Numerous physical models have been developed in order to describe the physical and chemical processes constituting the optical microlithography process. Many of these models depend on parameters that have to be calibrated against experimental data. An optimization routine using a genetic algorithm (GA) proved a feasible approach in order to find adequate model parameters. However, the high computation time and the need for a better reproducibility of the results suggest improvements of this approach. In this chapter we show that the application of the proposed memetic algorithm (MA) to the calibration of photoresist parameters is suited to improve both the convergence behavior and the reproducibility of results. As a GA is a model of Darwinian natural evolution, so can an MA be qualified as a model of cultural evolution. An MA can be characterized as a combination of interactive and individual search of a given population. From this general model, a variety of implementations can be derived. In this chapter, we will present a hybrid MA employing a GA and a method from the field of mathematical constrained optimization, the sequential quadratic programming (SQP) algorithm.
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References
Tollkühn, B., Fühner, T., Matiut, D., Erdmann, A., Semmler, A., Küchler, B., Kókai, G.: Will Darwin’s law help us to improve our resist models? (2003)
Spellucci, P.: Numerische Verfahren der nichtlinearen Optimierung. Birkhäuser, Basel (1993)
Erdmann, A.: Simulation of optical lithography. In: Optics and Optoelectronics, Theory, Devices and Applications. Volume 2. Narosa (1998) p. 1271
Tollkühn, B., Erdmann, A., Lammers, J., Nölscher, C.: Do we need complex resist models for predictive simulation of lithographic process performance? Proc. SPIE 5376 (2004)
Dill, F., Hornberger, W., Hauge, P., Shaw, J.: Characterization of positive photoresist. In: IEEE Transactions on Electron Devices. 22(7) (1975) p. 445
Moscato, P.: On evolution, search, optimization, genetic algorithms and martial arts: To-wards memetic algorithms. Technical Report C3P 826, Caltech Concurrent Computation Program, California Institute of Technology, Pasadena, CA (1989)
Cangelosi, A., Parisi, D.: The emergence of a ‘language’ in an evolving population of neural networks. Connection Science 10(2) (1998) pp. 83-97
Dawkins, R.: Viruses of the mind. In: Dennett and His Critics: Demystifying Mind. Blackwell, Cambridge, MA (1993)
Dawkins, R.: The Selfish Gene. Clarendon, Oxford (1976)
Krasnogor, N.: Studies on the Theory and Design Space of Memetic Algorithms. Ph.d. thesis, University of the West of England, Bristol (2002)
Goldberg, D.E., Voessner, S.: Optimizing global-local search hybrids. In Banzhaf, W., Daida, J., Eiben, A.E., Garzon, M.H., Honavar, V., Jakiela, M., Smith, R.E., eds.: Pro-ceedings of the Genetic and Evolutionary Computation Conference. Volume 1., Orlando, FL, USA, Morgan Kaufmann, Los Altos (1999) pp. 220-228
Areibi, S., Yang, Z.: Effective memetic algorithms for vlsi design = genetic algorithms + local search + multi-level clustering. Evolutionary Computation 12(3) (2004) pp. 327-353
Ong, Y.S., Keane, A.J.: Meta-lamarckian learning in memetic algorithms. IEEE Transactions On Evolutionary Computation 8 (2004) pp. 99-110
Hart, W.E.: Locally-adaptive and memetic evolutionary pattern search algorithms. Evolutionary Computation 11(1) (2003) pp. 29-51
Rudolph, G.: Convergence analysis of canonical genetic algorithms. IEEE Transactions on Neural Networks 5(1) (1994) pp. 96-101
Ishibuchi, H., Yoshida, T., Murata, T.: Balance between genetic search and local search in memetic algorithms for multiobjective permutation flowshop scheduling. IEEE Trans-actions on Evolutionary Computation 7(2) (2003) pp. 204-223
