Yield-Aware Analog IC Design and Optimization in Nanometer-scale Technologies

This book presents a new methodology with reduced time impact to address the problem of analog integrated circuit (IC) yield estimation by means of Monte Carlo (MC) analysis, inside an optimization loop of a population-based algorithm. The low time impact on the overall optimization processes enables IC designers to perform yield optimization with the most accurate yield estimation method, MC simulations using foundry statistical device models considering local and global variations.  The methodology described by the authors delivers on average a reduction of 89% in the total number of MC simulations, when compared to the exhaustive MC analysis over the full population.  In addition to describing a newly developed yield estimation technique, the authors also provide detailed background on automatic analog IC sizing and optimization.
Describes a new yield estimation methodology to reduce the time impact caused by Monte Carlo simulations, enabling its adoption in analog integrated circuits sizing and optimization processes with population-based algorithms;Enables designers to reduce the number of redesign iterations, by considering the robustness of solutions at early stages of the analog IC design flow;Includes detailed background on automatic analog IC sizing and optimization.