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Rising Stars
2025

D-Index & Metrics

Rising Stars

D-Index
33
Citations
5792
World Ranking
914
National Ranking
149

Electronics and Electrical Engineering

D-Index
31
Citations
6165
World Ranking
6461
National Ranking
2117

Pai-Yu Chen publication distribution in Electronics and Electrical Engineering in 2026

The chart shows the distribution of publications by all Research.com ranked scientists in the field of Electronics and Electrical Engineering in 2026. The highlighted bar marks where Pai-Yu Chen sits on this spectrum.

34–53 publications: 24 scientists 54–73 publications: 52 scientists 74–93 publications: 114 scientists 94–113 publications: 203 scientists 114–133 publications: 269 scientists 134–153 publications: 355 scientists 154–173 publications: 403 scientists 174–193 publications: 445 scientists 194–213 publications: 430 scientists 214–233 publications: 431 scientists 234–253 publications: 399 scientists 254–273 publications: 366 scientists 274–293 publications: 335 scientists 294–313 publications: 300 scientists 314–333 publications: 276 scientists 334–353 publications: 250 scientists 354–373 publications: 214 scientists 374–393 publications: 187 scientists 394–413 publications: 152 scientists 414–433 publications: 169 scientists 434–453 publications: 147 scientists 454–473 publications: 111 scientists 474–493 publications: 117 scientists 494–513 publications: 103 scientists 514–533 publications: 99 scientists 534–553 publications: 92 scientists 554–573 publications: 75 scientists 574–593 publications: 58 scientists 594–613 publications: 69 scientists 614–633 publications: 50 scientists 634–653 publications: 62 scientists 654–673 publications: 54 scientists 674–693 publications: 44 scientists 694–713 publications: 37 scientists 714–733 publications: 28 scientists 734–753 publications: 26 scientists 754–773 publications: 26 scientists 774–793 publications: 19 scientists 794–813 publications: 23 scientists 814–833 publications: 20 scientists 834–853 publications: 16 scientists 854–873 publications: 20 scientists 874–893 publications: 11 scientists 894–913 publications: 11 scientists 914–933 publications: 16 scientists 934–953 publications: 13 scientists 954–973 publications: 10 scientists 974–993 publications: 11 scientists 994–1,013 publications: 9 scientists 1,014–1,033 publications: 9 scientists 1,034–1,053 publications: 10 scientists 1,054–1,064 publications: 6 scientists 1,065+ publications: 99 scientists
34 publications 1,065+

This scientist: 57 publications — 1st percentile

1% of scientists in this discipline score the same or lower.

The last bar groups every scientist with 1,065 publications or more.

Pai-Yu Chen D-index placement in Electronics and Electrical Engineering in 2026

The chart shows the D-index (discipline H-index) distribution of Electronics and Electrical Engineering scientists ranked by Research.com in 2026. The highlighted bar marks where Pai-Yu Chen sits on this spectrum.

30 D-Index: 178 scientists 31 D-Index: 257 scientists 32 D-Index: 263 scientists 33 D-Index: 262 scientists 34 D-Index: 244 scientists 35 D-Index: 236 scientists 36 D-Index: 211 scientists 37 D-Index: 220 scientists 38 D-Index: 214 scientists 39 D-Index: 214 scientists 40 D-Index: 205 scientists 41 D-Index: 187 scientists 42 D-Index: 194 scientists 43 D-Index: 201 scientists 44 D-Index: 155 scientists 45 D-Index: 189 scientists 46 D-Index: 148 scientists 47 D-Index: 160 scientists 48 D-Index: 134 scientists 49 D-Index: 130 scientists 50 D-Index: 141 scientists 51 D-Index: 156 scientists 52 D-Index: 108 scientists 53 D-Index: 130 scientists 54 D-Index: 112 scientists 55 D-Index: 97 scientists 56 D-Index: 111 scientists 57 D-Index: 102 scientists 58 D-Index: 108 scientists 59 D-Index: 120 scientists 60 D-Index: 103 scientists 61 D-Index: 93 scientists 62 D-Index: 92 scientists 63 D-Index: 74 scientists 64 D-Index: 77 scientists 65 D-Index: 73 scientists 66 D-Index: 64 scientists 67 D-Index: 69 scientists 68 D-Index: 60 scientists 69 D-Index: 39 scientists 70 D-Index: 57 scientists 71 D-Index: 59 scientists 72 D-Index: 46 scientists 73 D-Index: 49 scientists 74 D-Index: 38 scientists 75 D-Index: 35 scientists 76 D-Index: 32 scientists 77 D-Index: 35 scientists 78 D-Index: 31 scientists 79 D-Index: 22 scientists 80 D-Index: 34 scientists 81 D-Index: 31 scientists 82 D-Index: 34 scientists 83 D-Index: 23 scientists 84 D-Index: 18 scientists 85 D-Index: 30 scientists 86 D-Index: 19 scientists 87 D-Index: 19 scientists 88 D-Index: 20 scientists 89 D-Index: 8 scientists 90 D-Index: 17 scientists 91 D-Index: 7 scientists 92 D-Index: 14 scientists 93 D-Index: 9 scientists 94 D-Index: 15 scientists 95 D-Index: 10 scientists 96 D-Index: 12 scientists 97 D-Index: 10 scientists 98 D-Index: 10 scientists 99 D-Index: 12 scientists 100 D-Index: 16 scientists 101 D-Index: 5 scientists 102 D-Index: 7 scientists 103 D-Index: 7 scientists 104 D-Index: 8 scientists 105 D-Index: 9 scientists 106 D-Index: 13 scientists 107 D-Index: 4 scientists 108 D-Index: 5 scientists 109 D-Index: 10 scientists 110 D-Index: 8 scientists 111+ D-Index: 96 scientists
30 D-Index 111+

This scientist: 31 D-Index — 6th percentile

6% of scientists in this discipline score the same or lower.

