activity
20092023
most citedHigh-Precision Tuning of State for Memristive Devices by Adaptable Variation-Tolerant Algorithm

504 citations · 1.7k across the 31 of their papers we have counts for

collaborators

31 papers

cs.AR2023★ 1 cited

Hardware-aware Training Techniques for Improving Robustness of Ex-Situ Neural Network Transfer onto Passive TiO2 ReRAM Crossbars

Philippe Drolet, Raphaël Dawant, Victor Yon +9

Passive resistive random access memory (ReRAM) crossbar arrays, a promising emerging technology used for analog matrix-vector multiplications, are far superior to their active (1T1…

eess.SY2023★ 1 cited

A tunable and versatile 28nm FD-SOI crossbar output circuit for low power analog SNN inference with eNVM synapses

Joao Henrique Quintino Palhares, Yann Beilliard, Jury Sandrini +7

In this work we report a study and a co-design methodology of an analog SNN crossbar output circuit designed in a 28nm FD-SOI technology node that comprises a tunable current atten…

cs.AR2022★ 10 cited

Memristor-based cryogenic programmable DC sources for scalable in-situ quantum-dot control

Pierre-Antoine Mouny, Yann Beilliard, Sébastien Graveline +8

Current quantum systems based on spin qubits are controlled by classical electronics located outside the cryostat at room temperature. This approach creates a major wiring bottlene…

cs.NE2022★ 21 cited

Voltage-Dependent Synaptic Plasticity (VDSP): Unsupervised probabilistic Hebbian plasticity rule based on neurons membrane potential

Nikhil Garg, Ismael Balafrej, Terrence C. Stewart +7

This study proposes voltage-dependent-synaptic plasticity (VDSP), a novel brain-inspired unsupervised local learning rule for the online implementation of Hebb's plasticity mechani…

physics.app-ph2021

Multi-terminal memristive devices enabling tunable synaptic plasticity in neuromorphic hardware: a mini-review

Yann Beilliard, Fabien Alibart

Neuromorphic computing based on spiking neural networks has the potential to significantly improve on-line learning capabilities and energy efficiency of artificial intelligence, s…

eess.SP2021★ 1 cited

Bio-inspired adaptive sensing through electropolymerization of organic electrochemical transistors

Mahdi Ghazal, Michel Daher Mansour, Corentin Scholaert +4

Organic Electrochemical Transistors are considered today as a key technology to interact with biological medium through their intrinsic ionic-electronic coupling. In this paper, we…

cond-mat.dis-nn2021

Dendritic organic electrochemical transistors grown by electropolymerization for 3D neuromorphic engineering

Kamila Janzakova, Mahdi Ghazal, Ankush Kumar +3

One of the major limitation of standard top-down technologies used in today's neuromorphic engineering is their inability to map the 3D nature of biological brains. Here, we show h…

cond-mat.dis-nn2021

Theoretical modeling of dendrite growth from conductive wire electropolymerization

Ankush Kumar, Kamila Janzakova, Yannick Coffinier +2

Electropolymerization is a bottom-up materials engineering process of micro and nano-scale that utilizes electrical signals to deposit conducting dendrites' morphologies by a redox…

cs.ET2021

Fully CMOS-compatible passive TiO2-based memristor crossbars for in-memory computing

Abdelouadoud El Mesoudy, Gwénaëlle Lamri, Raphaël Dawant +7

Brain-inspired computing and neuromorphic hardware are promising approaches that offer great potential to overcome limitations faced by current computing paradigms based on traditi…

eess.SP2021★ 16 cited

Signals to Spikes for Neuromorphic Regulated Reservoir Computing and EMG Hand Gesture Recognition

Nikhil Garg, Ismael Balafrej, Yann Beilliard +3

Surface electromyogram (sEMG) signals result from muscle movement and hence they are an ideal candidate for benchmarking event-driven sensing and computing. We propose a simple yet…

cond-mat.dis-nn2021★ 24 cited

Analog Programing of Conducting-Polymer Dendritic Interconnections and Control of their Morphology

Kamila Janzakova, Ankush Kumar, Mahdi Ghazal +4

Although materials and processes are different from biological cells', brain mimicries led to tremendous achievements in massively parallel information processing via neuromorphic…

physics.app-ph2020★ 1 cited

Oxygen vacancy engineering of TaOx-based resistive memories by Zr doping for improved variability and synaptic behavior

