Utilizing Differential Evolution for an Automated Compact Model Parameter Extraction

Parameter extraction is a challenging task, as it searches for a solution inside a high dimensional plus non-convex space. To be able to apply well known gradient based optimizers, the problem is dissected into multiple simpler yet intertwined tasks, which yields a complex and manual labour intensive procedure. On the contrary to gradient based methods, genetic algorithms perform excellent on global search problems, which eliminates the need for such a sophisticated workflow. In this paper a highly automated methodology is presented that is capable of replacing the standard manual extraction sequence for the BSIM MOSFET compact model. Due to its superior extreme finding behaviour, the Differential Evolution algorithm is applied in combination with a special error metric to ensure a high fitting quality, in all regions of the output and transfer curves. Repeatably good results for 20k measurement points are obtained, with a reduction of factor 10 in total fitting duration, while coincidentally consuming mostly computation instead of manual labour time.