Use of adaptive network fuzzy inference system to predict plasma charging damage on electrical MOSFET properties

Authors:
Byungwhan Kim;Hee Ju Kwon;Seongjin Choi
Affiliations:
Department of Electronic Engineering, Sejong University, Seoul 143-747, Republic of Korea;Department of Electronic Engineering, Sejong University, Seoul 143-747, Republic of Korea;Department of Electronics and Information Engineering, Korea University, 208 Seochang, Jochiwon 339-700, Republic of Korea
Venue:
Expert Systems with Applications: An International Journal
Year:
2009

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Abstract

A prediction model of plasma-induced charging damage is presented. The model was constructed using adaptive network fuzzy inference system (ANFIS). The prediction performance of ANFIS model was optimized as a function of training factors, including a step-size, a normalization factor, and type of membership function. Charging damage data were obtained from antenna-structured MOSFET with the variations in process parameters. For a systematic modeling, the experiment was characterized by means of a face-centered Box Wilson experiment. Electrical properties modeled include a threshold voltage (V), a subthreshold swing (S), and a transconductance (G). Both S and G were found to be considerably affected by the normalization factor. For the variations in the type of membership function, either V or S was the most significantly influenced. The optimized root mean square errors are about 0.041 (V), 5.040 (mV/decade), and 12.311 (x10^-^6/@W), respectively. Better predictions were demonstrated against statistical regression models and the improvements were even more than 15% for V and S models.