Accelerated evolutionary algorithms with parameterimportance based population initialization for variation-aware analog yield optimization

Evolutionary algorithms are popularly used methods to estimate yield for faster convergence. Monte-Carlo is the method of choice for accurate yield estimation. Standard Monte-Carlo methods suffer from huge computational burden even though they are very accurate. Monte-Carlo efficiency is not high enough to use impartial for Analog Yield Optimization. Population initialization is a decisive task in evolutionary algorithms. It can strongly influence the convergence speed and also the qualities of the final solution. We propose a method to initialize population of evolutionary algorithms ESP (Evolutionary Algorithms with Special Sampling), ERP (Evolutionary Algorithms with Random Sampling) to achieve significant computational savings at no loss of accuracy. We focus on statistical variables that have a significant impact on yield. We adapt this technique used in parametric yield estimation of digital integrated circuits. This knowledge of design variables is used to initialize population that achieves faster convergence and lower error compared to random population initialization in the same simulation time.