You have requested a machine translation of selected content from our databases. This functionality is provided solely for your convenience and is in no way intended to replace human translation. Neither SPIE nor the owners and publishers of the content make, and they explicitly disclaim, any express or implied representations or warranties of any kind, including, without limitation, representations and warranties as to the functionality of the translation feature or the accuracy or completeness of the translations.
Translations are not retained in our system. Your use of this feature and the translations is subject to all use restrictions contained in the Terms and Conditions of Use of the SPIE website.
6 November 2008Deep level defect structure of SiC material by local cluster neural network
Main goal of presented work was the construction of neural network for detection of deep defect centers in semiinsulating
materials. The element of novelty is the implementation of local cluster function combined with the leave-one-out method, used to determine the appropriate structure of neural net.
Tomasz Pichlak andStanislaw Jankowski
"Deep level defect structure of SiC material by local cluster neural network", Proc. SPIE 7124, Photonics Applications in Astronomy, Communications, Industry, and High-Energy Physics Experiments 2008, 712402 (6 November 2008); https://doi.org/10.1117/12.817929
The alert did not successfully save. Please try again later.
Tomasz Pichlak, Stanislaw Jankowski, "Deep level defect structure of SiC material by local cluster neural network," Proc. SPIE 7124, Photonics Applications in Astronomy, Communications, Industry, and High-Energy Physics Experiments 2008, 712402 (6 November 2008); https://doi.org/10.1117/12.817929