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An improved description of the dielectric breakdown in oxides based on a generalized Weibull distribution

Author

Listed:
  • Costa, U.M.S.
  • Freire, V.N.
  • Malacarne, L.C.
  • Mendes, R.S.
  • Picoli Jr., S.
  • de Vasconcelos, E.A.
  • da Silva Jr., E.F.

Abstract

In this work, we address modal parameter fluctuations in statistical distributions describing charge-to-breakdown (QBD) and/or time-to-breakdown (tBD) during the dielectric breakdown regime of ultra-thin oxides, which are of high interest for the advancement of electronic technology. We reobtain a generalized Weibull distribution (q-Weibull), which properly describes (tBD) data when oxide thickness fluctuations are present, in order to improve reliability assessment of ultra-thin oxides by time-to-breakdown (tBD) extrapolation and area scaling. The incorporation of fluctuations allows a physical interpretation of the q-Weibull distribution in connection with the Tsallis statistics. In support to our results, we analyze tBD data of SiO2-based MOS devices obtained experimentally and theoretically through a percolation model, demonstrating an advantageous description of the dielectric breakdown by the q-Weibull distribution.

Suggested Citation

  • Costa, U.M.S. & Freire, V.N. & Malacarne, L.C. & Mendes, R.S. & Picoli Jr., S. & de Vasconcelos, E.A. & da Silva Jr., E.F., 2006. "An improved description of the dielectric breakdown in oxides based on a generalized Weibull distribution," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 361(1), pages 209-215.
    • Handle: RePEc:eee:phsmap:v:361:y:2006:i:1:p:209-215
    DOI: 10.1016/j.physa.2005.07.017
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    Citations

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    Cited by:

    1. K. Jose & Shanoja Naik & Miroslav Ristić, 2010. "Marshall–Olkin q-Weibull distribution and max–min processes," Statistical Papers, Springer, vol. 51(4), pages 837-851, December.
    2. Nadarajah, Saralees & Kotz, Samuel, 2007. "On the q-type distributions," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 377(2), pages 465-468.
    3. Xiang Jia & Saralees Nadarajah & Bo Guo, 2020. "Inference on q-Weibull parameters," Statistical Papers, Springer, vol. 61(2), pages 575-593, April.
    4. Ewin Sánchez, 2023. "Q-Weibull distribution to explain the PM2.5 air pollution concentration in Santiago de Chile," The European Physical Journal B: Condensed Matter and Complex Systems, Springer;EDP Sciences, vol. 96(8), pages 1-8, August.
    5. Xu, Meng & Droguett, Enrique López & Lins, Isis Didier & das Chagas Moura, Márcio, 2017. "On the q-Weibull distribution for reliability applications: An adaptive hybrid artificial bee colony algorithm for parameter estimation," Reliability Engineering and System Safety, Elsevier, vol. 158(C), pages 93-105.
    6. Jose, K.K. & Naik, Shanoja R., 2008. "A class of asymmetric pathway distributions and an entropy interpretation," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 387(28), pages 6943-6951.
    7. Reyes-Santias, Francisco & Reboredo, Juan C. & de Assis, Edilson Machado & Rivera-Castro, Miguel A., 2021. "Does length of hospital stay reflect power-law behavior? A q-Weibull density approach," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 568(C).

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