Extracting an optimal set of parameter values for an FET device model is a complex problem. The final model must not only describe the performance of a set of hardware to an acceptable level of accuracy, but must satisfy criteria outside the deviceís allowed operating regime to ensure robust convergence properties during simulation. Traditional methods of parameter extraction which rely on gradient techniques can produce far-from-optimal solutions because of the presence of local optima in the solution space. As a result, parameter extraction has traditionally been more art than science, requiring several iterations by an experienced engineer. Genetic algorithms are well-suited for finding near-optimal solutions in irregular parameter spaces. We have applied a genetic algorithm to the problem of device model parameter extraction and are able to produce models of superior accuracy in much less time and with less reliance on human expertise.
Journal: TechConnect Briefs
Volume: Technical Proceedings of the 1999 International Conference on Modeling and Simulation of Microsystems
Published: April 19, 1999
Pages: 176 - 179
Industry sector: Sensors, MEMS, Electronics
Topic: Modeling & Simulation of Microsystems