems provide brain-inspired methods of computing. In a neuromorphic architecture, inputs are processed by a network of neurons receiving operands through synaptic interconnections, tuned in the process of learning. Neurons act simultaneousl ...

EPFL2017

Unsupervised Learning Using Phase-Change Synapses and Complementary Patterns

Yusuf Leblebici, Severin Sidler, Stanislaw Andrzej Wozniak, Angeliki Pantazi, Evangelos Eleftheriou

Neuromorphic systems using memristive devices provide a brain-inspired alternative to the classical von Neumann processor architecture. In this work, a spiking neural network (SNN) implemented using phase-change synapses is studied. The network is equipped ...

2017

Thermal Diffusion in Binary Mixtures: Transient Behavior and Transport Coefficients from Equilibrium and Nonequilibrium Molecular Dynamics

Mauro Ferrario, Sara Bonella

Equilibrium and nonequilibrium molecular dynamics simulations are combined to compute the full set of coefficients that appear in the phenomenological equations describing thermal transport in a binary mixture subject to a constant thermal, gradient The Dy ...

Amer Chemical Soc2017

All-memristive neuromorphic computing with level-tuned neurons

Stanislaw Andrzej Wozniak, Angeliki Pantazi, Evangelos Eleftheriou

In the new era of cognitive computing, systems will be able to learn and interact with the environment in ways that will drastically enhance the capabilities of current processors, especially in extracting knowledge from vast amount of data obtained from m ...

Iop Publishing Ltd2016

Label-Free Ultrasensitive Memristive Aptasensor

Giovanni De Micheli, Sandro Carrara, Ioulia Tzouvadaki

We present the very first worldwide ever-reported electrochemical biosensor based on a memristive effect and DNA aptamers. This novel device is developed to propose a completely new approach in cancer diagnostics. In this study, an affinity-based technique ...

American Chemical Society2016

Cross-Layer Energy-Efficiency Optimization of Packet Based Wireless MIMO Communication Systems

Andreas Peter Burg, Carl Christian Sten Dominic Senning, Georgios Karakonstantis

Energy in today's short-range wireless communication is mostly spent on the analog-and digital hardware rather than on radiated power. Hence, purely information-theoretic considerations fail to achieve the lowest energy per information bit and the optimiza ...

Springer Verlag2016

Source Localization on Graphs via l1 Recovery and Spectral Graph Theory

Pierre Vandergheynst, Xavier Bresson, Rodrigo Cerqueira Gonzalez Pena

We cast the problem of source localization on graphs as the simultaneous problem of sparse recovery and diffusion ker- nel learning. An l1 regularization term enforces the sparsity constraint while we recover the sources of diffusion from a single snapshot ...

Ieee2016

Modeling Memristive Biosensors

Giovanni De Micheli, Sandro Carrara, Francesca Puppo, Ioulia Tzouvadaki

In the present work, a computational study is carried out investigating the relationship between the biosensing and the electrical characteristics of two-terminal Schottky- barrier silicon nanowire devices. The model suggested successfully reproduces compu ...

2015