Title :
An accurate and efficient yield optimization method for analog circuits based on computing budget allocation and memetic search technique
Author :
Liu, Bo ; Fernández, Francisco V. ; Gielen, Georges
Author_Institution :
ESAT-MICAS, Katholieke Univ. Leuven, Leuven, Belgium
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.
Keywords :
Monte Carlo methods; analogue integrated circuits; constraint theory; estimation theory; evolutionary computation; integrated circuit yield; sampling methods; search problems; Latin-hypercube sampling techniques; Monte-Carlo simulation; acceptance sampling; analog integrated circuits; budget allocation; convergence speed; hybrid evolutionary constrained optimization algorithm; memetic ordinal optimization; memetic search technique; sampling-based yield optimization approach; two-stage estimation flow; yield estimation; Analog circuits; Analog computers; CMOS technology; Circuit simulation; Computational modeling; Constraint optimization; Integrated circuit yield; Optimization methods; Sampling methods; Yield estimation; Monte-Carlo; OO; Yield optimization; memetic algorithm; process variation; variation-aware analog sizing;
Conference_Titel :
Design, Automation & Test in Europe Conference & Exhibition (DATE), 2010
Conference_Location :
Dresden
Print_ISBN :
978-1-4244-7054-9
DOI :
10.1109/DATE.2010.5456974