Neural computing: theory and practice
Neural computing: theory and practice
Proceedings of the 38th annual Design Automation Conference
Convergence Properties of the Nelder--Mead Simplex Method in Low Dimensions
SIAM Journal on Optimization
Simulation Budget Allocation for Further Enhancing theEfficiency of Ordinal Optimization
Discrete Event Dynamic Systems
Differential Evolution: A Practical Approach to Global Optimization (Natural Computing Series)
Differential Evolution: A Practical Approach to Global Optimization (Natural Computing Series)
Generation of yield-aware Pareto surfaces for hierarchical circuit design space exploration
Proceedings of the 43rd annual Design Automation Conference
Ordinal Optimization: Soft Computing for Hard Problems (International Series on Discrete Event Dynamic Systems)
VLSID '09 Proceedings of the 2009 22nd International Conference on VLSI Design
A memetic approach to the automatic design of high-performance analog integrated circuits
ACM Transactions on Design Automation of Electronic Systems (TODAES)
A Fast Non-Monte-Carlo Yield Analysis and Optimization by Stochastic Orthogonal Polynomials
ACM Transactions on Design Automation of Electronic Systems (TODAES)
| Hi-index | 0.00 |
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 μm and 90nm CMOS technologies show that MOHECO presents important advantages in terms of accuracy and efficiency.