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SUMMARY:Variation-Aware and Data-Driven Algorithms and Tools for Enabling 
 Reliable Nanascale Design
DTSTART:20170203T141500
DTSTAMP:20260407T014444Z
UID:8defceaa4337e0582dc71107237389587e6da6de7a841e29474a8ca8
CATEGORIES:Conferences - Seminars
DESCRIPTION:Dr Zheng Zhang\, Massachusetts Institute of Technology USA\nA
 bstract\nThe reliability of many engineering systems (such as nanoscale in
 tegrated systems\, Internet of Things) can be significantly degraded by un
 avoidable uncertainties (e.g.\, fabrication process variations\, uncertain
  external environment\, and sensor noise). In order to improve circuit and
  system reliability\, variability-aware design automation algorithms and t
 ools should be developed to characterize\, estimate and control uncertaint
 ies at different levels of design hierarchy. These algorithms and tools ar
 e generally data-intensive: a huge amount of data should be generated by n
 on-trivial simulation or measurement\, leading to the notorious curse of d
 imensionality (i.e.\, the cost grows extremely fast as the number of uncer
 tain parameters increases). This talk presents some fast non-Monte-Carlo t
 echniques to estimate the uncertainties in nanoscale integrated circuits\,
  microelectromechanical systems (MEMS) and silicon photonics. A non-sampli
 ng stochastic simulator will be presented to estimate the uncertainties in
  DC\, transient and periodic steady-state analysis. Advanced algorithms wi
 ll be introduced to efficiently handle many process variations. These tech
 niques can be used as the backbone of a variation-aware design automation 
 flow\, and they can accelerate a lot of reliability-related tasks (e.g.\, 
 yield optimization\, chip testing\, process modeling\, and fault detection
 ). On the IC\, MEMS and photonic examples that we have tested\, our approa
 ches are significantly faster than Monte Carlo (by 100X to 1000X) and rece
 nt spectral methods (by dozens of times).\n \nBio:\nZheng Zhang is a post
 doc associate with MIT\, where he received his Ph.D. degree in Electrical 
 Engineering and Computer Science in 2015. He is interested in high-dimensi
 onal uncertainty analysis and data inference for diverse engineering probl
 ems\, including nanoscale devices and systems\, hybrid systems (e.g.\, pow
 er systems and robots) and MRI. Dr. Zhang received the 2016 ACM Outstandin
 g PhD Dissertation Award in Electronic Design Automation\, the 2015 Doctor
 al Dissertation Seminar Award from the Microsystems Technology Laboratory 
 of MIT\, and the 2014 Best Paper Award from IEEE Transactions on CAD of In
 tegrated Circuits and Systems. He is a TPC member of Design Automation Con
 ference (DAC) and International Conference on Computer-Aided Design (ICCAD
 ).\n 
LOCATION:ME D2 1124
STATUS:CONFIRMED
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