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Designs in nanoelectronics often lead to challenging simulation
problems and include strong feedback couplings. Industry demands
provisions for variability in order to guarantee quality and yield.
It also requires the incorporation of higher abstraction levels to
allow for system simulation in order to shorten the design cycles,
while at the same time preserving accuracy. The methods developed
here promote a methodology for circuit-and-system-level modelling
and simulation based on best practice rules, which are used to deal
with coupled electromagnetic field-circuit-heat problems, as well
as coupled electro-thermal-stress problems that emerge in
nanoelectronic designs. This book covers: (1) advanced
monolithic/multirate/co-simulation techniques, which are combined
with envelope/wavelet approaches to create efficient and robust
simulation techniques for strongly coupled systems that exploit the
different dynamics of sub-systems within multiphysics problems, and
which allow designers to predict reliability and ageing; (2) new
generalized techniques in Uncertainty Quantification (UQ) for
coupled problems to include a variability capability such that
robust design and optimization, worst case analysis, and yield
estimation with tiny failure probabilities are possible (including
large deviations like 6-sigma); (3) enhanced sparse, parametric
Model Order Reduction techniques with a posteriori error estimation
for coupled problems and for UQ to reduce the complexity of the
sub-systems while ensuring that the operational and coupling
parameters can still be varied and that the reduced models offer
higher abstraction levels that can be efficiently simulated. All
the new algorithms produced were implemented, transferred and
tested by the EDA vendor MAGWEL. Validation was conducted on
industrial designs provided by end-users from the semiconductor
industry, who shared their feedback, contributed to the
measurements, and supplied both material data and process data. In
closing, a thorough comparison to measurements on real devices was
made in order to demonstrate the algorithms' industrial
applicability.
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