Engineers traditionally use deterministic modeling in their tasks, but challenges for developing and optimizing products and processes inspire us to venture beyond deterministic to probabilistic or stochastic modeling. In this continuation from the prior presentation, a case study applying predictive engineering for the optimization of 4 requirements for an integrated circuit (Integrated Alternator Regulator for automobiles) will be shared. Deterministic models were derived by using design of experiments (DOE) and response surface modeling (RSM) in concert with circuit simulations. These deterministic models were melded with probabilistic modeling using Monte Carlo Simulation and Variance Transmission. Yield Surface Modeling™ 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.
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Co-sponsored by: Ad Astra Foundation
Speaker(s): Eric Maass, PhD
Virtual: https://events.vtools.ieee.org/m/424630
Using Predictive Engineering for Multiple Response Optimization of an Integrated Circuit
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