2020 IEDM Short Courses

IEDM will offer two short courses with in-depth coverage of highly relevant topics from world experts.
Advance registration is recommended.

Starting Sunday, December 6, 2020 – On Demand Streaming of the Short Courses
Live Question and Answer with Lecturers – Sunday, December 13, 8:00 AM– 9:00 AM (PST)
If you have purchased the short course bundle registration, you will have access to both Short Courses

Short Course 1:
Innovative trends in device technology to enable the next computing revolution, Organizer: Anne Vandooren, IMEC

  • Differentiated Silicon Technologies for Mobile Radio Front End – a journey from sub 6GHz to mmWave, Anirban Bandyopadhyay, Director, Strategic Applications, GLOBALFOUNDRIES, Inc. (Download Abstract/Bio)
  • Power Electronics for Next-Gen Computing: Topologies and Device Needs, Yogesh Ramadass, Texas Instruments (Download Abstract/Bio)
  • Enablement of Next Generation High Performance NanoSheet Transistors, Nicolas Loubet, Advanced CMOS Logic Research, IBM Research (Download Abstract/Bio) 
  • Advanced 3D System Integration Technologies, KC Yee, TSMC (Download Abstract/Bio)
  • 3D sequential integration : opportunities, breakthrough and challenges, Claire Fenouillet-Beranger, PhD, LETI (Download Abstract/Bio) 
  • Integration Technology – From Package Level to Wafer Level Integration,  SE-Ho You, Samsung (Download Abstract/Bio)

Short Course 2:
Memory bound computing, Organizer, Ian Young , Intel

  • Compute memory trends: from application requirements to architectural needs,  Simon Hammond, DOE
  • Role of persistent memory in computing for high performance computing, Frank Hady, Intel
  • HBM D-RAM and beyond, Shekhar Borkar, Qualcomm
  • Memory for secure computing, Todd Austin, University of Michigan
  • PUFs for hardware security, Sanu Mathew, Intel
  • Alternate technologies for SRAM – pros/cons DRAM, RRAM, MRAM, RRAM, FE-FET, to be announced
  • Analog memory needs for AI – compute-in-memory, Multi-level bit-cell, Shimeng Yu, Georgia Tech
  • One-shot learning with memory augmented neural network, Michael Niemier, Notre Dame