World's Best Scientists 2026 revealed!

D-Index & Metrics

Electronics and Electrical Engineering

D-Index
35
Citations
5254
World Ranking
5559
National Ranking
112

Overview

E. Vianello is affiliated with the French Alternative Energies and Atomic Energy Commission (CEA) in France. Their research focuses primarily on engineering and neuroscience, with significant contributions to the subfields of electrical and electronic engineering, cellular and molecular neuroscience, artificial intelligence, cognitive neuroscience, and materials chemistry.

The scientist's publication record reflects extensive work in advanced memory systems and neural computing. Key topics addressed in their research include advanced memory and neural computing, ferroelectric and negative capacitance devices, CCD and CMOS imaging sensors, neuroscience and neural engineering, neural dynamics and brain function, photoreceptor and optogenetics research, and neural networks and reservoir computing.

E. Vianello's recent scientific papers include:

  • Embedded Devices for Neuromorphic Time-Series Assessment (2022) - Maryland Shared Open Access Repository (USMAI Consortium)
  • In situ learning using intrinsic memristor variability via Markov chain Monte Carlo sampling (2021) - Nature Electronics
  • Roadmap to neuromorphic computing with emerging technologies (2024) - APL Materials
  • The growing memristor industry (2025) - Nature
  • DenRAM: neuromorphic dendritic architecture with RRAM for efficient temporal processing with delays (2024) - Nature Communications

Their collaborative efforts involve frequent co-authorship with several researchers, including Damien Querlioz, Tifenn Hirtzlin, Thomas Dalgaty, Jean-Michel Portal, and Giacomo Indiveri, each contributing to multiple joint publications.

Publication outlets where E. Vianello has most frequently contributed feature:

  • arXiv (Cornell University)
  • Nature Communications
  • Nature Electronics
  • Frontiers in Neuroscience
  • Research Square (Research Square)

The breadth of their research spans core scientific challenges in electrical engineering and neural system modeling, with application in emerging memory technology and neuromorphic computing paradigms. Their interdisciplinary approach integrates engineering principles with neuroscience insights to explore device-level implementations supporting brain-inspired computation.

Best Publications

  • HfO 2 -Based OxRAM Devices as Synapses for Convolutional Neural Networks

    Daniele Garbin;Elisa Vianello;Olivier Bichler;Quentin Rafhay

  • Understanding RRAM endurance, retention and window margin trade-off using experimental results and simulations

    C. Nail;G. Molas;P. Blaise;G. Piccolboni

  • HfO 2 -Based RRAM: Electrode Effects, Ti/HfO 2 Interface, Charge Injection, and Oxygen (O) Defects Diffusion Through Experiment and Ab Initio Calculations

    Boubacar Traore;Philippe Blaise;Elisa Vianello;Luca Perniola

  • Resistive Memories for Ultra-Low-Power embedded computing design

    E. Vianello;O. Thomas;G. Molas;O. Turkyilmaz

  • Resistive random access memory (RRAM) technology: From material, device, selector, 3D integration to bottom-up fabrication

    Hong Yu Chen;Stefano Brivio;Che Chia Chang;Jacopo Frascaroli

  • On the Origin of Low-Resistance State Retention Failure in HfO 2 -Based RRAM and Impact of Doping/Alloying

    Boubacar Traore;Philippe Blaise;Elisa Vianello;Helen Grampeix

  • Variability-tolerant Convolutional Neural Network for Pattern Recognition applications based on OxRAM synapses

    D. Garbin;O. Bichler;E. Vianello;Q. Rafhay

  • In-Memory and Error-Immune Differential RRAM Implementation of Binarized Deep Neural Networks

    M. Bocquet;T. Hirztlin;J.-O. Klein;E. Nowak

  • 3D Sequential Integration: Application-driven technological achievements and guidelines

    P. Batude;L. Brunet;C. Fenouillet-Beranger;F. Andrieu

  • A Combined Ab Initio and Experimental Study on the Nature of Conductive Filaments in ${ m Pt}/{ m Hf}{ m O}_{2}/{ m Pt}$ Resistive Random Access Memory

    Kan-Hao Xue;Boubacar Traore;Philippe Blaise;Leonardo R. C. Fonseca

  • Experimental and Simulation Analysis of Program/Retention Transients in Silicon Nitride-Based NVM Cells

    E. Vianello;F. Driussi;A. Arreghini;P. Palestri

  • Digital Biologically Plausible Implementation of Binarized Neural Networks With Differential Hafnium Oxide Resistive Memory Arrays.

    Tifenn Hirtzlin;Marc Bocquet;Bogdan Penkovsky;Jacques-Olivier Klein

  • Explanation of the Charge Trapping Properties of Silicon Nitride Storage Layers for NVMs—Part II: Atomistic and Electrical Modeling

    E. Vianello;F. Driussi;P. Blaise;P. Palestri

  • Resistive RAM With Multiple Bits Per Cell: Array-Level Demonstration of 3 Bits Per Cell

    Binh Q. Le;Alessandro Grossi;Elisa Vianello;Tony Wu

  • High-Density 3D Monolithically Integrated Multiple 1T1R Multi-Level-Cell for Neural Networks

    E. Esmanhotto;L. Brunet;N. Castellani;D. Bonnet

  • Processing EMG signals using reservoir computing on an event-based neuromorphic system

    Elisa Donati;Melika Payvand;Nicoletta Risi;Renate Krause

  • 28nm advanced CMOS resistive RAM solution as embedded non-volatile memory

    Antoine Benoist;S. Blonkowski;S. Jeannot;S. Denorme

  • Sb-doped GeS 2 as performance and reliability booster in Conductive Bridge RAM

    E. Vianello;G. Molas;F. Longnos;P. Blaise

  • Hybrid neuromorphic circuits exploiting non-conventional properties of RRAM for massively parallel local plasticity mechanisms

    Thomas Dalgaty;Melika Payvand;Filippo Moro;Denys R. B. Ly

  • Controlling oxygen vacancies in doped oxide based CBRAM for improved memory performances

    G. Molas;E. Vianello;F. Dahmani;M. Barci

  • Investigation of the physical mechanisms governing data-retention in down to 10nm nano-trench Al 2 O 3 /CuTeGe conductive bridge RAM (CBRAM)

    J. Guy;G. Molas;E. Vianello;F. Longnos

  • 14.3 A 43pJ/Cycle Non-Volatile Microcontroller with 4.7μs Shutdown/Wake-up Integrating 2.3-bit/Cell Resistive RAM and Resilience Techniques

    Tony F. Wu;Binh Q. Le;Robert Radway;Andrew Bartolo

Frequent Co-Authors

B. De Salvo
B. De Salvo Meta for Business
Gerard Ghibaudo
Gerard Ghibaudo Grenoble Alpes University
Yoshio Nishi
Yoshio Nishi Stanford University
Luca Larcher
Luca Larcher University of Modena and Reggio Emilia
O. Faynot
O. Faynot CEA LETI
Luca Selmi
Luca Selmi University of Modena and Reggio Emilia
Alessandro Paccagnella
Alessandro Paccagnella University of Padua
David Esseni
David Esseni University of Udine

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