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D-Index & Metrics

Electronics and Electrical Engineering

D-Index
57
Citations
33696
World Ranking
1918
National Ranking
751

Research.com Recognitions

  • 2013 - Hellman Fellow

Overview

Dmitri B. Strukov is affiliated with the University of California, Santa Barbara in the United States. Their research spans multiple fields, centered largely on engineering and computer science with specific focus areas in electrical and electronic engineering, artificial intelligence, and cellular and molecular neuroscience.

The main topics covered by Dmitri B. Strukov's research include:

  • Advanced Memory and Neural Computing
  • Ferroelectric and Negative Capacitance Devices
  • Neuroscience and Neural Engineering
  • CCD and CMOS Imaging Sensors
  • Neural Networks and Applications
  • Quantum Computing Algorithms and Architecture
  • Neural Networks and Reservoir Computing

Frequent publication venues for their work are:

  • arXiv (Cornell University)
  • Nature Communications
  • Frontiers in Big Data
  • IEEE Transactions on Electron Devices
  • IEEE Transactions on Nanotechnology

Selected recent publications showcase the diversity of their research:

  • "Wafer-scale integration of two-dimensional materials in high-density memristive crossbar arrays for artificial neural networks", 2020, Nature Electronics
  • "Recent Advances and Future Prospects for Memristive Materials, Devices, and Systems", 2023, ACS Nano
  • "4K-memristor analog-grade passive crossbar circuit", 2021, Nature Communications
  • "Roadmap on emerging hardware and technology for machine learning", 2020, Nanotechnology
  • "Applications and Techniques for Fast Machine Learning in Science", 2022, Frontiers in Big Data

Dmitri B. Strukov has collaborated frequently with several co-authors, including:

  • Mohammad Reza Mahmoodi
  • John Paul Strachan
  • Hussein Nili
  • Tinish Bhattacharya
  • Giacomo Pedretti

In recognition of their contributions, they received the Hellman Fellow award in 2013.

Best Publications

  • The missing memristor found

    Dmitri B. Strukov;Gregory S. Snider;Duncan R. Stewart;R. Stanley Williams

  • Memristive devices for computing

    J. Joshua Yang;Dmitri B. Strukov;Duncan R. Stewart

  • Training and operation of an integrated neuromorphic network based on metal-oxide memristors

    Mirko Prezioso;Farnood Merrikh-Bayat;Brian Hoskins;Gina C. Adam

  • Switching dynamics in titanium dioxide memristive devices

    Matthew D. Pickett;Dmitri B. Strukov;Julien L. Borghetti;J. Joshua Yang

  • Memristor―CMOS Hybrid Integrated Circuits for Reconfigurable Logic

    Qiangfei Xia;Warren Robinett;Michael W. Cumbie;Neel Banerjee

  • High precision tuning of state for memristive devices by adaptable variation-tolerant algorithm

    Fabien Alibart;Ligang Gao;Brian D Hoskins;Dmitri B Strukov

  • Pattern classification by memristive crossbar circuits using ex situ and in situ training

    Fabien Alibart;Elham Zamanidoost;Dmitri B. Strukov

  • CMOL FPGA: a reconfigurable architecture for hybrid digital circuits with two-terminal nanodevices

    Dmitri B Strukov;Konstantin K Likharev

  • Exponential ionic drift: fast switching and low volatility of thin-film memristors

    Dmitri B. Strukov;R. Stanley Williams

  • Wafer-scale integration of two-dimensional materials in high-density memristive crossbar arrays for artificial neural networks

    Shaochuan Chen;Shaochuan Chen;Mohammad Reza Mahmoodi;Yuanyuan Shi;Chandreswar Mahata

  • Implementation of multilayer perceptron network with highly uniform passive memristive crossbar circuits.

    F. Merrikh Bayat;M. Prezioso;B. Chakrabarti;H. Nili

  • Coupled Ionic and Electronic Transport Model of Thin‐Film Semiconductor Memristive Behavior

    Dmitri B. Strukov;Julien L. Borghetti;R. Stanley Williams

  • Flexible three-dimensional artificial synapse networks with correlated learning and trainable memory capability.

    Chaoxing Wu;Tae Whan Kim;Hwan Young Choi;Dmitri B. Strukov

  • CMOL: Devices, Circuits, and Architectures

    Konstantin K. Likharev;Dmitri B. Strukov

  • 3-D Memristor Crossbars for Analog and Neuromorphic Computing Applications

    Gina C. Adam;Brian D. Hoskins;Mirko Prezioso;Farnood Merrikh-Bayat

  • Thermophoresis/diffusion as a plausible mechanism for unipolar resistive switching in metal–oxide–metal memristors

    Dmitri B. Strukov;Fabien Alibart;R. Stanley Williams

  • Resistive switching and its suppression in Pt/Nb:SrTiO3 junctions

    Evgeny Mikheev;Brian D. Hoskins;Dmitri B. Strukov;Susanne Stemmer

  • Programmable CMOS/Memristor Threshold Logic

    Ligang Gao;F. Alibart;D. B. Strukov

  • Spike-timing-dependent plasticity learning of coincidence detection with passively integrated memristive circuits

    M. Prezioso;M. R. Mahmoodi;F. Merrikh Bayat;H. Nili

  • 4K-memristor analog-grade passive crossbar circuit.

    H. Kim;H. Kim;M. R. Mahmoodi;H. Nili;D. B. Strukov

  • Fast, energy-efficient, robust, and reproducible mixed-signal neuromorphic classifier based on embedded NOR flash memory technology

    X. Guo;F. Merrikh Bayat;M. Bavandpour;M. Klachko

  • Resistive switching phenomena in thin films: Materials, devices, and applications

    D. B. Strukov;H. Kohlstedt

Frequent Co-Authors

Konstantin K. Likharev
Konstantin K. Likharev Stony Brook University
R. Stanley Williams
R. Stanley Williams Texas A&M University
J. Joshua Yang
J. Joshua Yang University of Southern California
Kwang-Ting Cheng
Kwang-Ting Cheng Hong Kong University of Science and Technology
Zhiyong Li
Zhiyong Li Chinese Academy of Sciences
Yunseok Kim
Yunseok Kim Sungkyunkwan University
Sharath Sriram
Sharath Sriram RMIT University
Madhu Bhaskaran
Madhu Bhaskaran RMIT University
Sergei V. Kalinin
Sergei V. Kalinin University of Tennessee at Knoxville
Sumeet Walia
Sumeet Walia RMIT University

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