Applying machine learning to semiconductor manufacturing

Author

Irani, Keki B. ; Cheng, Jie ; Fayyad, Usama M. ; Qian, Zhaogang

Author_Institution

Dept. of Electr. Eng. & Comput. Sci., Michigan Univ., Ann Arbor, MI, USA

Volume

8

Issue

1

fYear

1993

Firstpage

41

Lastpage

47

Abstract

The generalized ID3 (GID3) algorithm, which takes a training set of experimental data and produces a decision tree that predicts the outcome of future experiments under various, more general conditions, is described. The tree can then be translated into a set of rules for an expert system. Two extensions to GID3MmRIST, and KARSM-that deal with the problems of noisy data and the limited availability of training data are discussed. The application of GID3 to reactive ion etching manufacturing process diagnosis and optimization and to knowledge acquisition for an expert system is described.<>

Keywords

computer integrated manufacturing; expert systems; knowledge acquisition; learning (artificial intelligence); GID3; KARSM; decision tree; expert system; generalized ID3; knowledge acquisition; machine learning; noisy data; optimization; reactive ion etching manufacturing process diagnosis; semiconductor manufacturing; training set; Decision trees; Diagnostic expert systems; Etching; Knowledge acquisition; Machine learning; Machine learning algorithms; Manufacturing processes; Semiconductor device manufacture; Semiconductor device noise; Training data;

fLanguage

English

Journal_Title

IEEE Expert

Publisher

ieee

ISSN

0885-9000

Type

jour

DOI

10.1109/64.193054

Filename

193054