Use of adaptive network fuzzy inference system to predict plasma charging damage on electrical MOSFET properties
- Authors
- Kim, Byungwhan; Kwon, Hee Ju; Choi, Seongjin
- Issue Date
- 4월-2009
- Publisher
- PERGAMON-ELSEVIER SCIENCE LTD
- Keywords
- Charging damage; Metal-oxide-semiconcluctor field-effect transistors; Adaptive network fuzzy inference system; Plasma
- Citation
- EXPERT SYSTEMS WITH APPLICATIONS, v.36, no.3, pp.6570 - 6573
- Indexed
- SCIE
SCOPUS
- Journal Title
- EXPERT SYSTEMS WITH APPLICATIONS
- Volume
- 36
- Number
- 3
- Start Page
- 6570
- End Page
- 6573
- URI
- https://scholar.korea.ac.kr/handle/2021.sw.korea/120330
- DOI
- 10.1016/j.eswa.2008.07.034
- ISSN
- 0957-4174
- 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 (x 10(-6)/Omega), respectively. Better predictions were demonstrated against statistical regression models and the improvements were even more than 15% for V and S models. (C) 2008 Elsevier Ltd. All rights reserved.
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