EDP Sciences logo
Submit your paper
Search
Menu
Home All issues Volume 10 (2019) Int. J. Simul. Multidisci. Des. Optim., 10 (2019) A3 References
Open Access
Issue
Int. J. Simul. Multidisci. Des. Optim.
Volume 10, 2019
Article Number A3
Number of page(s) 16
DOI https://doi.org/10.1051/smdo/2019002
Published online 15 March 2019
  1. M. Nubli Zulkifli, Z. Azhar Zahid Jamal, G. Abdul Quadir, Temperature cycling analysis for ball grid array package using finite element analysis, Microelectron. Int. 28, 17–28 (2011) [CrossRef] [Google Scholar]
  2. W.D. Van Driel, H.P. Hochstenbach, G.Q. Zhang, Design for reliability of wafer level packages, in: EuroSime 2006–7th International Conference on Thermal, Mechanical and Multiphysics Simulation and Experiments in Micro-Electronics and Micro-Systems, 2006 (IEEE, NY, 2016) pp. 1–6 [Google Scholar]
  3. M.M. Hossain, S.G. Jagarkal, D. Agonafer, M. Lulu, S. Reh, Design optimization and reliability of PWB level electronic package, J. Electron. Packaging 129, 9–18 (2007) [CrossRef] [Google Scholar]
  4. Y. Aoues, A. Makhloufi, P. Pougnet, A. El Hami, Probabilistic assessment of thermal fatigue of solder joints in mechatronic packaging, in: Proceedings of the 1st International Symposium on Uncertainty Quantification and Stochastic Modeling, Maresias, SP, Brazil, 2012 [Google Scholar]
  5. N. Saadoune, B. Radi, Probabilistic study of an embedded system, in: 2016 4th IEEE International Colloquium on Information Science and Technology (CiSt) (IEEE, NY, 2016), pp. 756–761 [CrossRef] [Google Scholar]
  6. Standard, JEDEC, JESD22-A104-B, Temperature Cycling, July, 2000 [Google Scholar]
  7. A. El Hami, P. Pougnet, Embedded mechatronic systems (Elsevier, Amsterdam, 2015), Vol. 2 [Google Scholar]
  8. A. El Hami, B. Radi, Uncertainty and optimization in structural mechanics (John Wiley & Sons, NJ, 2013) [Google Scholar]
  9. R. El Maani, A. Makhloufi, B. Radi, A. El Hami, Reliability-based design optimization with frequency constraints using a new safest point approach, Eng. Optim. 1–18 (2018) [Google Scholar]
  10. O. Kramer, A brief introduction to continuous evolutionary optimization (Springer, Berlin, 2014) [CrossRef] [Google Scholar]
  11. T. Bäck, C. Foussette, P. Krause, Contemporary evolution strategies (Springer, Berlin, Heidelberg, 2013) [CrossRef] [Google Scholar]
  12. Y. Jin, B. Sendhoff, Fitness approximation in evolutionary computation − a survey, in: Proceedings of the 4th Annual Conference on Genetic and Evolutionary Computation (Morgan Kaufmann Publishers Inc, CA, 2000), pp. 1105–1112 [Google Scholar]
  13. Y. Jin, A comprehensive survey of fitness approximation in evolutionary computation, Soft Comput. 9, 3–12 (2005) [CrossRef] [Google Scholar]
  14. H. Hamdani, B. Radi, A. El Hami, Métamodélisation pour une conception robuste des systèmes mécatroniques, Incertitudes et fiabilité des systèmes multiphysiques 2, 10 (2017) [Google Scholar]
  15. N. Hansen, The CMA evolution strategy: a tutorial, 2016. arXiv:1604.00772 [Google Scholar]
  16. G. Matheron, Principles of geostatistics, Econ. Geol. 58, 1246–1266 (1963) [CrossRef] [Google Scholar]
  17. D.G. Krige, A statistical approach to some basic mine valuation problems on the Witwatersrand, J. Southern Afr. Inst. Min. Metall. 52, 119–139 (1951) [Google Scholar]
  18. J. Sacks, W.J. Welch, T.J. Mitchell, H.P. Wynn, Design and analysis of computer experiments, Stat. Sci. 409–423 (1989) [CrossRef] [MathSciNet] [Google Scholar]
