An Accurate and Efficient Yield Optimization Method for Analog Circuits Based on Computing Budget Allocation and Memetic Search Technique

Liu, B. , Fernández, F. V. and Gielen, G. (2010) An Accurate and Efficient Yield Optimization Method for Analog Circuits Based on Computing Budget Allocation and Memetic Search Technique. In: 2010 Design, Automation and Test in Europe Conference and Exhibition (DATE 2010), Dresden, Germany, 08-12 Mar 2010, ISBN 9783981080162 (doi: 10.1109/DATE.2010.5456974)

Abstract

Monte-Carlo (MC) simulation is still the most commonly used technique for yield estimation of analog integrated circuits, because of its generality and accuracy. However, although some speed acceleration methods for MC simulation have been proposed, their efficiency is not high enough for MC-based yield optimization (determines optimal device sizes and optimizes yield at the same time), which requires repeated yield calculations. In this paper, a new sampling-based yield optimization approach is presented, called the Memetic Ordinal Optimization (OO)-based Hybrid Evolutionary Constrained Optimization (MOHECO) algorithm, which significantly enhances the efficiency for yield optimization while maintaining the high accuracy and generality of MC simulation. By proposing a two-stage estimation flow and introducing the OO technology in the first stage, sufficient samples are allocated to promising solutions, and repeated MC simulations of non-critical solutions are avoided. By the proposed memetic search operators, the convergence speed of the algorithm can considerably be enhanced. With the same accuracy, the resulting MOHECO algorithm can achieve yield optimization by approximately 7 times less computational effort compared to a state-of-the-art MC-based algorithm integrating the acceptance sampling (AS) plus the Latin-hypercube sampling (LHS) techniques. Experiments and comparisons in 0.35 ¿m and 90 nm CMOS technologies show that MOHECO presents important advantages in terms of accuracy and efficiency.

• Item Type: Conference Proceedings
• Status: Published
• Refereed: Yes
• Glasgow Author(s) Enlighten ID: Liu, Professor Bo
• Authors: Liu, B., Fernández, F. V., and Gielen, G.
• College/School: College of Science and Engineering > School of Engineering
• ISSN: 1530-1591
• ISBN: 9783981080162
• Published Online: 29 April 2010