Defect spatial pattern recognition using a hybrid SOM–SVM approach in semiconductor manufacturing

Author:

Li Te-Sheng,Huang Cheng-Lung

Publisher

Elsevier BV

Subject

Artificial Intelligence,Computer Science Applications,General Engineering

Reference25 articles.

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2. Chang, C. C., & Lin, C. J. (2001). LIBSVM: A library for support vector machines. Software available from http://www.csie.ntu.edu.tw/~cjlin/libsvm.

3. A neural-network approach to recognize defect spatial pattern in semiconductor fabrication;Chen;IEEE Transactions on Semiconductor Manufacturing,2000

4. Data mining for yield enhancement in semiconductor manufacturing and an empirical study;Chien;Expert Systems with Applications,2007

5. Duvivier, F. (1999). Automatic detection of spatial signature on wafermaps in a high volume production. In Proceedings of the 14th international symposium on defect and fault-tolerance in VLSI systems, Albuquerque, NM, USA (pp.61–67).

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