Abstract
The increasing complexity of circuit design needs to be managed with appropriate optimization algorithms and accurate statistical descriptions of design models in order to reach the design specifics, thus guaranteeing “zero defects”. In theDesign for Yieldopen problems are the design of effective optimization algorithms and statistical analysis for yield design, which require time consuming techniques. New methods have to balance accuracy, robustness and computational effort. Typical analog integrated circuit optimization problems are computationally hard and require the handling ofmultiple, conflicting, and non-commensurate objectives having strong nonlinear interdependence. This paper tackles the problem by evolutionary algorithms to produce tradeoff solutions. In this research work, Integrated Circuit (IC) design has been formulated as a constrained multi-objective optimization problem defined in a mixed integer/ discrete/continuous domain. TheRF Low Noise Amplifier, Leapfrog Filter, and Ultra Wideband LNA real-life circuits were selected as test beds. The proposed algorithm, A-NSGAII, was shown to produce acceptable and robust solutions in the tested applications, where state-ofart algorithms and circuit designers failed. The results show significant improvement in all the chosen IC design problems.
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References
M. Burns, G. W. Roberts, ”An Introduction to Mixed-Signal IC Test and Measurement,” Oxford University Press, 2001.
V. Cutello, G. Nicosia, M. Pavone, ”A hybrid Immune Algorithm with Information Gain for the Graph Coloring Problem,” Proc. GECCO’03, LNCS2723, pp.171-182, 2003.
B. Razavi, ”RF Microelectronics,” Prentice Hall Inc., 1998
P. Magarshack, P.G. Paulin, ”System-on-Chip Beyond the Nanometer Wall,” 40th DAC, pp. 419-424, 2003.
F. Schenkel, et al., ”Mismatch Analysis and Direct Yield Optimization by Spec-Wise Linearization and Feasibility Guided Search,” 38th DAC, pp. 858-863, 2001.
W. Stadler, ”Fundamentals of multicriteria optimization”. In Stadler ed., ”Multicriteria Optimization in Engineering and the Sciences,” pp 1-25, Plenum Press, NY, 1988.
K. Deb, ”Multi-Objective Optimization Using Evolutionary Algorithms,”Wiley, 2001.
V. Cutello, G. Morelli, G. Nicosia, M. Pavone, ”Immune Algorithms with Aging Operators for the String Folding Problem and the Protein Folding Problem,” Proc. EvoCop’05, LNCS 3448, pp. 80-90, 2005.
L. Cirio, S. Lucidi, F. Parasiliti, M. Villani, ”A global optimization approach for the synchronous motors design by finite element analysis,” Int. J. of Applied Electromagnetics and Mechanics, 16:13-27, 2002.
V. Cutello, G. Narzisi, M. Pavone, ”Real Coded Clonal Selection Algorithm for Global Numerical Optimization using a new Inversely Proportional Hypermutation Operator,” 21st ACM SAC, pp. 950-954, 2006.
J. M. Gablonsky, C. T. Kelley,”A locally-biased form of the DIRECT Algorithm,”J. of Global Optimization, 21:27-37, 2001.
M.J.D. Powell, ”The NEWUOA software for unconstrained optimization without derivatives,”, in Large-Scale Nonlinear Optimization, eds. G. Di Pillo & M. Roma, Springer (NY), pp. 255-297, 2006.
G. Girlando et al., ”Silicon Bipolar LNAs in the X and Ku Bands,” Analog Integrated Circuits and Signal Processing, 41:119-127, 2004.
B.H. Calhoun, et al., ”Static Noise Margin Variation for Sub-threshold SRAM in 65-nm CMOS,” IEEE J. of Solid-State Cir., 41:1673-1679, 2006.
J. Rabaey, et al., ”Ultra-Low-Power Design,” IEEE Circuits and Devices Magazine, 22(4):23-29, 2006.
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Nicosia, G., Rinaudo, S., Sciacca, E. (2008). An Evolutionary Algorithm-Based Approach to Robust Analog Circuit Design using Constrained Multi-Objective Optimization. In: Bramer, M., Coenen, F., Petridis, M. (eds) Research and Development in Intelligent Systems XXIV. SGAI 2007. Springer, London. https://doi.org/10.1007/978-1-84800-094-0_2
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DOI: https://doi.org/10.1007/978-1-84800-094-0_2
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