Fault diagnosis of plasma etch equipment

Author

Ison, Anna M. ; Li, Wei ; Spanos, Costas J.

Author_Institution

Dept. of Electr. Eng. & Comput. Sci., California Univ., Berkeley, CA, USA

fYear

1997

fDate

6-8 Oct 1997

Abstract

The development and implementation of robust methods for fault detection promises to enhance manufacturing by improving our capability to monitor equipment and processes. In order to fully utilize this capability it is important that the machine fault is not only detected, but also diagnosed as belonging to a fault category so that appropriate corrective action can promptly be taken. In this paper we examine the diagnostic performance of two probabilistic modeling techniques in using sensor signals to classify faults. We also discuss how the strengths of these models may be combined in a hierarchical architecture giving rise to a more powerful diagnostic tool

Keywords

decision theory; fault diagnosis; integrated circuit yield; prediction theory; probability; semiconductor process modelling; sputter etching; state estimation; state-space methods; statistical process control; decision tree; diagnostic performance; fault diagnosis; faults classification; gas ratio response; generalized linear models; hierarchical architecture; machine fault; machine state monitoring; plasma etch equipment; predictors; probabilistic modeling techniques; robust methods; sensor signals; signal selection; tree-based models; Circuit faults; Condition monitoring; Etching; Fault detection; Fault diagnosis; Manufacturing industries; Manufacturing processes; Plasma applications; Plasma diagnostics; Semiconductor device modeling;

fLanguage

English

Publisher

ieee

Conference_Titel

Semiconductor Manufacturing Conference Proceedings, 1997 IEEE International Symposium on

Conference_Location

San Francisco, CA

Print_ISBN

0-7803-3752-2

Type

conf

DOI

10.1109/ISSM.1997.664509

Filename

664509