World's Best Scientists 2026 revealed!

D-Index & Metrics

Electronics and Electrical Engineering

D-Index
38
Citations
6317
World Ranking
4893
National Ranking
89

Overview

Chih-Cheng Hsieh is affiliated with National Tsing Hua University in Taiwan and primarily works in the field of engineering, with a strong focus on electrical and electronic engineering. Their research covers multiple subfields, including artificial intelligence, cellular and molecular neuroscience, computer vision and pattern recognition, and biomedical engineering.

The research topics that Hsieh has extensively contributed to include:

  • Advanced Memory and Neural Computing
  • Ferroelectric and Negative Capacitance Devices
  • Semiconductor materials and devices
  • CCD and CMOS Imaging Sensors
  • Neuroscience and Neural Engineering
  • Low-power high-performance VLSI design
  • Advanced Neural Network Applications

Hsieh has published numerous papers, with frequent appearances in high-profile venues such as the IEEE Journal of Solid-State Circuits, the 2022 IEEE International Solid-State Circuits Conference (ISSCC), and Nature Electronics. Their publication record features significant contributions to compute-in-memory technologies and AI edge devices.

Selected recent papers include:

  • 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

Their frequent coauthors include:

  • Kea-Tiong Tang
  • Meng-Fan Chang
  • Ren-Shuo Liu
  • Chung-Chuan Lo
  • Win-San Khwa

Hsieh's work is mainly published in the following venues:

  • IEEE Journal of Solid-State Circuits
  • 2022 IEEE International Solid-State Circuits Conference (ISSCC)
  • Nature Electronics
  • Science
  • Nature

Best Publications

  • 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

  • Focal-plane-arrays and CMOS readout techniques of infrared imaging systems

    Chih-Cheng Hsieh;Chung-Yu Wu;Far-Wen Jih;Tai-Ping Sun

  • 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

  • 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

  • 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 0.3 V 10-bit 1.17 f SAR ADC With Merge and Split Switching in 90 nm CMOS

    Jin-Yi Lin;Chih-Cheng Hsieh

  • A new cryogenic CMOS readout structure for infrared focal plane array

    Chih-Cheng Hsieh;Chung-Yu Wu;Tai-Ping Sun

  • A 2.4-to-5.2fJ/conversion-step 10b 0.5-to-4MS/s SAR ADC with charge-average switching DAC in 90nm CMOS

    Chang-Yuan Liou;Chih-Cheng Hsieh

  • 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

  • High-performance CMOS buffered gate modulation input (BGMI) readout circuits for IR FPA

    Chih-Cheng Hsieh;Chung-Yu Wu;Tai-Ping Sun;Far-Wen Jih

  • A 2.02–5.16 fJ/Conversion Step 10 Bit Hybrid Coarse-Fine SAR ADC With Time-Domain Quantizer in 90 nm CMOS

    Yan-Jiun Chen;Kwuang-Han Chang;Chih-Cheng Hsieh

  • 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

  • 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

Kea-Tiong Tang
Kea-Tiong Tang National Tsing Hua University
Meng-Fan Chang
Meng-Fan Chang National Tsing Hua University
Chung-Yu Wu
Chung-Yu Wu 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

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