  19. C.E. Rasmussen, C.K. Williams, Gaussian processes for machine learning (MIT Press, Cambridge, 2006), Vol. 1 [Google Scholar]
  20. B. Sudret, Meta-models for structural reliability and uncertainty quantification, 2012. arXiv:1203.2062 [Google Scholar]
  21. Y. Jin, Surrogate-assisted evolutionary computation: recent advances and future challenges, Swarm Evol. Comput. 1, 61–70 (2011) [CrossRef] [Google Scholar]
  22. Y. Jin, M. Olhofer, B. Sendhoff, On evolutionary optimization with approximate fitness functions, in: Proceedings of the 2nd Annual Conference on Genetic and Evolutionary Computation (Morgan Kaufmann Publishers Inc, CA, 2000), pp. 786–793 [Google Scholar]
  23. Y. Jin, M. Hüsken, B. Sendhoff, Quality measures for approximate models in evolutionary computation, GECCO 170–173 (2003) [Google Scholar]
  24. T.M. Runarsson, Constrained evolutionary optimization by approximate ranking and surrogate models, in: International Conference on Parallel Problem Solving from Nature (Springer, Berlin, 2004), pp. 401–410 [Google Scholar]
  25. C. Huang, Kriging-assisted evolution strategy for optimization and application in material parameters identification, PhD thesis, Rouen, INSA, 2017 [Google Scholar]
  26. C. Huang, B. Radi, A. El Hami, H. Bai, CMA evolution strategy assisted by kriging model and approximate ranking, Appl. Intell. 1–17 (2018) [Google Scholar]
  27. S. Kern, N. Hansen, P. Koumoutsakos, Local meta-models for optimization using evolution strategies , PPSN (Springer, Berlin, 2006) pp. 939–948 [Google Scholar]
  28. J. Branke, C. Schmidt, Faster convergence by means of fitness estimation, Soft Comput. 9, 13–17 (2005) [CrossRef] [Google Scholar]
  29. A.C. Rencher, Methods of multivariate analysis (John Wiley & Sons, NJ, 2003), Vol. 2 [Google Scholar]
  30. A. El Hami, B. Radi, Comparison study of different reliability-based design optimization approaches, Adv. Mater. Res. 274, 113–121 (2011) [CrossRef] [Google Scholar]
  31. M. Grieu, Étude de la fatigue des joints brasés de composants électroniques soumis á des sollicitations thermomécaniques, vibratoires et combinées, PhD thesis, École Nationale Supérieure des Mines de Paris, 2010 [Google Scholar]
  32. ANSYS Guide, ANSYS Structural Analysis Guide, 2016 [Google Scholar]
  33. L. Anand, Constitutive equations for the rate-dependent deformation of metals at elevated temperatures, J. Eng. Mater. Technol. 104, 12– 17 (1982) [CrossRef] [Google Scholar]
  34. G.Z. Wang, Z.N. Cheng, K. Becker, J. Wilde, Applying Anand model to represent the viscoplastic deformation behavior of solder alloys, J. Electron. Packaging 123, 247–253 (2001) [CrossRef] [Google Scholar]
  35. W. Lee, L. Nguyen, G.S. Selvaduray, Solder joint fatigue models: review and applicability to chip scale packages, Microelectron. Reliab. 40, 231–244 (2000) [CrossRef] [Google Scholar]
  36. C. Kanchanomai, Y. Miyashita, Y. Mutoh, Low cycle fatigue behavior and mechanisms of a eutectic Sn-Pb solder 63Sn/37Pb, Int. J. Fatigue 24, 671–683 (2002) [CrossRef] [Google Scholar]
  37. C. Kanchanomai, Y. Miyashita, Y. Yoshiharu, Low-cycle fatigue behavior of Sn-Ag, Sn-Ag-Cu, and Sn-Ag-Cu-Bi lead-free solders, J. Electron. Mater. 31, 456–465 (2002) [CrossRef] [Google Scholar]

Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.

Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.

Initial download of the metrics may take a while.