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.
