Seyoung Kim           

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/