Joao H. Quintino Palhares, Yann Beilliard, Fabien Alibart +5

Resistive switching devices are promising emerging non-volatile memories. However, one of the biggest challenges for resistive switching (RS) memory applications is the device-to-d…

cs.ET2020★ 1 cited

AIDX: Adaptive Inference Scheme to Mitigate State-Drift in Memristive VMM Accelerators

Tony Liu, Amirali Amirsoleimani, Fabien Alibart +3

An adaptive inference method for crossbar (AIDX) is presented based on an optimization scheme for adjusting the duration and amplitude of input voltage pulses. AIDX minimizes the l…

physics.app-ph2020★ 9 cited

Conductive filament evolution dynamics revealed by cryogenic (1.5 K) multilevel switching of CMOS-compatible Al2O3/TiO2 resistive memories

Yann Beilliard, François Paquette, Frédéric Brousseau +3

This study demonstrates multilevel switching at 1.5 K of Al2O3/TiO2-x resistive memory devices fabricated with CMOS-compatible processes and materials. The I-V characteristics exhi…

physics.app-ph2019★ 28 cited

Observation of Highly Nonlinear Resistive Switching of Al2O3/TiO2-x Memristors at Cryogenic Temperature (1.5 K)

Yann Beilliard, François Paquette, Frédéric Brousseau +3

In this work, we investigate the behavior of Al2O3/TiO2-x cross-point memristors in cryogenic environment. We report successful resistive switching of memristor devices from 300 K…

physics.app-ph2019★ 12 cited

Physical mechanisms involved in the formation and operation of memory devices based on a monolayer of gold nanoparticles-polythiophene hybrid materials

T. Zhang, D. Guérin, F. Alibart +10

Understanding the physical and chemical mechanisms occurring during the forming process and operation of an organic resistive memory device is a major issue for better performances…

physics.app-ph2018★ 48 cited

Perspective: Organic electronic materials and devices for neuromorphic engineering

Sebastien Pecqueur, Dominique Vuillaume, Fabien Alibart

Neuromorphic computing and engineering has been the focus of intense research efforts that have been intensified recently by the mutation of Information and Communication Technolog…

physics.app-ph2018★ 27 cited

Light-stimulable molecules/nanoparticles networks for switchable logical functions and reservoir computing

Y. Viero, D. Guerin, A. Vladyka +4

We report the fabrication and electron transport properties of nanoparticles self-assembled networks (NPSAN) of molecular switches (azobenzene derivatives) interconnected by Au nan…

physics.app-ph2018★ 65 cited

Neuromorphic Time-Dependent Pattern Classification with Organic Electrochemical Transistor Arrays

Sebastien Pecqueur, Maurizio Mastropasqua Talamo, David Guerin +4

Based on bottom-up assembly of highly variable neural cells units, the nervous system can reach unequalled level of performances with respect to standard materials and devices used…

physics.app-ph2018★ 38 cited

Cation Discrimination in Organic Electrochemical Transistors by Dual Frequency Sensing

Sebastien Pecqueur, David Guerin, Dominique Vuillaume +1

In this work, we propose a strategy to sense quantitatively and specifically cations, out of a single organic electrochemical transistor (OECT) device exposed to an electrolyte. Fr…

physics.app-ph2017★ 2 cited

Concentric-electrode organic electrochemical transistors: case study for selective hydrazine sensing

S Pecqueur, S Lenfant, D Guerin +2

We report on hydrazine-sensing organic electrochemical transistors (OECTs) with a design consisting in concentric annular electrodes. The design engineering of these OECTs was moti…

cs.ET2016★ 14 cited

A Reconfigurable FIR Filter with Memristor-Based Weights

F. Merrikh Bayat, F. Alibart, L. Gao +1

We report on experimental demonstration of a mixed-signal 6-tap finite-impulse response (FIR) filter in which weights are implemented with titanium dioxide memristive devices. In t…

cs.NE2016

Exploiting the Short-term to Long-term Plasticity Transition in Memristive Nanodevice Learning Architectures

Christopher H. Bennett, Selina La Barbera, Adrien F. Vincent +2

Memristive nanodevices offer new frontiers for computing systems that unite arithmetic and memory operations on-chip. Here, we explore the integration of electrochemical metallizat…

physics.bio-ph2015★ 221 cited

Filamentary Switching: Synaptic Plasticity through Device Volatility

Selina La Barbera, Dominique Vuillaume, Fabien Alibart

Replicating the computational functionalities and performances of the brain remains one of the biggest challenges for the future of information and communication technologies. Such…