Binning algorithm for accurate computer aided device modeling

Accurate modeling of devices is critical to efficient computer aided design and optimization. Commonly encountered modeling techniques include empirical formulae, equivalent circuits, and black-box models (eg. neural networks). Important criteria in device modeling are model accuracy, computational simplicity, generality of the modeling approach, and so forth. In this paper, we present a new and systematic CAD algorithm to device modeling based on a concept often referred to as binning. For a given set of data either from measurements or simulations, the proposed algorithm leads to an accurate model comprising of a set of sub-models with best possible accuracy, while keeping the model structure simple. The proposed algorithm is general and can be applied in the context of any black-box modeling technique. In this paper, the algorithm is illustrated for the case of neural network modeling. Resulting models are shown to exhibit relatively better accuracies compared to those developed using a standard modeling approach. Both active and passive modeling examples are presented.