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D-Index & Metrics

Electronics and Electrical Engineering

D-Index
81
Citations
31726
World Ranking
480
National Ranking
219

Overview

Shimeng Yu is affiliated with the Georgia Institute of Technology in the United States. Their research primarily focuses on engineering, with a specialization in electrical and electronic engineering. They have contributed extensively to the study and development of semiconductor materials and devices, advanced memory technologies, and neural computing systems.

The scientist's work covers several subfields, including materials chemistry, artificial intelligence, cellular and molecular neuroscience, and computer vision and pattern recognition. Their main research topics are:

  • Ferroelectric and Negative Capacitance Devices
  • Advanced Memory and Neural Computing
  • Semiconductor materials and devices
  • Ferroelectric and Piezoelectric Materials
  • Advancements in Semiconductor Devices and Circuit Design
  • Neuroscience and Neural Engineering
  • MXene and MAX Phase Materials

Shimeng Yu has published in a range of specialized venues. Frequent publication outlets include:

  • IEEE Transactions on Electron Devices
  • arXiv (Cornell University)
  • IEEE Electron Device Letters
  • IEEE Journal on Exploratory Solid-State Computational Devices and Circuits
  • IEEE Transactions on Very Large Scale Integration (VLSI) Systems

The scientist collaborates regularly with a set of frequent coauthors. The most common collaborators are:

  • Asif Islam Khan
  • Suman Datta
  • Jae Hur
  • Yuan-Chun Luo
  • Anni Lu

Recent papers by Shimeng Yu illustrate a focus on neuromorphic computing, in-memory computing, and compute-in-memory architectures for machine learning. Notable publications include:

  • "Neuro-inspired computing chips," 2020, Nature Electronics
  • "Power-efficient combinatorial optimization using intrinsic noise in memristor Hopfield neural networks," 2020, Nature Electronics
  • "Compute-in-Memory Chips for Deep Learning: Recent Trends and Prospects," 2021, IEEE Circuits and Systems Magazine
  • "DNN+NeuroSim V2.0: An End-to-End Benchmarking Framework for Compute-in-Memory Accelerators for On-Chip Training," 2020, IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems
  • "High-Throughput In-Memory Computing for Binary Deep Neural Networks With Monolithically Integrated RRAM and 90-nm CMOS," 2020, IEEE Transactions on Electron Devices

Best Publications

  • Metal–Oxide RRAM

    H-S P. Wong;Heng-Yuan Lee;Shimeng Yu;Yu-Sheng Chen

  • Synaptic electronics: materials, devices and applications

    Duygu Kuzum;Duygu Kuzum;Shimeng Yu;Shimeng Yu;H-S Philip Wong

  • Optoelectronic resistive random access memory for neuromorphic vision sensors.

    Feichi Zhou;Zheng Zhou;Jiewei Chen;Tsz Hin Choy

  • Neuro-Inspired Computing With Emerging Nonvolatile Memorys

    Shimeng Yu

  • An Electronic Synapse Device Based on Metal Oxide Resistive Switching Memory for Neuromorphic Computation

    Shimeng Yu;Yi Wu;R. Jeyasingh;D. Kuzum

  • Neuro-inspired computing chips

    Wenqiang Zhang;Bin Gao;Jianshi Tang;Peng Yao

  • SiGe epitaxial memory for neuromorphic computing with reproducible high performance based on engineered dislocations

    Shinhyun Choi;Scott H. Tan;Zefan Li;Yunjo Kim

  • NeuroSim: A Circuit-Level Macro Model for Benchmarking Neuro-Inspired Architectures in Online Learning

    Pai-Yu Chen;Xiaochen Peng;Shimeng Yu

  • Emerging Memory Technologies: Recent Trends and Prospects

    Shimeng Yu;Pai-Yu Chen

  • Ferroelectric FET analog synapse for acceleration of deep neural network training

    Matthew Jerry;Pai-Yu Chen;Jianchi Zhang;Pankaj Sharma

  • 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

  • Conduction mechanism of TiN/HfOx/Pt resistive switching memory: A trap-assisted-tunneling model

    Shimeng Yu;Ximeng Guan;H.-S. Philip Wong

  • NeuroSim+: An integrated device-to-algorithm framework for benchmarking synaptic devices and array architectures

    Pai-Yu Chen;Xiaochen Peng;Shimeng Yu

  • 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

  • On the Switching Parameter Variation of Metal-Oxide RRAM—Part I: Physical Modeling and Simulation Methodology

    Ximeng Guan;Shimeng Yu;H.-S Philip Wong

  • Power-efficient combinatorial optimization using intrinsic noise in memristor Hopfield neural networks

    Fuxi Cai;Fuxi Cai;Suhas Kumar;Thomas Van Vaerenbergh;Xia Sheng

  • Overcoming the challenges of crossbar resistive memory architectures

    Cong Xu;Dimin Niu;Naveen Muralimanohar;Rajeev Balasubramonian

  • Investigating the switching dynamics and multilevel capability of bipolar metal oxide resistive switching memory

    Shimeng Yu;Yi Wu;H.-S. Philip Wong

  • Compact Modeling of RRAM Devices and Its Applications in 1T1R and 1S1R Array Design

    Pai-Yu Chen;Shimeng Yu

  • DNN+NeuroSim: An End-to-End Benchmarking Framework for Compute-in-Memory Accelerators with Versatile Device Technologies

    Xiaochen Peng;Shanshi Huang;Yandong Luo;Xiaoyu Sun

  • 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

  • A SPICE Compact Model of Metal Oxide Resistive Switching Memory With Variations

    Ximeng Guan;Shimeng Yu;H-S P. Wong

Frequent Co-Authors

H.-S. Philip Wong
H.-S. Philip Wong Stanford University
Pai-Yu Chen
Pai-Yu Chen Arizona State University
Bin Gao
Bin Gao Xi'an Jiaotong University
Jinfeng Kang
Jinfeng Kang Peking University
Yu Cao
Yu Cao University of Minnesota
Huaqiang Wu
Huaqiang Wu Tsinghua University
Sarma Vrudhula
Sarma Vrudhula Arizona State University
He Qian
He Qian Tsinghua University
Asif Islam Khan
Asif Islam Khan Georgia Institute of Technology
Hugh J. Barnaby
Hugh J. Barnaby Arizona State University

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