World's Best Scientists 2026 revealed!
Award Badge
Electronics and Electrical Engineering
Taiwan
2026

D-Index & Metrics

Electronics and Electrical Engineering

D-Index
69
Citations
14734
World Ranking
979
National Ranking
10

Meng-Fan Chang 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 Meng-Fan Chang 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: 327 publications — 63rd percentile

63% of scientists in this discipline score the same or lower.

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

Meng-Fan Chang 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 Meng-Fan Chang 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: 69 D-Index — 86th percentile

86% 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

  • 2026 - Research.com Electronics and Electrical Engineering in Taiwan Leader Award

Overview

Meng-Fan Chang is affiliated with National Tsing Hua University in Taiwan. Their research primarily focuses on engineering, with a strong emphasis on electrical and electronic engineering. The scope of their work covers several specialized subfields, including hardware and architecture, artificial intelligence, cellular and molecular neuroscience, and computer vision and pattern recognition.

The scientist's research topics encompass advanced memory and neural computing, ferroelectric and negative capacitance devices, semiconductor materials and devices, neuroscience and neural engineering, CCD and CMOS imaging sensors, advanced neural network applications, and parallel computing and optimization techniques.

Meng-Fan Chang has contributed to multiple significant publications. Notable papers include:

  • "Neuro-inspired computing chips" (2020, Nature Electronics)
  • "Memristive technologies for data storage, computation, encryption, and radio-frequency communication" (2022, Science)
  • "Hardware implementation of memristor-based artificial neural networks" (2024, Nature Communications)
  • "Challenges and Trends of SRAM-Based Computing-In-Memory for AI Edge Devices" (2021, IEEE Transactions on Circuits and Systems I Regular Papers)
  • "In-memory Learning with Analog Resistive Switching Memory: A Review and Perspective" (2020, Proceedings of the IEEE)

The scientist frequently collaborates with a network of coauthors including Kea-Tiong Tang, Win-San Khwa, Chung-Chuan Lo, Chih-Cheng Hsieh, and Ren-Shuo Liu.

Meng-Fan Chang's work appears predominantly in venues such as the IEEE Journal of Solid-State Circuits, the IEEE International Solid-State Circuits Conference (ISSCC), Nature Electronics, the IEEE International Electron Devices Meeting (IEDM), and IEEE Transactions on Electron Devices. Specifically, they have published extensively with 30 papers in the IEEE Journal of Solid-State Circuits and 8 at the 2022 IEEE ISSCC.

Best Publications

  • Neuro-inspired computing chips

    Wenqiang Zhang;Bin Gao;Jianshi Tang;Peng Yao

  • Small-Subthreshold-Swing and Low-Voltage Flexible Organic Thin-Film Transistors Which Use HfLaO as the Gate Dielectric

    M.F. Chang;P.T. Lee;S.P. McAlister;A. Chin

  • A 4Mb embedded SLC resistive-RAM macro with 7.2ns read-write random-access time and 160ns MLC-access capability

    Shyh-Shyuan Sheu;Meng-Fan Chang;Ku-Feng Lin;Che-Wei Wu

  • A 65nm 1Mb nonvolatile computing-in-memory ReRAM macro with sub-16ns multiply-and-accumulate for binary DNN AI edge processors

    Wei-Hao Chen;Kai-Xiang Li;Wei-Yu Lin;Kuo-Hsiang Hsu

  • 24.1 A 1Mb Multibit ReRAM Computing-In-Memory Macro with 14.6ns Parallel MAC Computing Time for CNN Based AI Edge Processors

    Cheng-Xin Xue;Wei-Hao Chen;Je-Syu Liu;Jia-Fang Li

  • An 89TOPS/W and 16.3TOPS/mm 2 All-Digital SRAM-Based Full-Precision Compute-In Memory Macro in 22nm for Machine-Learning Edge Applications

    Yu-Der Chih;Po-Hao Lee;Hidehiro Fujiwara;Yi-Chun Shih

  • 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

  • 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

  • 24.5 A Twin-8T SRAM Computation-In-Memory Macro for Multiple-Bit CNN-Based Machine Learning

    Xin Si;Jia-Jing Chen;Yung-Ning Tu;Wei-Hsing Huang

  • CMOS-integrated memristive non-volatile computing-in-memory for AI edge processors

    Wei-Hao Chen;Chunmeng Dou;Kai-Xiang Li;Wei-Yu Lin

  • Challenges and Trends of SRAM-Based Computing-In-Memory for AI Edge Devices

    Chuan-Jia Jhang;Cheng-Xin Xue;Je-Min Hung;Fu-Chun Chang

  • 15.4 A 22nm 2Mb ReRAM Compute-in-Memory Macro with 121-28TOPS/W for Multibit MAC Computing for Tiny AI Edge Devices

