  The temperature developed in bondwires of integrated circuits (ICs) is a
possible source of malfunction, and has to be taken into account during the
design phase of an IC. Due to manufacturing tolerances, a bondwire's
geometrical characteristics are uncertain parameters, and as such their impact
has to be examined with the use of uncertainty quantification (UQ) methods.
Sampling methods, like the Monte Carlo (MC), converge slowly, while efficient
alternatives scale badly with respect to the number of considered
uncertainties. Possible remedies to this, so-called, curse of dimensionality
are sought in the application of stochastic collocation (SC) on sparse grids
(SGs) and of the recently emerged low-rank tensor decomposition methods, with
emphasis on the tensor train (TT) decomposition.
