Modeling the properties of PECVD silicon dioxide films using optimized back-propagation neural networks
- 1 June 1994
- journal article
- Published by Institute of Electrical and Electronics Engineers (IEEE) in IEEE Transactions on Components, Packaging, and Manufacturing Technology: Part A
- Vol. 17 (2) , 174-182
- https://doi.org/10.1109/95.296398
Abstract
No abstract availableThis publication has 10 references indexed in Scilit:
- Neural network-based modeling of the plasma-enhanced chemical vapor deposition of silicon dioxidePublished by Institute of Electrical and Electronics Engineers (IEEE) ,2002
- Estimation of generalization capability by combination of new information criterion and cross validationPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2002
- Advantages of plasma etch modeling using neural networks over statistical techniquesIEEE Transactions on Semiconductor Manufacturing, 1993
- Optical emission investigation of the plasma enhanced chemical vapor deposition of silicon oxide filmsJournal of Vacuum Science & Technology A, 1992
- Etch process characterization using neural network methodology: a case studyPublished by SPIE-Intl Soc Optical Eng ,1992
- Energy Considerations in the Deposition of High‐Quality Plasma‐Enhanced CVD Silicon DioxideJournal of the Electrochemical Society, 1991
- Statistical experimental design in plasma etch modelingIEEE Transactions on Semiconductor Manufacturing, 1991
- An information criterion for optimal neural network selectionIEEE Transactions on Neural Networks, 1991
- Stress in silicon dioxide films deposited using chemical vapor deposition techniques and the effect of annealing on these stressesJournal of Vacuum Science & Technology B, 1990
- An introduction to computing with neural netsIEEE ASSP Magazine, 1987