World's Best Scientists 2026 revealed!

D-Index & Metrics

Electronics and Electrical Engineering

D-Index
71
Citations
24287
World Ranking
839
National Ranking
139

Overview

Huaqiang Wu is a researcher affiliated with Tsinghua University in China. Their work primarily focuses on the field of Engineering, with a strong emphasis on Electrical and Electronic Engineering.

Their research spans multiple specialized subfields, including:

  • Electrical and Electronic Engineering
  • Cellular and Molecular Neuroscience
  • Artificial Intelligence
  • Cognitive Neuroscience
  • Materials Chemistry

Within these areas, the main topics covered in their publications include:

  • Advanced Memory and Neural Computing
  • Ferroelectric and Negative Capacitance Devices
  • Neuroscience and Neural Engineering
  • Semiconductor materials and devices
  • CCD and CMOS Imaging Sensors
  • Neural Networks and Reservoir Computing
  • Neural dynamics and brain function

Huaqiang Wu has contributed to numerous publications in a variety of scientific venues. They have frequently published in the following journals and platforms:

  • IEEE Transactions on Electron Devices
  • Nature Communications
  • Nature Electronics
  • Zenodo (CERN European Organization for Nuclear Research)
  • arXiv (Cornell University)

Their recent papers illustrate a focus on memristor technology, memory devices, and neuromorphic computing. Selected recent publications include:

  • Fully hardware-implemented memristor convolutional neural network, 2020, Nature
  • Resistive switching materials for information processing, 2020, Nature Reviews Materials
  • Neuro-inspired computing chips, 2020, Nature Electronics
  • A compute-in-memory chip based on resistive random-access memory, 2022, Nature
  • Dynamic memristor-based reservoir computing for high-efficiency temporal signal processing, 2021, Nature Communications

They have collaborated extensively with a number of coauthors, including:

  • Bin Gao
  • Jianshi Tang
  • He Qian
  • Peng Yao
  • Qingtian Zhang

Best Publications

  • Fully hardware-implemented memristor convolutional neural network

    Peng Yao;Huaqiang Wu;Bin Gao;Jianshi Tang

  • Resistive switching materials for information processing

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

  • Fully memristive neural networks for pattern classification with unsupervised learning

    Zhongrui Wang;Saumil Joshi;Sergey Savel’ev;Wenhao Song

  • Large-scale neuromorphic optoelectronic computing with a reconfigurable diffractive processing unit

    Tiankuang Zhou;Xing Lin;Jiamin Wu;Yitong Chen

  • Towards artificial general intelligence with hybrid Tianjic chip architecture.

    Jing Pei;Lei Deng;Sen Song;Sen Song;Mingguo Zhao

  • Face classification using electronic synapses

    Peng Yao;Huaqiang Wu;Bin Gao;Sukru Burc Eryilmaz

  • Neuro-inspired computing chips

    Wenqiang Zhang;Bin Gao;Jianshi Tang;Peng Yao

  • Bridging Biological and Artificial Neural Networks with Emerging Neuromorphic Devices: Fundamentals, Progress, and Challenges.

    Jianshi Tang;Fang Yuan;Xinke Shen;Zhongrui Wang

  • Recommended Methods to Study Resistive Switching Devices

    Mario Lanza;H.-S. Philip Wong;Eric Pop;Daniele Ielmini

  • Dynamic memristor-based reservoir computing for high-efficiency temporal signal processing.

    Yanan Zhong;Jianshi Tang;Xinyi Li;Bin Gao

  • An artificial nociceptor based on a diffusive memristor.

    Jung Ho Yoon;Zhongrui Wang;Kyung Min Kim;Huaqiang Wu

  • Understanding memristive switching via in situ characterization and device modeling.

    Wen Sun;Wen Sun;Wen Sun;Bin Gao;Miaofang Chi;Qiangfei Xia

  • Threshold Switching of Ag or Cu in Dielectrics: Materials, Mechanism, and Applications

    Zhongrui Wang;Mingyi Rao;Rivu Midya;Saumil Joshi

  • Reliability of analog resistive switching memory for neuromorphic computing

    Meiran Zhao;Bin Gao;Jianshi Tang;He Qian

  • 33.2 A Fully Integrated Analog ReRAM Based 78.4TOPS/W Compute-In-Memory Chip with Fully Parallel MAC Computing

    Qi Liu;Bin Gao;Peng Yao;Dong Wu

  • Graphene Oxide Quantum Dots Based Memristors with Progressive Conduction Tuning for Artificial Synaptic Learning

    Xiaobing Yan;Xiaobing Yan;Lei Zhang;Huawei Chen;Xiaoyan Li

  • In situ training of feed-forward and recurrent convolutional memristor networks

    Zhongrui Wang;Can Li;Can Li;Peng Lin;Mingyi Rao

  • Capacitive neural network with neuro-transistors.

    Zhongrui Wang;Mingyi Rao;Jin Woo Han;Jiaming Zhang

  • Improving Analog Switching in HfO x -Based Resistive Memory With a Thermal Enhanced Layer

    Wei Wu;Huaqiang Wu;Bin Gao;Ning Deng

  • Synthesis and characterization of vertically standing MoS2 nanosheets.

    Han Li;Huaqiang Wu;Shuoguo Yuan;He Qian

  • Alloying conducting channels for reliable neuromorphic computing

    Hanwool Yeon;Peng Lin;Chanyeol Choi;Scott H. Tan

  • Memory materials and devices: From concept to application

    Zhenhan Zhang;Zongwei Wang;Tuo Shi;Chong Bi

  • Power-efficient neural network with artificial dendrites.

    Xinyi Li;Jianshi Tang;Qingtian Zhang;Bin Gao

Frequent Co-Authors

He Qian
He Qian Tsinghua University
Bin Gao
Bin Gao Xi'an Jiaotong University
Jianshi Tang
Jianshi Tang Tsinghua University
Shimeng Yu
Shimeng Yu Georgia Institute of Technology
Michael G. Spencer
Michael G. Spencer Cornell University
J. Joshua Yang
J. Joshua Yang University of Southern California
Feng Xu
Feng Xu Xi'an Jiaotong University
Jinfeng Kang
Jinfeng Kang Peking University
Qiangfei Xia
Qiangfei Xia University of Massachusetts Amherst
Meng-Fan Chang
Meng-Fan Chang National Tsing Hua University

If you think any of the details on this page are incorrect, let us know.

Report an issue

We appreciate your kind effort to assist us to improve this page, it would be helpful providing us with as much detail as possible in the text box below:

Related Online Degrees & Career Pathways

For those studying Electronics and Electrical Engineering in the USA, exploring related online degrees can open diverse career pathways. Many students consider short certificate programs that pay well as an efficient way to gain specialized skills quickly, enhancing job prospects without committing to long-term study.

Careers in this field often attract introverts who prefer focused and analytical work environments. If you identify as one, check out jobs for introverts that pay well to find roles that match your personality and strengths while offering competitive salaries.

Project management is another complementary skill set relevant to engineering professionals. Pursuing an online accelerated project management degree programs can help you develop leadership abilities and manage complex engineering projects efficiently.

For a more foundational approach, enrolling in a bachelor project management degree program online provides comprehensive knowledge and credentials that boost your career flexibility across engineering and management domains.

Best Scientists Citing Huaqiang Wu

Trending Scientists