Self-learning fuzzy modeling of semiconductor processing equipment

Author

Chen, Raymond L. ; Spanos, Costas J.

Author_Institution

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

fYear

1992

fDate

30 Sep-1 Oct 1992

Firstpage

100

Lastpage

106

Abstract

A qualitative equipment model for a low pressure chemical vapor deposition (LPCVD) process is presented. The model is based on fuzzy representation of input-output relationships and utilizes self-tuning membership functions. To demonstrate this concept a fuzzy inference system has been built for polysilicon grain size prediction based on deposition and annealing temperatures. After the system is trained with experimental data, it automatically tunes its membership functions to accommodate additional experimental data

Keywords

annealing; chemical vapour deposition; fuzzy logic; inference mechanisms; self-adjusting systems; semiconductor process modelling; semiconductor technology; annealing temperatures; fuzzy inference system; fuzzy representation; input-output relationships; low pressure chemical vapor deposition; polysilicon grain size prediction; qualitative equipment model; self-tuning membership functions; semiconductor processing equipment; Annealing; Chemical vapor deposition; Computer aided manufacturing; Fuzzy systems; Grain size; Predictive models; Rough surfaces; Semiconductor device manufacture; Semiconductor process modeling; Temperature;

fLanguage

English

Publisher

ieee

Conference_Titel

Advanced Semiconductor Manufacturing Conference and Workshop, 1992. ASMC 92 Proceedings. IEEE/SEMI 1992

Conference_Location

Cambridge, MA

Print_ISBN

0-7803-0740-2

Type

conf

DOI

10.1109/ASMC.1992.253846

Filename

253846