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Reactive ion etch modeling using neural networks and simulated annealing

Content Provider	IEEE Xplore Digital Library
Author	Kim, B. May, G.S.
Copyright Year	1996
Abstract	Silicon dioxide films are useful as interlayer dielectrics for integrated circuits and multichip modules (MCM's), and reactive ion etching (RIE) in RF glow discharges is a popular method for forming via holes in SiO/sub 2/ between metal layers of an MCM. However, precise modeling of RIE is difficult due to the extremely complex nature of particle dynamics within a plasma. Recently, empirical RIE models derived from neural networks have been shown to offer advantages in both accuracy and robustness over more traditional statistical approaches. In this paper, a new learning rule for training back-propagation neural networks is introduced and compared to the standard generalized delta rule. This new rule quantifies network memory during training and reduces network disorder gradually over time using an approach similar to simulated annealing. The modified neural networks are used to build models of etch rate, anisotropy, uniformity, and selectivity for SiO/sub 2/ films etched in a chloroform and oxygen plasma. Network training data was obtained from a 2/sup 4/ factorial experiment designed to characterize etch variation with RF power, pressure, and gas composition. Etching took place in a Plasma Therm 700 series RIE system. Excellent agreement between model predictions and measured data was obtained.
Sponsorship	ESD Association
Starting Page	3
Ending Page	8
Page Count	6
File Size	733602
File Format	PDF
ISSN	10834400
Volume Number	19
Issue Number	1
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE) Components, Packaging, and Manufacturing Technology society and the IEEE TAB Steering Committee
Publisher Date	1996-01-01
Publisher Place	U.S.A.
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Etching Neural networks Plasma applications Plasma measurements Radio frequency Circuit simulation Silicon compounds Semiconductor films Dielectrics Radiofrequency integrated circuits
Content Type	Text
Resource Type	Article
Subject	Engineering

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