A Floating-Point Based Evolutionary Algorithm for Model Parameters Extraction and Optimization in HBT Device Simulation

  • Yiming Li
  • Chuen-Tast Sun
  • Cheng-Kai Chen
Conference paper
Part of the Advances in Soft Computing book series (AINSC, volume 19)


In this paper, we extract the HBT model parameters with a computational intelligence algorithm for the optimal VLSI device characterization. For a specified VLSI circuit, the proposed method to solve the HBT equivalent model and to extract parameters is based on the monotone iterative (MI) method and the genetic algorithm (GA) with floating-point operators. First, a set of nonlinear equations is solved with the MI method, and the solved results are used for the optimization with the GA method. The iteration will be stopped when the self-consistent convergent solution is obtained. Our simulation results demonstrate this method has excellent convergent property and highly computational efficiency. The approach not only provides a novel alternative for optimal VLSI circuit and device design but also has many practical applications in nanodevice I-V characterization, RF circuit optimization and system-on-a-chip function design.


Genetic Algorithm Genetic Algorithm Method VLSI Circuit Dynamic Mutation Metal Oxide Semiconductor Field Effect Transistor 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2003

Authors and Affiliations

  • Yiming Li
    • 1
    • 2
  • Chuen-Tast Sun
    • 3
  • Cheng-Kai Chen
    • 3
  1. 1.National Nano Device LaboratoriesHsinchu 300Taiwan
  2. 2.Microelectronics and Information Systems Research CenterNational Chiao Tung UniversityHsinchu 300Taiwan
  3. 3.Department Computer and Information ScienceNational Chiao Tung UniversityHsinchu 300Taiwan

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