World's Best Scientists 2026 revealed!

D-Index & Metrics

Electronics and Electrical Engineering

D-Index
37
Citations
6187
World Ranking
5099
National Ranking
1772

Overview

Mingoo Seok is affiliated with Columbia University in the United States. The research focus spans multiple fields including Engineering and Computer Science, with an emphasis on Electrical and Electronic Engineering, Hardware and Architecture, Biomedical Engineering, Computer Networks and Communications, and Computer Vision and Pattern Recognition.

The scientist's research covers several main topics, including:

  • Advanced Memory and Neural Computing
  • Low-power high-performance VLSI design
  • Ferroelectric and Negative Capacitance Devices
  • Semiconductor materials and devices
  • Analog and Mixed-Signal Circuit Design
  • Advancements in Semiconductor Devices and Circuit Design
  • Parallel Computing and Optimization Techniques

Frequent coauthors include:

  • Sung Justin Kim
  • Weifeng He
  • Jae-sun Seo
  • Dewei Wang
  • Ram Krishnamurthy

Common publication venues are:

  • IEEE Journal of Solid-State Circuits
  • arXiv (Cornell University)
  • IEEE Solid-State Circuits Letters
  • IEEE Transactions on Applied Superconductivity
  • IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems

Notable recent papers include:

  • "XNOR-SRAM: In-Memory Computing SRAM Macro for Binary/Ternary Deep Neural Networks" (2020), published in IEEE Journal of Solid-State Circuits
  • "C3SRAM: An In-Memory-Computing SRAM Macro Based on Robust Capacitive Coupling Computing Mechanism" (2020), published in IEEE Journal of Solid-State Circuits
  • "DIMC: 2219TOPS/W 2569F2/b Digital In-Memory Computing Macro in 28nm Based on Approximate Arithmetic Hardware" (2022), published in 2022 IEEE International Solid-State Circuits Conference (ISSCC)
  • "PIMCA: A Programmable In-Memory Computing Accelerator for Energy-Efficient DNN Inference" (2022), published in IEEE Journal of Solid-State Circuits
  • "Nanowatt Acoustic Inference Sensing Exploiting Nonlinear Analog Feature Extraction" (2021), published in IEEE Journal of Solid-State Circuits

Best Publications

  • A Portable 2-Transistor Picowatt Temperature-Compensated Voltage Reference Operating at 0.5 V

    Mingoo Seok;Gyouho Kim;D. Blaauw;D. Sylvester

  • XNOR-SRAM: In-Memory Computing SRAM Macro for Binary/Ternary Deep Neural Networks

    Shihui Yin;Zhewei Jiang;Jae-Sun Seo;Mingoo Seok

  • C3SRAM: An In-Memory-Computing SRAM Macro Based on Robust Capacitive Coupling Computing Mechanism

    Zhewei Jiang;Shihui Yin;Jae-Sun Seo;Mingoo Seok

  • Millimeter-scale nearly perpetual sensor system with stacked battery and solar cells

    Gregory Chen;Matthew Fojtik;Daeyeon Kim;David Fick

  • A cubic-millimeter energy-autonomous wireless intraocular pressure monitor

    G Chen;H Ghaed;R Haque;M Wieckowski

  • A Low-Voltage Processor for Sensing Applications With Picowatt Standby Mode

    Scott Hanson;Mingoo Seok;Yu-Shiang Lin;Zhiyoong Foo

  • A Low-Voltage Processor for Sensing Applications With Picowatt Standby Mode

    S. Hanson;Mingoo Seok;Yu-Shiang Lin;Zhi Yoong Foo

  • The Phoenix Processor: A 30pW platform for sensor applications

    Mingoo Seok;S. Hanson;Yu-Shiang Lin;Zhiyoong Foo

  • Exploring Variability and Performance in a Sub-200-mV Processor

    S. Hanson;Bo Zhai;Mingoo Seok;B. Cline

  • Nanometer Device Scaling in Subthreshold Logic and SRAM

    S. Hanson;Mingoo Seok;D. Sylvester;D. Blaauw

  • XNOR-SRAM: In-Memory Computing SRAM Macro for Binary/Ternary Deep Neural Networks

    Zhewei Jiang;Shihui Yin;Mingoo Seok;Jae-sun Seo

  • A Millimeter-Scale Energy-Autonomous Sensor System With Stacked Battery and Solar Cells

    M. Fojtik;Daeyeon Kim;G. Chen;Yu-Shiang Lin

  • Circuits for a Cubic-Millimeter Energy-Autonomous Wireless Intraocular Pressure Monitor

    Mohammad Hassan Ghaed;Gregory Chen;Razi-ul Haque;Michael Wieckowski

  • Variation-Tolerant, Ultra-Low-Voltage Microprocessor With a Low-Overhead, Within-a-Cycle In-Situ Timing-Error Detection and Correction Technique

    Seongjong Kim;Mingoo Seok

  • A Super-Pipelined Energy Efficient Subthreshold 240 MS/s FFT Core in 65 nm CMOS

    Dongsuk Jeon;Mingoo Seok;C. Chakrabarti;D. Blaauw

  • Energy-Efficient Hybrid Analog/Digital Approximate Computation in Continuous Time

    Ning Guo;Yipeng Huang;Tao Mai;Sharvil Patil

  • Ultra-Compact and Robust Physically Unclonable Function Based on Voltage-Compensated Proportional-to-Absolute-Temperature Voltage Generators

    Jiangyi Li;Mingoo Seok

  • Performance and Variability Optimization Strategies in a Sub-200mV, 3.5pJ/inst, 11nW Subthreshold Processor

    S. Hanson;Bo Zhai;Mingoo Seok;B. Cline

  • A hybrid DC-DC converter for sub-microwatt sub-1V implantable applications

    Michael Wieckowski;Gregory K. Chen;Mingoo Seok;David Blaauw

  • 8.2 Fully integrated low-drop-out regulator based on event-driven PI control

    Doyun Kim;Mingoo Seok

  • CAS-FEST 2010: Mitigating Variability in Near-Threshold Computing

    Mingoo Seok;Gregory Chen;Scott Hanson;Michael Wieckowski

  • 14.1 A 510nW 0.41V Low-Memory Low-Computation Keyword-Spotting Chip Using Serial FFT-Based MFCC and Binarized Depthwise Separable Convolutional Neural Network in 28nm CMOS

    Weiwei Shan;Minhao Yang;Jiaming Xu;Yicheng Lu

Frequent Co-Authors

Dennis Sylvester
Dennis Sylvester University of Michigan–Ann Arbor
David Blaauw
David Blaauw University of Michigan–Ann Arbor
Peter R. Kinget
Peter R. Kinget Columbia University
Yannis Tsividis
Yannis Tsividis Columbia University
Yoonmyung Lee
Yoonmyung Lee Sungkyunkwan University
Chaitali Chakrabarti
Chaitali Chakrabarti Arizona State University
Huadong Ma
Huadong Ma Beijing University of Posts and Telecommunications
Aurel A. Lazar
Aurel A. Lazar Columbia University
Ioannis Kymissis
Ioannis Kymissis Columbia University
Kensall D. Wise
Kensall D. Wise University of Michigan–Ann Arbor

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