Efficient and accurate statistical analog yield optimization and variation-aware circuit sizing based on computational intelligence techniques

Liu, B. , Fernandez, F. and Gielen, G. G.E. (2011) Efficient and accurate statistical analog yield optimization and variation-aware circuit sizing based on computational intelligence techniques. IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems, 30(6), pp. 793-805. (doi: 10.1109/TCAD.2011.2106850)

Abstract

In nanometer complementary metal-oxide-semiconductor 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.

• Item Type: Articles
• Status: Published
• Refereed: Yes
• Glasgow Author(s) Enlighten ID: Liu, Dr Bo
• Authors: Liu, B., Fernandez, F., and Gielen, G. G.E.
• College/School: College of Science and Engineering > School of Engineering
• Journal Name: IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems
• Publisher: IEEE
• ISSN: 0278-0070
• ISSN (Online): 1937-4151
• Published Online: 16 May 2011