Efficient prediction of stochastic eye patterns via Lagrange interpolation

The statistical assessment of system performance is addressed via the efficient estimation of eye diagram parameters under process variability. Statistical information pertaining to eye parameters is reconstructed by interpolation of a reduced set of simulations. The proposed strategy shows a remarkable efficiency improvement with respect to classical blind and brute-force sampling-based methods.