World's Best Scientists 2026 revealed!

D-Index & Metrics

Electronics and Electrical Engineering

D-Index
41
Citations
7062
World Ranking
4289
National Ranking
70

Overview

Kea-Tiong Tang is affiliated with National Tsing Hua University in Taiwan and has contributed extensively in the field of engineering, with a particular emphasis on electrical and electronic engineering. Their research encompasses diverse subfields including biomedical engineering, cellular and molecular neuroscience, artificial intelligence, and computer vision and pattern recognition.

Their work focuses on topics such as advanced memory and neural computing, ferroelectric and negative capacitance devices, advanced chemical sensor technologies, semiconductor materials and devices, CCD and CMOS imaging sensors, neuroscience and neural engineering, and gas sensing nanomaterials and sensors.

They have a substantial publication record with papers appearing frequently in prominent venues. Key publication venues include:

  • IEEE Journal of Solid-State Circuits
  • 2022 IEEE International Solid-State Circuits Conference (ISSCC)
  • IEEE Transactions on Very Large Scale Integration (VLSI) Systems
  • IEEE Transactions on Biomedical Circuits and Systems
  • IEEE Transactions on Circuits & Systems II Express Briefs

Frequent collaborators in their research include Meng-Fan Chang, Chih-Cheng Hsieh, Chung-Chuan Lo, Ren-Shuo Liu, and Meysam Akbari.

Selected recent publications highlight key advancements in compute-in-memory technologies and AI edge devices:

  • "A CMOS-integrated compute-in-memory macro based on resistive random-access memory for AI edge devices", 2020, Nature Electronics
  • "A Local Computing Cell and 6T SRAM-Based Computing-in-Memory Macro With 8-b MAC Operation for Edge AI Chips", 2021, IEEE Journal of Solid-State Circuits
  • "A 28nm 1Mb Time-Domain Computing-in-Memory 6T-SRAM Macro with a 6.6ns Latency, 1241GOPS and 37.01TOPS/W for 8b-MAC Operations for Edge-AI Devices", 2022, 2022 IEEE International Solid-State Circuits Conference (ISSCC)
  • "An 8-Mb DC-Current-Free Binary-to-8b Precision ReRAM Nonvolatile Computing-in-Memory Macro using Time-Space-Readout with 1286.4-21.6TOPS/W for Edge-AI Devices", 2022, 2022 IEEE International Solid-State Circuits Conference (ISSCC)
  • "A four-megabit compute-in-memory macro with eight-bit precision based on CMOS and resistive random-access memory for AI edge devices", 2021, Nature Electronics

Best Publications

  • A review of sensor-based methods for monitoring hydrogen sulfide

    Sudhir Kumar Pandey;Ki-Hyun Kim;Kea-Tiong Tang

  • 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

  • 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

  • Towards a Chemiresistive Sensor-Integrated Electronic Nose: A Review

    Shih-Wen Chiu;Kea-Tiong Tang

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

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

  • 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

  • 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

  • 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 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

  • 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

  • A CMOS-integrated compute-in-memory macro based on resistive random-access memory for AI edge devices

    Cheng-Xin Xue;Yen-Cheng Chiu;Ta-Wei Liu;Tsung-Yuan Huang

  • Development of a Portable Electronic Nose System for the Detection and Classification of Fruity Odors

    Kea-Tiong Tang;Shih-Wen Chiu;Chih-Heng Pan;Hung-Yi Hsieh

  • A Local Computing Cell and 6T SRAM-Based Computing-in-Memory Macro With 8-b MAC Operation for Edge AI Chips

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

  • A Battery-Less, Implantable Neuro-Electronic Interface for Studying the Mechanisms of Deep Brain Stimulation in Rat Models

    Yu-Po Lin;Chun-Yi Yeh;Pin-Yang Huang;Zong-Ye Wang

  • Embedded 1-Mb ReRAM-Based Computing-in- Memory Macro With Multibit Input and Weight for CNN-Based AI Edge Processors

    Cheng-Xin Xue;Ting-Wei Chang;Tung-Cheng Chang;Hui-Yao Kao

  • A 0.5-V Real-Time Computational CMOS Image Sensor With Programmable Kernel for Feature Extraction

    Tzu-Hsiang Hsu;Yi-Ren Chen;Ren-Shuo Liu;Chung-Chuan Lo

  • VLSI Implementation of a Bio-Inspired Olfactory Spiking Neural Network

    Hung-Yi Hsieh;Kea-Tiong Tang

  • A 4-Kb 1-to-8-bit Configurable 6T SRAM-Based Computation-in-Memory Unit-Macro for CNN-Based AI Edge Processors

    Yen-Cheng Chiu;Zhixiao Zhang;Jia-Jing Chen;Xin Si

  • Apparatus to provide Safety Checks for Neural Stimulation

    Robert Greenberg;Kelly Mcclure;James Little;Rongqing Dai

  • A Low-Power Electronic Nose Signal-Processing Chip for a Portable Artificial Olfaction System

    Kea-Tiong Tang;Shih-Wen Chiu;Meng-Fan Chang;Chih-Cheng Hsieh

Frequent Co-Authors

Chih-Cheng Hsieh
Chih-Cheng Hsieh National Tsing Hua University
Meng-Fan Chang
Meng-Fan Chang National Tsing Hua University
Li-Chun Wang
Li-Chun Wang National Yang Ming Chiao Tung University
Ya-Chin King
Ya-Chin King National Tsing Hua University
Shimeng Yu
Shimeng Yu Georgia Institute of Technology
Chih-I Wu
Chih-I Wu National Taiwan University
Mohamad Sawan
Mohamad Sawan Polytechnique Montréal

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 exploring further education options in Electronics and Electrical Engineering, flexible learning paths are increasingly available. Many professionals benefit from online degrees for working adults, allowing them to balance career demands with continuing education.

Additionally, interdisciplinary fields like instructional design are gaining popularity. Pursuing an instructional design masters online can open doors to career opportunities in training and development, leveraging technical expertise to create educational content.

Competency-based approaches also play a significant role in modern education. Many institutions offer competency-based online colleges where students progress by demonstrating skills and knowledge, rather than time spent in class—ideal for self-motivated learners.

Moreover, for military families, finding the right program can be a challenge. Thankfully, there are several options among the best online college for military spouses listings that accommodate their unique needs, offering support and flexibility.

Best Scientists Citing Kea-Tiong Tang

Trending Scientists

Recently Published Articles