| | GSRC Student Profile:
Research Overview: Nonlinear model order reduction techniques and yield estimation methods considering paramter variations
My research interests include RF circuit simulation and macromodelling, applied numerical methods on circuit simulation, sustainable and modular numerical simulation infrastructures, etc.
Recent researches include nonlinear model order reduction methods, and yield estimation methods considering parameter variations.
The nonlinear MOR technique presents a new and powerful paradigm in this area, corrects several key problems in some previous techniques, and casts inspirations for further researches. It is validated on both analog circuits and bio-chemical systems, and shows great reduction in model size.
The yield estimation problem has been a research for a long time, and is becoming increasingly important with the CMOS shrinking. The method we present employs adaptive geometric hypervolume calculation to efficiently determine the yield. It is also tested on a analog circuit (ring oscillator) and a digital circuit (an SRAM cell), and shows considerable speed-ups over classical Monte-Carlo.
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