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Stimulus with limited band optimization for analogue circuit testing

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Języki publikacji
EN
Abstrakty
EN
The paper presents an analogue circuit testing method that engages the analysis of the time response to a nonperiodic stimulus specialized for the verification of selected specifications. The decision about the current circuit diagnostic state depends on an amplitude spectrum decomposition of the time response measured during the test. A shape of the test excitation spectrum is optimized with the use of a differential evolution algorithm and it allows for achieving maximum fault coverage and the optimal conditions for fault isolation. Genotypes of the evolutionary system encode the amplitude spectrum of candidates for testing stimuli by means of rectangle frequency windows with amplitudes determined evolutionarily.
Rocznik
Strony
73--84
Opis fizyczny
Bibliogr. 20 poz., rys., tab., wykr.
Twórcy
autor
autor
autor
  • Silesian University of Technology, Faculty Of Automatic Control, Electronics And Computer Science, Institute of Electronics, Akademicka 16, 44-100 Gliwice, Poland, tgolonek@polsl.pl
Bibliografia
  • [1] Milor, L.S. (1998). A Tutorial Introduction to Research on Analog and Mixed-Signal Circuit Testing. IEEE Trans. on Cir. and Syst.-II, Analog and Digital Signals Processing, 45(10), 1389-1407.
  • [2] Załęski, D., Zielonko, R., Bartosiński, B. (2010). Application of Complementary Signals in Built-In Self Testers for Mixed-Signal Embedded Electronic Systems. IEEE Trans. on Inst. and Measure., 59(2), 345-352.
  • [3] Tadeusiewicz, M., Hałgas, S. (2009). Multiple catastrophic fault diagnosis of linear circuits considering the component tolerances. In Proc. ECCTD, 647-650.
  • [4] Bartosiński, B., Toczek, W. (2003). Some methods of diagnosis of analog circuits using mixed signal test bus IEEE 1149.4. Metrology and Measurement Systems, 10(2), 157-172.
  • [5] Chalk, C., Zwoliński, M. (1997). A Design for Test Technique to Increase the Resolution of Analogue Supply Current Tests. Electronic Letters, 33(21).
  • [6] Jantos, P., Golonek, T., Rutkowski, J. (2011). An Analogue Electronic Circuits Specification Driven Testing with the use of Time Domain Response’s Features. In Proc. Mixed Design of Integrated Circuits and Systems, MIXDES.
  • [7] Jantos, P., Grzechca, D., Rutkowski, J. (2009). Global Parametric Faults Identification in Analogue Electronic Circuits. Metrology and Measurement Systems, 16(3), 391-402.
  • [8] Alippi, C., Catelani, M., Fort, A., Mugnaini, M. (2005). Automated Selection of Test Frequencies for Fault Diagnosis in Analog Electronic Circuits. IEEE Trans. On Instr. and Measur., 54(3).
  • [9] Tadeusiewicz, M., Hałgas, S. (2010). A method for fast simulation of multiple catastrophic faults in analogue circuits. International Journal of Circuit Theory and Applications, 38(3), 275-290.
  • [10] Jantos, P., Grzechca, D., Rutkowski, J. (2010). An analogue integrated circuits yield optimisation with the use of genetic algorithm. In Proc. International Conference on Signal and Electronic Systems, ICSES, 293-296.
  • [11] Korzybski, M. (2008). Dictionary method for multiple soft and catastrophic fault diagnosis based on evolutionary computation. In Proc. International Conference on Signal and Electronic Systems, 553-556.
  • [12] Golonek, T., Rutkowski, J. (2007). Genetic-Algorithm-Based Method for Optimal Analog Test Points Selection. IEEE Trans. on Cir. and Syst.-II., 54(2), 117-121.
  • [13] Golonek, T., Jantos, P., Rutkowski, J. (2011). The Use of Stimulus with Limited Band for Analogue Circuit Testing. The National Electronics Conference, 884-889. (in Polish)
  • [14] Grzechca, D. (2011). Soft Fault Clustering in Analog Electronic Circuits with the Use of Self Organizing Neural Network. Metrology and Measurement Systems, 18(4), 555-568.
  • [15] Pułka, A. (2011). Two Heuristic Algorithms for Test Point Selection in Analog Circuits Diagnoses. Metrology and Measurement Systems, 18(1), 115-128.
  • [16] Price, K., Storn, R.M., Lampinen, J.A. (2005). Differential Evolution - A Practical Approach to Global Optimization. Springer.
  • [17] Goldberg, D.E. (1989). Genetic Algorithms in Search / Optimization and Machine Learning. Addison Wesley.
  • [18] Michalewicz, Z. (1996). Genetic Algorithms+Data Structures=Evolution Programs. Springer-Verlag.
  • [19] Koza, J.R. (1992). Genetic Programming: on the programming of computers by means of natural selection. MIT Press.
  • [20] UAF42 design software. http://focus.ti.com/docs/prod/folders/print/uaf42.html.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-article-BSW1-0090-0006
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