DocumentCode
1739215
Title
A comparison between various empirical models for TCAD purposes
Author
Govoreanu, B. ; Suykens, J. ; Schoenmaker, W. ; Amza, C. ; Dima, G. ; Vandewalle, J. ; Profirescu, M.
Author_Institution
IMEC, Leuven, Belgium
Volume
1
fYear
2000
fDate
2000
Firstpage
315
Abstract
In this paper we discuss some of the existing response surface modeling techniques currently used for TCAD purposes and bring to the readers´ attention a Bayesian framework for training feed-forward neural networks. We assess the model performances of different models by studying a 0.25 μm nMOS transistor. We show that the Bayesian learning neural network modeling may successfully replaces traditional models in particular cases
Keywords
Bayes methods; MOSFET; feedforward neural nets; learning (artificial intelligence); semiconductor device models; surface fitting; technology CAD (electronics); 0.25 micron; Bayesian learning model; NMOS transistor; TCAD; empirical model; feedforward neural network; response surface model; Bayesian methods; Computer errors; Feedforward neural networks; Feedforward systems; Least squares approximation; MOS devices; Neural networks; Response surface methodology; Software tools; Very large scale integration;
fLanguage
English
Publisher
ieee
Conference_Titel
Semiconductor Conference, 2000. CAS 2000 Proceedings. International
Conference_Location
Sinaia
Print_ISBN
0-7803-5885-6
Type
conf
DOI
10.1109/SMICND.2000.890244
Filename
890244
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