Abstract
This work describes a new approach to C–V dopant profiling by means of nonlinear least squares inverse modeling. It is shown that a genetic algorithm can replace standard nonlinear minimization procedure in identification of doping profile parameters. The most important advantages of the genetic algorithm are its ability to avoid local minima and often faster convergence in ‘‘difficult’’ cases. Practical implementation of the genetic algorithm is described in detail, and experimental results are shown.
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