    Cheng-Xin Xue;Tsung-Yuan Huang;Je-Syu Liu;Ting-Wei Chang

  • A Twin-8T SRAM Computation-in-Memory Unit-Macro for Multibit CNN-Based AI Edge Processors

    Xin Si;Rui Liu;Shimeng Yu;Ren-Shuo Liu

  • Low Store Energy, Low VDDmin, 8T2R Nonvolatile Latch and SRAM With Vertical-Stacked Resistive Memory (Memristor) Devices for Low Power Mobile Applications

    Pi-Feng Chiu;Meng-Fan Chang;Che-Wei Wu;Ching-Hao Chuang

  • 15.5 A 28nm 64Kb 6T SRAM Computing-in-Memory Macro with 8b MAC Operation for AI Edge Chips

    Xin Si;Yung-Ning Tu;Wei-Hsing Huanq;Jian-Wei Su

  • In-memory Learning with Analog Resistive Switching Memory: A Review and Perspective

    Yue Xi;Bin Gao;Jianshi Tang;An Chen

  • 16.3 A 28nm 384kb 6T-SRAM Computation-in-Memory Macro with 8b Precision for AI Edge Chips

    Jian-Wei Su;Yen-Chi Chou;Ruhui Liu;Ta-Wei Liu

  • A 130 mV SRAM With Expanded Write and Read Margins for Subthreshold Applications

    Meng-Fan Chang;Shi-Wei Chang;Po-Wei Chou;Wei-Cheng Wu

  • A 22nm 4Mb 8b-Precision ReRAM Computing-in-Memory Macro with 11.91 to 195.7TOPS/W for Tiny AI Edge Devices

    Cheng-Xin Xue;Je-Min Hung;Hui-Yao Kao;Yen-Hsiang Huang

  • 19.4 embedded 1Mb ReRAM in 28nm CMOS with 0.27-to-1V read using swing-sample-and-couple sense amplifier and self-boost-write-termination scheme

    Meng-Fan Chang;Jui-Jen Wu;Tun-Fei Chien;Yen-Chen Liu

  • 15.2 A 28nm 64Kb Inference-Training Two-Way Transpose Multibit 6T SRAM Compute-in-Memory Macro for AI Edge Chips

    Jian-Wei Su;Xin Si;Yen-Chi Chou;Ting-Wei Chang

  • Ambient energy harvesting nonvolatile processors: from circuit to system

    Yongpan Liu;Zewei Li;Hehe Li;Yiqun Wang

  • Fast-Write Resistive RAM (RRAM) for Embedded Applications

    Shyh-Shyuan Sheu;Kuo-Hsing Cheng;Meng-Fan Chang;Pei-Chia Chiang

Frequent Co-Authors

Kea-Tiong Tang
Kea-Tiong Tang National Tsing Hua University
Chih-Cheng Hsieh
Chih-Cheng Hsieh National Tsing Hua University
Ya-Chin King
Ya-Chin King National Tsing Hua University
Yongpan Liu
Yongpan Liu Tsinghua University
Huazhong Yang
Huazhong Yang Tsinghua University
Frederick T. Chen
Frederick T. Chen ITRI International
Ming-Jinn Tsai
Ming-Jinn Tsai Industrial Technology Research Institute
Shimeng Yu
Shimeng Yu Georgia Institute of Technology
Sumeet Kumar Gupta
Sumeet Kumar Gupta Purdue University West Lafayette
Huaqiang Wu
Huaqiang Wu Tsinghua 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 interested in Electronics and Electrical Engineering, pursuing competency based programs offers a flexible and skills-focused education model. These programs emphasize mastering practical skills over traditional credit hours, making them ideal for learners who want to progress at their own pace.

Military spouses and dependents often face unique challenges when pursuing higher education. Thankfully, many online colleges for military spouses provide tailored support systems, flexible scheduling, and affordability to help them succeed in their academic and career goals.

Flexibility is key for working professionals or those balancing other commitments. Online colleges with flexible start dates allow students to begin courses throughout the year, reducing wait times and accommodating varied schedules for a more convenient learning experience.

Additionally, for individuals seeking to quickly gain relevant skills and enter the workforce, short certificate programs that pay well online offer accelerated pathways to certification and higher earning potential within months, making them an attractive option for timely career advancement.

Best Scientists Citing Meng-Fan Chang

Trending Scientists

Recently Published Articles