Seyoung Kim
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Research Staff MemberIBM Thomas J. Watson Research Center, Yorktown Heights, NY USA +1
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Research Highlights
- Devices and Materials for Deep Learning and AI
- Established HfOx-based RRAM prototype and studied T-dependent conduction mechanism (VLSI TSA 2014, APS 2014)
- Demonstrated sensorimotor learning in RRAM crossbar array using spiking neurons and STDP learning rule (APS 2015)
- Revealed trade-off between on-off ratio and symmetry in RRAM by fast-pulse switching measurement (MRS 2017)
- Proposed a capacitor-based synapse design (MWSCAS 2017) and demonstrated record linearity in switching (VLSI 2018)
- Separated signal and noise in pulse switching response of RRAM using Gaussian process regression (Nature Comm 2018)
- Demonstrated highly-symmetric switching in Li-based ECRAM by novel current source-based cell design (IEDM 2018)
- 2D Physics and Electronics: Graphene FETs and Heterostructures
- Realized dual-gated graphene FET using CVD-grown (Science 2009, citation: 8836) and natural graphene, and proposed a physical model widely accepted in the field (APL 2009, citation: 941)
- Pioneered the fabrication of graphene double-layer heterostructure and performed Coulomb drag measurement to characterize electron-electron interaction between two layers (PRB 2011, citation 164)
- Studied quantum Hall effect in graphene bilayer system at high magnetic field (Nano Letters 2011, PRL 2011)
- Proposed a novel technique to directly measure Fermi energy of electron system using graphene layer (PRL 2012, citation: 73)
Education
- Doctor of Philosophy
- Electrical and Computer Engineering, The University of Texas at Austin, 2012
- Master of Science in Engineering
- Electrical and Computer Engineering, The University of Texas at Austin, 2008
- Bachelor of Science in Electrical Engineering
- Electrical Engineering, Seoul National University, Korea, 2006
Google Scholar: http://scholar.google.com/citations?user=G99KBM8AAAAJ&hl=en
LinkedIn: https://www.linkedin.com/in/seyoungkim/