World's Best Scientists 2026 revealed!

D-Index & Metrics

Electronics and Electrical Engineering

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

E. Vianello publication distribution in Electronics and Electrical Engineering in 2026

The chart shows the distribution of publications by all Research.com ranked scientists in the field of Electronics and Electrical Engineering in 2026. The highlighted bar marks where E. Vianello sits on this spectrum.

34–53 publications: 24 scientists 54–73 publications: 52 scientists 74–93 publications: 114 scientists 94–113 publications: 203 scientists 114–133 publications: 269 scientists 134–153 publications: 355 scientists 154–173 publications: 403 scientists 174–193 publications: 445 scientists 194–213 publications: 430 scientists 214–233 publications: 431 scientists 234–253 publications: 399 scientists 254–273 publications: 366 scientists 274–293 publications: 335 scientists 294–313 publications: 300 scientists 314–333 publications: 276 scientists 334–353 publications: 250 scientists 354–373 publications: 214 scientists 374–393 publications: 187 scientists 394–413 publications: 152 scientists 414–433 publications: 169 scientists 434–453 publications: 147 scientists 454–473 publications: 111 scientists 474–493 publications: 117 scientists 494–513 publications: 103 scientists 514–533 publications: 99 scientists 534–553 publications: 92 scientists 554–573 publications: 75 scientists 574–593 publications: 58 scientists 594–613 publications: 69 scientists 614–633 publications: 50 scientists 634–653 publications: 62 scientists 654–673 publications: 54 scientists 674–693 publications: 44 scientists 694–713 publications: 37 scientists 714–733 publications: 28 scientists 734–753 publications: 26 scientists 754–773 publications: 26 scientists 774–793 publications: 19 scientists 794–813 publications: 23 scientists 814–833 publications: 20 scientists 834–853 publications: 16 scientists 854–873 publications: 20 scientists 874–893 publications: 11 scientists 894–913 publications: 11 scientists 914–933 publications: 16 scientists 934–953 publications: 13 scientists 954–973 publications: 10 scientists 974–993 publications: 11 scientists 994–1,013 publications: 9 scientists 1,014–1,033 publications: 9 scientists 1,034–1,053 publications: 10 scientists 1,054–1,064 publications: 6 scientists 1,065+ publications: 99 scientists
34 publications 1,065+

This scientist: 201 publications — 30th percentile

30% of scientists in this discipline score the same or lower.

The last bar groups every scientist with 1,065 publications or more.

E. Vianello D-index placement in Electronics and Electrical Engineering in 2026

The chart shows the D-index (discipline H-index) distribution of Electronics and Electrical Engineering scientists ranked by Research.com in 2026. The highlighted bar marks where E. Vianello sits on this spectrum.

30 D-Index: 178 scientists 31 D-Index: 257 scientists 32 D-Index: 263 scientists 33 D-Index: 262 scientists 34 D-Index: 244 scientists 35 D-Index: 236 scientists 36 D-Index: 211 scientists 37 D-Index: 220 scientists 38 D-Index: 214 scientists 39 D-Index: 214 scientists 40 D-Index: 205 scientists 41 D-Index: 187 scientists 42 D-Index: 194 scientists 43 D-Index: 201 scientists 44 D-Index: 155 scientists 45 D-Index: 189 scientists 46 D-Index: 148 scientists 47 D-Index: 160 scientists 48 D-Index: 134 scientists 49 D-Index: 130 scientists 50 D-Index: 141 scientists 51 D-Index: 156 scientists 52 D-Index: 108 scientists 53 D-Index: 130 scientists 54 D-Index: 112 scientists 55 D-Index: 97 scientists 56 D-Index: 111 scientists 57 D-Index: 102 scientists 58 D-Index: 108 scientists 59 D-Index: 120 scientists 60 D-Index: 103 scientists 61 D-Index: 93 scientists 62 D-Index: 92 scientists 63 D-Index: 74 scientists 64 D-Index: 77 scientists 65 D-Index: 73 scientists 66 D-Index: 64 scientists 67 D-Index: 69 scientists 68 D-Index: 60 scientists 69 D-Index: 39 scientists 70 D-Index: 57 scientists 71 D-Index: 59 scientists 72 D-Index: 46 scientists 73 D-Index: 49 scientists 74 D-Index: 38 scientists 75 D-Index: 35 scientists 76 D-Index: 32 scientists 77 D-Index: 35 scientists 78 D-Index: 31 scientists 79 D-Index: 22 scientists 80 D-Index: 34 scientists 81 D-Index: 31 scientists 82 D-Index: 34 scientists 83 D-Index: 23 scientists 84 D-Index: 18 scientists 85 D-Index: 30 scientists 86 D-Index: 19 scientists 87 D-Index: 19 scientists 88 D-Index: 20 scientists 89 D-Index: 8 scientists 90 D-Index: 17 scientists 91 D-Index: 7 scientists 92 D-Index: 14 scientists 93 D-Index: 9 scientists 94 D-Index: 15 scientists 95 D-Index: 10 scientists 96 D-Index: 12 scientists 97 D-Index: 10 scientists 98 D-Index: 10 scientists 99 D-Index: 12 scientists 100 D-Index: 16 scientists 101 D-Index: 5 scientists 102 D-Index: 7 scientists 103 D-Index: 7 scientists 104 D-Index: 8 scientists 105 D-Index: 9 scientists 106 D-Index: 13 scientists 107 D-Index: 4 scientists 108 D-Index: 5 scientists 109 D-Index: 10 scientists 110 D-Index: 8 scientists 111+ D-Index: 96 scientists
30 D-Index 111+

This scientist: 35 D-Index — 21st percentile

21% of scientists in this discipline score the same or lower.

The last bar groups every scientist with 111 D-Index or more.

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
Damien Querlioz
Damien Querlioz University of Paris-Saclay
Alessandro Paccagnella
Alessandro Paccagnella University of Padua
Luca Selmi
Luca Selmi University of Modena and Reggio Emilia

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