Effects of random number generations on intelligent semiconductor device model parameter extraction

Yi-Ming Li*

*Corresponding author for this work

Research output: Contribution to journalConference articlepeer-review


In this work, we experimentally compare the effect of random number generations on the performance of semiconductor device model parameter extraction. Based upon the genetic algorithm, the neural network and the Levenberg-Marquardt method, the prototype of parameter extraction has been developed in our earlier work. Property of the evolutionary technique is further advanced by implementing eight different random number generation schemes, where convergent behavior is compared. For both extraction cases of single and multiple nanoscale devices, the chaotic random number generator possesses superior convergence behavior than other random number generation methods. It generates the random numbers with better distribution which keeps the high diversity of the extraction system, thus the best performance of the convergence score is reached.

Original languageAmerican English
Pages (from-to)265-271
Number of pages7
JournalAIP Conference Proceedings
StatePublished - 25 Mar 2009
Event6th International Conference on Computational Methods in Science and Engineering, ICCMSE 2008 - Hersonissos, Crete, Greece
Duration: 25 Sep 200830 Sep 2008


  • Chaotic random number generator
  • Device model parameter extraction
  • Genetic algorithm
  • Intelligent methodology
  • Random number


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