Modeling the properties of PECVD silicon dioxide films using neural networks

Seung-Soo Han; Martin Ceiler; Sue Ann Bidstrup Allen; Paul A. Kohl; Gary Stephen May

doi:10.1117/12.167348

15 February 1994 Modeling the properties of PECVD silicon dioxide films using neural networks

Seung-Soo Han, Martin Ceiler, Sue Ann Bidstrup Allen, Paul A. Kohl, Gary Stephen May

Proceedings Volume 2091, Microelectronic Processes, Sensors, and Controls; (1994) https://doi.org/10.1117/12.167348
Event: Microelectronic Processing '93, 1993, Monterey, CA, United States

Abstract

Silicon dioxide films deposited by plasma-enhanced chemical vapor deposition (PECVD) are useful as interlayer dielectric for metal-insulator structures such as MOS integrated circuits and multichip modules. The PECVD for SiO₂ in a SiH₄/N₂O gas mixture yields films with excellent physical properties. However, due to the complex nature of particle dynamics within the plasma, it is difficult to determine the exact nature of the relationship between film properties and controllable deposition conditions. Previous modeling techniques such as first principles or statistical response surface methods are limited in either efficiency or accuracy. In this study, PECVD modeling using neural networks has been introduced. Neural networks have been shown to exhibit superior performance in both accuracy and prediction capability compared to statistical models.

Citation Download Citation

Seung-Soo Han, Martin Ceiler, Sue Ann Bidstrup Allen, Paul A. Kohl, and Gary Stephen May "Modeling the properties of PECVD silicon dioxide films using neural networks", Proc. SPIE 2091, Microelectronic Processes, Sensors, and Controls, (15 February 1994); https://doi.org/10.1117/12.167348

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available