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PRODID:IEEE vTools.Events//EN
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TZID:Pacific/Honolulu
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DTSTART:19470608T023000
TZOFFSETFROM:-1130
TZOFFSETTO:-1000
TZNAME:HST
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BEGIN:VEVENT
DTSTAMP:20240915T065946Z
UID:9AE8D623-4613-4BEB-842E-ED2FA45BA49F
DTSTART;TZID=Pacific/Honolulu:20240913T090000
DTEND;TZID=Pacific/Honolulu:20240913T100000
DESCRIPTION:Engineers traditionally use deterministic modeling in their tas
 ks\, but challenges for developing and optimizing products and processes i
 nspire us to venture beyond deterministic to probabilistic or stochastic m
 odeling. In this continuation from the prior presentation\, a case study a
 pplying predictive engineering for the optimization of 4 requirements for 
 an integrated circuit (Integrated Alternator Regulator for automobiles) wi
 ll be shared. Deterministic models were derived by using design of experim
 ents (DOE) and response surface modeling (RSM) in concert with circuit sim
 ulations. These deterministic models were melded with probabilistic modeli
 ng using Monte Carlo Simulation and Variance Transmission. Yield Surface M
 odeling™ will be introduced and shared and applied for stochastic co-opt
 imization of the four requirements of the design. Brief summaries and over
 views of other applications of predictive engineering to integrated circui
 t design stochastic optimization will be shared.\n\n[]\n\nCo-sponsored by:
  Ad Astra Foundation\n\nSpeaker(s): Eric Maass\, PhD\n\nVirtual: https://e
 vents.vtools.ieee.org/m/424630
LOCATION:Virtual: https://events.vtools.ieee.org/m/424630
ORGANIZER:brianne.tengan@ieee.org
SEQUENCE:32
SUMMARY:Using Predictive Engineering for Multiple Response Optimization of 
 an Integrated Circuit 
URL;VALUE=URI:https://events.vtools.ieee.org/m/424630
X-ALT-DESC:Description: <br /><p>Engineers traditionally use deterministic 
 modeling in their tasks\, but challenges for developing and optimizing pro
 ducts and processes inspire us to venture beyond deterministic to probabil
 istic or stochastic modeling.&nbsp\; In this continuation from the prior p
 resentation\, a case study applying predictive engineering for the optimiz
 ation of 4 requirements for an integrated circuit (Integrated Alternator R
 egulator for automobiles) will be shared. Deterministic models were derive
 d by using design of experiments (DOE) and response surface modeling (RSM)
  in concert with circuit simulations. These deterministic models were meld
 ed with probabilistic modeling using Monte Carlo Simulation and Variance T
 ransmission. Yield Surface Modeling&trade\; will be introduced and shared 
 and applied for stochastic co-optimization of the four requirements of the
  design. Brief summaries and overviews of other applications of predictive
  engineering to integrated circuit design stochastic optimization will be 
 shared.</p>\n<p><img style="display: block\; margin-left: auto\; margin-ri
 ght: auto\;" src="https://events.vtools.ieee.org/vtools_ui/media/display/8
 81f982a-aeae-4afe-8f59-8db6f02ca0d8" alt="" width="714" height="714"></p>
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