1 January 1992 Etch process characterization using neural network methodology: a case study
Author Affiliations +
Proceedings Volume 1594, Process Module Metrology, Control and Clustering; (1992); doi: 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.
© (1992) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Michael T. Mocella, James A. Bondur, 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); doi: 10.1117/12.56637; http://dx.doi.org/10.1117/12.56637
PROCEEDINGS
11 PAGES


SHARE
KEYWORDS
Neural networks

Data modeling

Process control

Etching

Metrology

Statistical analysis

Semiconducting wafers

Back to Top