Etch process characterization using neural network methodology: a case study

Michael T. Mocella; James A. Bondur; Terry R. Turner

doi:10.1117/12.56637

1 January 1992 Etch process characterization using neural network methodology: a case study

Michael T. Mocella, James A. Bondur, Terry R. Turner

Proceedings Volume 1594, Process Module Metrology, Control and Clustering; (1992) https://doi.org/10.1117/12.56637
Event: Microelectronic Processing Integration, 1991, San Jose, CA, United States

Abstract

Polysilicon etching in a single-wafer, parallel-plate, magnetically- enhanced RIE tool has been examined using two different approaches to the non-physical modeling of the system characteristics. The behavior of both process responses (polysilicon and oxide etch rates) and plasma parameters (voltage and current metrics) have been examined as a function of five variables (rf power, pressure, magnetic field, gas flow rate, and He backside cooling). The variable-response mapping was examined using both neural network and response surface approaches. The greater fitting power of the former method is demonstrated in a side-by-side, internally consistent comparison of the same data set using these two approaches.

Citation Download Citation

Michael T. Mocella, James A. Bondur, and Terry R. Turner "Etch process characterization using neural network methodology: a case study", Proc. SPIE 1594, Process Module Metrology, Control and Clustering, (1 January 1992); https://doi.org/10.1117/12.56637

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