World's Best Scientists 2026 revealed!

D-Index & Metrics

Engineering and Technology

D-Index
60
Citations
20318
World Ranking
2124
National Ranking
672

Overview

Geoffrey W. Burr is affiliated with IBM in the United States and has contributed extensively to the fields of engineering and computer science. Their research primarily concentrates on electrical and electronic engineering, materials chemistry, and artificial intelligence. The subfields encompassed in their work include electrical and electronic engineering, materials chemistry, artificial intelligence, computer vision and pattern recognition, and hardware and architecture.

Their scholarly output spans multiple topics, with significant focus on advanced memory and neural computing, ferroelectric and negative capacitance devices, neural networks and reservoir computing, phase-change materials and chalcogenides, machine learning in materials science, advanced neural network applications, and electronic and structural properties of oxides.

Frequent publishing venues for their work include Nature Communications, IEEE Transactions on Electron Devices, Advanced Electronic Materials, arXiv (Cornell University), and Nature Reviews Materials.

Among their recent papers are:

  • Resistive switching materials for information processing (2020) published in Nature Reviews Materials
  • An analog-AI chip for energy-efficient speech recognition and transcription (2023) published in Nature
  • Hardware-aware training for large-scale and diverse deep learning inference workloads using in-memory computing-based accelerators (2023) published in Nature Communications
  • A Heterogeneous and Programmable Compute-In-Memory Accelerator Architecture for Analog-AI Using Dense 2-D Mesh (2022) published in IEEE Transactions on Very Large Scale Integration (VLSI) Systems
  • Optimised weight programming for analogue memory-based deep neural networks (2022) published in Nature Communications

They have collaborated frequently with the following coauthors:

  • Hsinyu Tsai
  • Charles Mackin
  • Pritish Narayanan
  • Stefano Ambrogio
  • Abu Sebastian

Best Publications

  • Phase change memory technology

    Geoffrey W. Burr;Matthew J. Breitwisch;Michele Franceschini;Davide Garetto

  • Phase-change random access memory: a scalable technology

    S. Raoux;G. W. Burr;M. J. Breitwisch;C. T. Rettner

  • Holographic data storage

    J. Ashley;M.-P. Bernal;G. W. Burr;H. Coufal

  • Neuromorphic computing using non-volatile memory

    Geoffrey W. Burr;Robert M. Shelby;Abu Sebastian;Sangbum Kim

  • Equivalent-accuracy accelerated neural-network training using analogue memory

    Stefano Ambrogio;Pritish Narayanan;Hsinyu Tsai;Robert M. Shelby

  • Resistive switching materials for information processing

    Zhongrui Wang;Huaqiang Wu;Geoffrey W. Burr;Cheol Seong Hwang

  • Experimental Demonstration and Tolerancing of a Large-Scale Neural Network (165 000 Synapses) Using Phase-Change Memory as the Synaptic Weight Element

    Geoffrey W. Burr;Robert M. Shelby;Severin Sidler;Carmelo di Nolfo

  • Overview of candidate device technologies for storage-class memory

    G. W. Burr;B. N. Kurdi;J. C. Scott;C. H. Lam

  • Improving accuracy by subpixel smoothing in the finite-difference time domain.

    A. Farjadpour;David Roundy;Alejandro Rodriguez;M. Ibanescu

  • System metric for holographic memory systems.

    Fai H. Mok;Geoffrey W. Burr;Demetri Psaltis

  • Recent Progress in Phase-Change Memory Technology

    Geoffrey W. Burr;Matthew J. BrightSky;Abu Sebastian;Huai-Yu Cheng

  • Access devices for 3D crosspoint memorya)

    Geoffrey W. Burr;Rohit S. Shenoy;Kumar Virwani;Pritish Narayanan

  • Write Strategies for 2 and 4-bit Multi-Level Phase-Change Memory

    T. Nirschl;J.B. Phipp;T.D. Happ;G.W. Burr

  • Optimization of Conductance Change in Pr 1– x Ca x MnO 3 -Based Synaptic Devices for Neuromorphic Systems

    Jun-Woo Jang;Sangsu Park;Geoffrey W. Burr;Hyunsang Hwang

  • Tutorial: Brain-inspired computing using phase-change memory devices

    Abu Sebastian;Manuel Le Gallo;Geoffrey W. Burr;Sangbum Kim

  • Novel One-Mask Self-Heating Pillar Phase Change Memory

    T. Happ;M. Breitwisch;A. Schrott;J. Philipp

  • Holographic data storage

    D. Psaltis;G.W. Burr

  • Structure for confining the switching current in phase memory (PCM) cells

    Geoffrey W. Burr;Chung Hon Lam;Simone Raoux;Stephen M. Rossnagel

  • Modulation coding for pixel-matched holographic data storage.

    Geoffrey W. Burr;Jonathan Ashley;Hans Coufal;Robert K. Grygier

  • Ultra-Thin Phase-Change Bridge Memory Device Using GeSb

    Y. C. Chen;C. T. Rettner;S. Raoux;G. W. Burr

  • Experimental demonstration and tolerancing of a large-scale neural network (165,000 synapses), using phase-change memory as the synaptic weight element

    G.W. Burr;R.M. Shelby;C. di Nolfo;J.W. Jang

  • Overview of candidate device technologies for storage-class

    G. W. Burr;B. N. Kurdi;J. C. Scott;C. H. Lam

Frequent Co-Authors

Stefano Ambrogio
Stefano Ambrogio Meta for Business
Charles T. Rettner
Charles T. Rettner IBM (United States)
Matthew J. Breitwisch
Matthew J. Breitwisch IBM (United States)
Hyunsang Hwang
Hyunsang Hwang Pohang University of Science and Technology
Simone Raoux
Simone Raoux Helmholtz-Zentrum Berlin für Materialien und Energie
Demetri Psaltis
Demetri Psaltis École Polytechnique Fédérale de Lausanne
Yusuf Leblebici
Yusuf Leblebici École Polytechnique Fédérale de Lausanne
Andrew J. Kellock
Andrew J. Kellock IBM (United States)
Chung H. Lam
Chung H. Lam IBM (United States)
Bipin Rajendran
Bipin Rajendran King's College London

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