Semiconductor fabrication lines have become extremely costly, and achieving a good return from such a high capital investment requires efficient utilization of these expensive facilities. It is highly desirable to shorten processing development time, increase fabrication yield, enhance flexibility, improve quality, and minimize downtime. We propose that these ends can be achieved by applying recent advances in the areas of artificial neural networks, fuzzy logic, machine learning, and genetic algorithms. We use the term neural manufacturing to describe such applications. This paper describes our use of artificial neural networks to improve the monitoring and control of semiconductor process.

Date Published: 19 September 1995
PDF: 9 pages
Proc. SPIE 2637, Process, Equipment, and Materials Control in Integrated Circuit Manufacturing, (19 September 1995); doi: 10.1117/12.221322

Published in SPIE Proceedings Vol. 2637:
Process, Equipment, and Materials Control in Integrated Circuit Manufacturing
Anant G. Sabnis; Ivo J. Raaijmakers, Editor(s)