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Youngwoo Kim
Researcher at Nara Institute of Science and Technology
Publications - 85
Citations - 1007
Youngwoo Kim is an academic researcher from Nara Institute of Science and Technology. The author has contributed to research in topics: Interposer & Decoupling capacitor. The author has an hindex of 13, co-authored 85 publications receiving 645 citations. Previous affiliations of Youngwoo Kim include Georgia Institute of Technology & KAIST.
Papers
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Journal ArticleDOI
Giant Permittivity in Epitaxial Ferroelectric Heterostructures.
Journal ArticleDOI
EMI Reduction Methods in Wireless Power Transfer System for Drone Electrical Charger Using Tightly Coupled Three-Phase Resonant Magnetic Field
Chiuk Song,Hongseok Kim,Youngwoo Kim,Dong-Hyun Kim,Seungtaek Jeong,Yeonje Cho,Seongsoo Lee,Seungyoung Ahn,Joungho Kim +8 more
TL;DR: This paper proposes a new tightly coupled three-phase resonant magnetic field charger for a drone operating at 60 kHz, which can completely eliminate the third harmonic and its integer multiples in the output voltage and the conduction angle control is proposed for the WPT charging system.
Journal ArticleDOI
Through Silicon Via (TSV) Defect Modeling, Measurement, and Analysis
Daniel H. Jung,Youngwoo Kim,Jonghoon J. Kim,Heegon Kim,Sumin Choi,Yoon-Ho Song,Hyun-Cheol Bae,Kwang-Seong Choi,Stefano Piersanti,Francesco de Paulis,Antonio Orlandi,Joungho Kim +11 more
TL;DR: In this article, the authors proposed a noninvasive defect analysis method for high-speed TSV channel with designed and fabricated test vehicles, and the proposed method is demonstrated with time-domain reflectometry measurement results.
Proceedings ArticleDOI
A 2.1TFLOPS/W Mobile Deep RL Accelerator with Transposable PE Array and Experience Compression
TL;DR: Recently, deep neural networks are actively used for object recognition, but also for action control, so that an autonomous system, such as the robot, can perform human-like behaviors and operations and it is too slow to use remote learning on a server communicating through a network.
Journal ArticleDOI
Deep Reinforcement Learning-Based Optimal Decoupling Capacitor Design Method for Silicon Interposer-Based 2.5-D/3-D ICs
HyunWook Park,Seongguk Kim,Youngwoo Kim,Joungho Kim,Junyong Park,Subin Kim,Kyungjun Cho,Daehwan Lho,Seungtaek Jeong,Shinyoung Park,Gapyeol Park,Boogyo Sim +11 more
TL;DR: A deep reinforcement learning (RL)-based optimal decoupling capacitor (decap) design method for silicon interposer-based 2.5-D/3-D integrated circuits (ICs) that provides an optimal decap design that satisfies target impedance with a minimum area.