Abstract
Abstract In this study, two intelligent methodologies were used to estimate the anomalous threshold-voltage (V th ) measured behaviors in sub-micrometer Bipolar-CMOS-DMOS (BCD) low-voltage (LV) MOSFETs by using the grey system (GS) GM (1,1) model and a fuzzy-neural network (FNN). This paper describes the implementation procedures of these two models for making V th predictions. Moreover, discrepant comparisons between the GS and FNN output data are also demonstrated. Eventually, only the outputs of FNN can have the complex action of reverse short-channel property. Then, it will be developed to analyze the V th inclination of submicron n-channel MOSFETs due to the device geometric effect. A comparison between the measured characteristics of V th and the characteristics of V th predicted by the FNN shows good agreement for a wide range of channel lengths, widths and bias conditions. And, the maximum error percentage was less than 0.08%. As such, the developed procedure may be well suited for the data estimation of the complicated BSIM-model parameters in foundry fabrications.
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