Evolutionary methods to analogue electronic circuits yield optimization

• Autorzy: Jantos P. , Rutkowski J.
• Języki publikacji: EN

Abstrakty

EN

Evolutionary computing and algorithms are well known tools of optimization that are utilized for various areas of analogue electronic circuits design and diagnosis. This paper presents the possibility of using two evolutionary algorithms - genetic algorithm and evolutionary strategies - for the purpose of analogue circuits yield and cost optimization. Terms: technologic and parametric yield are defined. Procedures of parametric yield optimization, such as a design centring, a design tolerancing, a design centring with tolerancing, are introduced. Basics of genetic algorithm and evolutionary strategies are presented, differences between these two algorithms are high-lighted, certain aspects of implementation are discussed. Effectiveness of both algorithms in parametric yield optimization has been tested on several examples and results have been presented. A share of evolutionary algorithms computation cost in a total optimization cost is analyzed.

Słowa kluczowe

EN

evolutionary algorithms; parametric yield; production cost; optimization; computation cost

• Wydawca: Polska Akademia Nauk, Wydział IV Nauk Technicznych
• Czasopismo: Bulletin of the Polish Academy of Sciences. Technical Sciences
• Rocznik: 2008
• Tom: Vol. 56, nr 1
• Strony: 9--16
• Opis fizyczny: Bibliogr. 8 poz., rys., tab.

Twórcy

autor

Jantos P.

autor

Rutkowski J.

• Faculty of Automatic Control, Electronics and Computer Science, Silesian Technical University, 16 Akademicka St., 44-100 Gliwice, Poland,

Bibliografia

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• [2] H. Tian and L. Milor, "Yield optimization for integrated circuits", ISR TR 92-103, http://hdl.handle.net/1903/5284 (1992).
• [3] L. Zielinski and J. Rutkowski, "Design tolerancing with utilization of gene expression programming and genetic algorithm", Int. Conf. Signals and Electronic Systems ICSES, 13-15 (2004).
• [4] T. Back, D.B. Fogel, and Z. Michalewicz, Evolutionary Computation I - Basic Algorithms and Operators, IOP Publishing Ltd, Bristol, 2000.
• [5] T. Back, D.B. Fogel, and Z. Michalewicz, Evolutionary Computation II - Advanced Algorithms and Operators, IOP Publishing Ltd, Bristol, 2000.
• [6] D. Quagliarella, J. Periaux, C. Poloni, and G. Winter, Genetic Algorithms and Evolutionary Strategies in Engineering and Computer Science - Recent Advances and Industrial Applications, John Wiley & Sons Ltd, London, 1998.
• [7] R. L. Haupt and S.E. Haupt, Practical Genetic Algorithms, John Wiley & Sons Ltd, London, 1998.
• [8] D. Whitley, "An overview of evolutionary algorithms: practical issues and common pitfalls", Information and Software Technology 43 (14), 817-831 (2001).