The last bar groups every scientist with 111 D-Index or more.

Research.com Recognitions

  • 2025 - Research.com Rising Stars Award

Overview

Pai-Yu Chen is affiliated with Arizona State University in the United States. Their academic profile currently does not list specific publications, co-authors, or detailed research topics. Despite the absence of publicly noted recent papers or book publications, their connection with a major research institution suggests engagement in scholarly activities.

Currently, there are no listed frequent publication venues associated with Pai-Yu Chen, indicating that detailed information about favored journals or conferences is not available at this time.

Similarly, there is no data on the primary or subfields of study. This lack of explicit categorization may reflect a broad or emerging research focus or an incomplete public record of their academic output.

Their profile does not include information on main research topics, which implies that specific areas of expertise or thematic concentration have not been documented in the available data.

There are no recorded awards or honors for Pai-Yu Chen in the provided information, which may suggest either an early stage in their career or that such recognitions are not publicly documented.

Best Publications

  • 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

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

    Pai-Yu Chen;Xiaochen Peng;Shimeng Yu

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

    Pai-Yu Chen;Shimeng Yu

  • A 65nm 4Kb algorithm-dependent computing-in-memory SRAM unit-macro with 2.3ns and 55.8TOPS/W fully parallel product-sum operation for binary DNN edge processors

    Win-San Khwa;Jia-Jing Chen;Jia-Fang Li;Xin Si

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

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

  • Scaling-up resistive synaptic arrays for neuro-inspired architecture: Challenges and prospect

    Shimeng Yu;Pai-Yu Chen;Yu Cao;Lixue Xia

  • MNSIM: Simulation Platform for Memristor-Based Neuromorphic Computing System

    Lixue Xia;Boxun Li;Tianqi Tang;Peng Gu

  • Demonstration of Convolution Kernel Operation on Resistive Cross-Point Array

    Ligang Gao;Pai-Yu Chen;Shimeng Yu

  • NbOx based oscillation neuron for neuromorphic computing

    Ligang Gao;Pai Yu Chen;Shimeng Yu

  • Fully parallel write/read in resistive synaptic array for accelerating on-chip learning.

    Ligang Gao;I-Ting Wang;Pai-Yu Chen;Sarma Vrudhula

  • Mitigating Effects of Non-ideal Synaptic Device Characteristics for On-chip Learning

    Pai-Yu Chen;Binbin Lin;I-Ting Wang;Tuo-Hung Hou

  • Device and materials requirements for neuromorphic computing

    Raisul Islam;Haitong Li;Pai-Yu Chen;Weier Wan

  • A ferroelectric field effect transistor based synaptic weight cell

    Matthew Jerry;Sourav Dutta;Arman Kazemi;Kai Ni

  • MNSIM: Simulation platform for memristor-based neuromorphic computing system

    Lixue Xia;Boxun Li;Tianqi Tang;Peng Gu

  • Programming Protocol Optimization for Analog Weight Tuning in Resistive Memories

    Ligang Gao;Pai-Yu Chen;Shimeng Yu

  • Understanding the resistive switching characteristics and mechanism in active SiOx-based resistive switching memory

    Yao-Feng Chang;Pai-Yu Chen;Burt Fowler;Yen-Ting Chen

  • Technology-design co-optimization of resistive cross-point array for accelerating learning algorithms on chip

    Pai-Yu Chen;Deepak Kadetotad;Zihan Xu;Abinash Mohanty

  • Physical Unclonable Function Exploiting Sneak Paths in Resistive Cross-point Array

    Ligang Gao;Pai-Yu Chen;Rui Liu;Shimeng Yu

Frequent Co-Authors

Shimeng Yu
Shimeng Yu Georgia Institute of Technology
Yu Cao
Yu Cao University of Minnesota
Jae-sun Seo
Jae-sun Seo Cornell University
Sarma Vrudhula
Sarma Vrudhula Arizona State University
Yao-Feng Chang
Yao-Feng Chang The University of Texas at Austin
Jack C. Lee
Jack C. Lee The University of Texas at Austin
Yuan Xie
Yuan Xie Hong Kong University of Science and Technology
Jieping Ye
Jieping Ye Alibaba Group (China)
Huaqiang Wu
Huaqiang Wu Tsinghua University
Suman Datta
Suman Datta Georgia Institute of Technology

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Related Online Degrees & Career Pathways

For professionals pursuing Electronics and Electrical Engineering, exploring related online degree programs can offer greater flexibility and career growth. Many working adults benefit from accelerated online degree programs for working adults, which allow faster completion without compromising quality.

Expanding skills beyond engineering, such as obtaining a master's in instructional design, provides opportunities in educational technology and training roles within engineering companies and organizations.

Competency-based education is increasingly popular, focusing on practical skills and mastery rather than time spent in class. This approach, offered by many competency based universities, is ideal for self-motivated learners eager to advance their careers efficiently.

Additionally, specialized programs at online colleges for military spouses provide accessible education options for those connected to the armed forces, facilitating career flexibility and support.

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