Comprehensive and predictive modeling of submicron devices using the traditional TCAD EDA tools of device simulation has\nbecome increasingly perplexing due to a lack of reliable models and difficulties in calibrating available device models. This paper\nproposes a new technique to model BCD submicron pMOSFET devices and to predict device behaviors under different bias\nconditions and different geometry dimensions by using the adaptive neurofuzzy inference system (ANFIS), which combines fuzzy\ntheory and adaptive neuronet working. Here, the power of using ANFIS to realize the I-V behaviors is demonstrated in these p channel\nMOS transistors. After a systematic evaluation, it can be found that the predicting results of I-V behaviors of complicated\nsub micron pMOSFETs by ANFIS are compared with the actual diagnostic experiment data, and a good agreement has been\nobtained. Furthermore, the error percentage was no greater than 2.5%. As such, the demonstrated benefits of this new proposed\ntechnique include precise prediction and easier implementation.
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