Optimizing semiconductor devices by self-organizing particle swarm

A self-organizing particle swarm is presented. It works in dissipative state by employing the small inertia weight, according to experimental analysis on a simplified model, which with fast convergence. Then by recognizing and replacing inactive particles according to the process deviation information of device parameters, the fluctuation is introduced so as to driving the irreversible evolution process with better fitness. The testing on benchmark functions and an application example for device optimization with designed fitness function indicates it improves the performance effectively.

[1]  Wenjun Zhang,et al.  Dissipative particle swarm optimization , 2002, Proceedings of the 2002 Congress on Evolutionary Computation. CEC'02 (Cat. No.02TH8600).

[2]  R. Eberhart,et al.  Empirical study of particle swarm optimization , 1999, Proceedings of the 1999 Congress on Evolutionary Computation-CEC99 (Cat. No. 99TH8406).

[3]  Russell C. Eberhart,et al.  A new optimizer using particle swarm theory , 1995, MHS'95. Proceedings of the Sixth International Symposium on Micro Machine and Human Science.

[4]  Michael N. Vrahatis,et al.  Recent approaches to global optimization problems through Particle Swarm Optimization , 2002, Natural Computing.

[5]  Wenjun Zhang,et al.  Realization of semiconductor device synthesis with the parallel genetic algorithm , 2001, ASP-DAC '01.

[6]  Ioan Cristian Trelea,et al.  The particle swarm optimization algorithm: convergence analysis and parameter selection , 2003, Inf. Process. Lett..

[7]  J. Schnakenberg,et al.  G. Nicolis und I. Prigogine: Self‐Organization in Nonequilibrium Systems. From Dissipative Structures to Order through Fluctuations. J. Wiley & Sons, New York, London, Sydney, Toronto 1977. 491 Seiten, Preis: £ 20.–, $ 34.– , 1978 .

[8]  Andrzej J. Strojwas,et al.  Perspectives on technology and technology-driven CAD , 2000, IEEE Trans. Comput. Aided Des. Integr. Circuits Syst..

[9]  K. Deb.  An Efficient Constraint Handling Method for Genetic Algorithms , 2000 .

[10]  Xiao-Feng Xie,et al.  Adaptive particle swarm optimization on individual level , 2002, 6th International Conference on Signal Processing, 2002..

[11]  Frans van den Bergh,et al.  An analysis of particle swarm optimizers , 2002 .

[12]  Zbigniew Michalewicz,et al.  Genetic Algorithms + Data Structures = Evolution Programs , 1992, Artificial Intelligence.

[13]  James E. Murguia,et al.  Use of focused-ion-beam and modeling to optimize submicron MOSFET characteristics , 1998 .

[14]  David H. Wolpert,et al.  No free lunch theorems for optimization , 1997, IEEE Trans. Evol. Comput..

[15]  Maurice Clerc,et al.  The particle swarm - explosion, stability, and convergence in a multidimensional complex space , 2002, IEEE Trans. Evol. Comput..

[16]  H. H. Hosack,et al.  Recent advances in process synthesis for semiconductor devices , 1998 .

[17]  R. Eberhart,et al.  Fuzzy adaptive particle swarm optimization , 2001, Proceedings of the 2001 Congress on Evolutionary Computation (IEEE Cat. No.01TH8546).

[18]  Peter J. Angeline,et al.  Evolutionary Optimization Versus Particle Swarm Optimization: Philosophy and Performance Differences , 1998, Evolutionary Programming.

[19]  W. Luder.  Introduction to thermodynamics of irreversible processes , 1955 .