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Self-learning fuzzy modeling of semiconductor processing equipment

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2 Author(s)
Chen, R.L. ; Dept. of Electr. Eng. & Comput. Sci., California Univ., Berkeley, CA, USA ; Spanos, Costas J.

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

Published in:

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

Date of Conference:

30 Sep-1 Oct 1992