Y. Li and Y.-Y. Cho (Taiwan)
Monotone iterative, Genetic algorithm, Deep-submicron MOSFET, Parameter extraction, Characteristics simulation
In this paper, we apply a novel genetic algorithm (GA) in parameter extraction and characteristic optimization for deep-submicron Metal-Oxide-Semiconductor Fiend Effect Transistors (MOSFETs). For a specified equivalent BSIM model of MOSFET, the proposed solution technique is based on the monotone iterative method and the genetic algorithm with floating-point operators. First, a set of nonlinear equations is solved with the MI method, and the result obtained is optimized with the GA method. The iteration of characterization process is stopped when a global self-consistent solution is obtained. This simulation methodology has been successfully implemented with a window-based interactive environment. It provides a user-friendly interface for practical engineering applications. Compared with measured data, our result shows good accuracy for different dimensional MOSFETs. For all simulations, this method has robust convergent property and computational efficiency. This characterization technique can also apply to advanced nanoscale devices.
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