Fühner, T., Jung, T.: Use of genetic algorithms for the development and optimization of crystal growth processes. Journal of Crystal Growth 266(1-3) (2004) pp. 229-238
Fühner, T., Erdmann, A., Farkas, R., Tollkühn, B., Kókai, G.: Genetic algorithms to im-prove mask and illumination geometries in lithographic imaging systems. In Raidl, G.R., et al., eds.: Applications of Evolutionary Computing, EvoWorkshops2004. Volume 3005 of LNCS., Coimbra, Portugal, Springer, Berlin Heidelberg New York (2004) pp. 208-218
Fühner, T., Erdmann, A., Ortiz, C.J., Lorenz, J.: Genetic algorithm for optimization and calibration in process simulation. In: Proceedings of International Conference on Simulation of Semiconductor Processes and Devices. (2004) pp. 347-350
Goldberg, D.E.: Genetic Algorithms in Search, Optimization and Machine Learning. Addison Wesley, Reading MA (1989)
Gray, F.: Pulse Code Communication. United States Patent Number 2632058 (1953)
Harik, G.R.: Finding Multimodal Solutions Using Restricted Tournament Selection. In Eshelman, L., ed.: Proceedings of the Sixth International Conference on Genetic Algo-rithms, San Francisco, CA, Morgan Kaufmann, Los Altos (1995) pp. 24-31
De Jong, K.A.: An Analysis of the Behavior of a Class of Genetic Adaptive Systems. PhD thesis, University of Michigan (1975)
Gill, P.E., Murray, W., Saunders, M.A., Wright, M.H.: Constrained nonlinear programming. In: Optimization. North-Holland, Amsterdam (1989) pp. 171-210
Fletcher, R.: Practical Methods of Optimization. 2 edn. Wiley, New York (1987)
Boggs, P.T., Tolle, J.W.: Sequential quadratic programming. In: Acta Numerica. (1995) pp. 1-51
Boggs, P.T., Tolle, J.W., Wang, P.: On the local convergence of quasi-Newton methods for constrained optimization. SIAM Journal of Control Optimisation 20 (1982) pp. 161-171
Dennis, Jr., J.E., Moré, J.J.: Quasi-Newton methods, motivation and theory. SIAM Review 19 (1977) pp. 46-89
Gill, P.E., Murray, W., Saunders, M.A., Wright, M.H.: User’s guide for npsol (version 4.0): A fortran package for nonlinear programming, technical report sol 2 86-2. Technical report, Department of Operations Research, Stanford University (1986)
Schittkowski, K.: On the convergence of a sequential quadratic programming method with an augmented Lagrangian line search function. Mathematishe Operations Forschung und Statistik, Series in Optimization 14 (1983) pp. 193-216
Boggs, P.T., Domich, P.D., Rogers, J.E.: An interior-point method for general large scale quadratic programming problems. Annals of Operations Research 62 (1996) pp. 419-438
Vanderbei, R.J., Carpenter, T.J.: Symmetric indefinite systems for interior point methods. Mathematical Programming 58 (1993) pp. 1-32
Schwefel, H.P.: Numerical optimization of computer models. Wiley, New York (1981)
Whitley, D., Mathias, K., Rana, S., Dzubera, J.: Building better test functions. In Eshel-man, L., ed.: Proceedings of the Sixth International Conference on Genetic Algorithms, San Francisco, CA, Morgan Kaufmann, Los Altos (1995) pp. 239-246
Ackley, D.H.: A Connectionist Machine for Genetic Hillclimbing. Kluwer, Boston, MA (1987)
Spellucci, P.: An SQP method for general nonlinear programs using only equality con-strained subproblems. Mathematical Programming 82 (1998) pp. 413-448
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Dür, C., Fühner, T., Tollkühn, B., Erdmann, A., Kókai, G. (2007). Memetic Algorithms Parametric Optimization for Microlithography. In: Abraham, A., Grosan, C., Ishibuchi, H. (eds) Hybrid Evolutionary Algorithms. Studies in Computational Intelligence, vol 75. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-73297-6_9
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