NDLI: Use of neural network to model the FTIR spectra of PECVD silicon nitride films for cardiovascular pressure sensor applications

Content Provider	IEEE Xplore Digital Library
Author	Thongvigitmanee, T. Titiroongruang, W. Srihapat, A. Poyai, A.
Copyright Year	2011
Description	Author affiliation: Department of Electrical Engineering, King Mongkut's Institute of Technology Ladkrabang Bangkok, Thailand (Thongvigitmanee, T.; Titiroongruang, W.) \|\| Thai Microelectronics Center, National Electronics and Computer Technology Center, Chachoengsao, Thailand (Srihapat, A.; Poyai, A.)
Abstract	In this paper, the empirical process models based on neural network are applied to discover the relationship between inputs and outputs of the plasma enhanced chemical deposition (PECVD) silicon nitride process. The design of experiments are based on a $2^{6–2}$ fractional factorial experiment with four center replicate on six factors which are 1) the SiH4 flow rate, 2) the NH3 flow rate, 3) the N2 flow rate, 4) the chamber pressure, 5) the radio frequency (RF) power/distance between the wafer base and shower gas, and 6) the deposition temperature. Once these experiments are performed, different neural networks are applied to identify these six inputs to the chemical bonding information from the FTIR measurements. The best performances of neural networks for each response are selected based on the smallest prediction error. Then the three-dimensional surface plots are generated to qualitatively interpret factor effects. The corrosion testing in saline solution based on a potentiostatic measurement of an aluminum film with the protective PECVD silicon nitride is demonstrated. This measurement could be used as a tool to identify the best dense PECVD silicon nitride film in order to improve the protective performance of the existing recipe. The silicon nitride film is used as the final passivation layer of the cardiovascular pressure sensor.
Starting Page	1
Ending Page	5
File Size	348598
Page Count	5
File Format	PDF
ISBN	9781612844084
ISSN	10788743
e-ISBN	9781612844091
DOI	10.1109/ASMC.2011.5898186
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2011-05-16
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Artificial neural networks Silicon Films Semiconductor device measurement Bonding Substrates Neurons FTIR Modeling neural networks plasma-enhanced chemical vapor deposition (PECVD) silicon nitride film
Content Type	Text
Resource Type	Article
Subject	Industrial and Manufacturing Engineering Electronic, Optical and Magnetic Materials Electrical and Electronic Engineering

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