World's Best Scientists 2026 revealed!

D-Index & Metrics

Electronics and Electrical Engineering

D-Index
71
Citations
21644
World Ranking
851
National Ranking
142

Overview

Bin Gao is affiliated with Xi'an Jiaotong University in China and specializes primarily in the field of Engineering with a strong focus on Electrical and Electronic Engineering. Their work spans across several related subfields including Cellular and Molecular Neuroscience, Artificial Intelligence, Cognitive Neuroscience, and Materials Chemistry.

Their research topics prominently 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 Dynamics and Brain Function, and Neural Networks and Reservoir Computing.

Bin Gao has published extensively, with frequent contributions to specific scientific venues. These include:

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

The scientist's recent papers reflect topics at the intersection of hardware implementation and neural computing:

  • Fully hardware-implemented memristor convolutional neural network (2020, Nature)
  • 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)
  • Reliability of analog resistive switching memory for neuromorphic computing (2020, Applied Physics Reviews)

Bin Gao collaborates frequently with several coauthors including Huaqiang Wu, Jianshi Tang, He Qian, Peng Yao, and Qingtian Zhang. These consistent collaborations suggest a network of research activity centered on electronic and neural engineering.

Best Publications

  • Fully hardware-implemented memristor convolutional neural network

    Peng Yao;Huaqiang Wu;Bin Gao;Jianshi Tang

  • 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

  • A Low Energy Oxide‐Based Electronic Synaptic Device for Neuromorphic Visual Systems with Tolerance to Device Variation

    Shimeng Yu;Bin Gao;Zheng Fang;Hongyu Yu

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

    Yanan Zhong;Jianshi Tang;Xinyi Li;Bin Gao

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

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

  • HfOx-based vertical resistive switching random access memory suitable for bit-cost-effective three-dimensional cross-point architecture.

    Shimeng Yu;Hong Yu Chen;Bin Gao;Jinfeng Kang

  • Reliability of analog resistive switching memory for neuromorphic computing

    Meiran Zhao;Bin Gao;Jianshi Tang;He Qian

  • HfOx based vertical resistive random access memory for cost-effective 3D cross-point architecture without cell selector

    Hong-Yu Chen;Shimeng Yu;Bin Gao;Peng Huang

  • 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

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

    Wei Wu;Huaqiang Wu;Bin Gao;Ning Deng

  • Alloying conducting channels for reliable neuromorphic computing

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

  • Power-efficient neural network with artificial dendrites.

    Xinyi Li;Jianshi Tang;Qingtian Zhang;Bin Gao

  • Ionic doping effect in ZrO2 resistive switching memory

    Haowei Zhang;Bin Gao;Bing Sun;Guopeng Chen

  • Binary neural network with 16 Mb RRAM macro chip for classification and online training

    Shimeng Yu;Zhiwei Li;Pai-Yu Chen;Huaqiang Wu

  • A neuromorphic visual system using RRAM synaptic devices with Sub-pJ energy and tolerance to variability: Experimental characterization and large-scale modeling

    Shimeng Yu;Bin Gao;Zheng Fang;Hongyu Yu

  • A Physics-Based Compact Model of Metal-Oxide-Based RRAM DC and AC Operations

    Peng Huang;Xiao Yan Liu;Bing Chen;Hai Tong Li

  • Unified Physical Model of Bipolar Oxide-Based Resistive Switching Memory

    Bin Gao;Bing Sun;Haowei Zhang;Lifeng Liu

  • The new generation of soft and wearable electronics for health monitoring in varying environment: From normal to extreme conditions

    Yan Niu;Hao Liu;Rongyan He;Zedong Li

  • Ultra-Low-Energy Three-Dimensional Oxide-Based Electronic Synapses for Implementation of Robust High-Accuracy Neuromorphic Computation Systems

    Bin Gao;Yingjie Bi;Hong Yu Chen;Rui Liu

  • Gd-doping effect on performance of HfO2 based resistive switching memory devices using implantation approach

    Haowei Zhang;Lifeng Liu;Bin Gao;Yuanjun Qiu

Frequent Co-Authors

Huaqiang Wu
Huaqiang Wu Tsinghua University
Jinfeng Kang
Jinfeng Kang Peking University
He Qian
He Qian Tsinghua University
Shimeng Yu
Shimeng Yu Georgia Institute of Technology
H.-S. Philip Wong
H.-S. Philip Wong Stanford University
Jianshi Tang
Jianshi Tang Tsinghua University
Meng-Fan Chang
Meng-Fan Chang National Tsing Hua University
Dim-Lee Kwong
Dim-Lee Kwong National University of Singapore
J. Joshua Yang
J. Joshua Yang University of Southern California
Guo-Qiang Lo
Guo-Qiang Lo Global Foundries

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