IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems vol:30 issue:6 pages:793-805
In nanometer complementary metal-oxide-semi-conductor technologies, worst-case design methods and response-surface-based yield optimization methods face challenges in
accuracy. Monte-Carlo (MC) simulation is general and accurate for yield estimation, but its efﬁciency is not high enough to make MC-based analog yield optimization, which requires many yield estimations, practical. In this paper, techniques inspired by computational intelligence are used to speed up yield optimization without sacriﬁcing accuracy. A new sampling-based yield optimization approach, which determines the device sizes to optimize yield, is presented, called the ordinal optimization (OO)- based random-scale differential evolution (ORDE) algorithm. By
proposing a two-stage estimation ﬂow and introducing the OO
technique in the ﬁrst stage, sufﬁcient samples are allocated to promising solutions, and repeated MC simulations of non-critical solutions are avoided. By the proposed evolutionary algorithm that uses differential evolution for global search and a random- scale mutation operator for ﬁne tunings, the convergence speed of the yield optimization can be enhanced signiﬁcantly. With
the same accuracy, the resulting ORDE algorithm can achieve
approximately a tenfold improvement in computational effort
compared to an improved MC-based yield optimization algorithm integrating the infeasible sampling and Latin hypercube sampling techniques. Furthermore, ORDE is extended from plain yield optimization to process-variation-aware single-objective circuit sizing.