Inspection of IC leadframes using an unsupervised neural network

Author

Lee, C.K. ; Chung, C.H.

Author_Institution

Dept. of Electron. Eng., Hong Kong Polytech., Hung Hom, Hong Kong

Volume

3

fYear

1996

fDate

5-10 Aug 1996

Firstpage

1459

Abstract

In this paper, the authors study the use of an unsupervised neural network to perform the inspection of IC leadframes. The network used is the learning by experience (LBE) type. They show the steps of varying the tolerance of acceptance whenever the network envisages some parts which cannot be classified. The Euclidean distance is used as the similarity measure. Here, two different types of unsupervised neural networks, namely adaptive resonance theory (ART2) and LBE, are compared. Experimental results on the classification of some patterns in a leadframe are also included

Keywords

ART neural nets; automatic optical inspection; computerised instrumentation; integrated circuits; learning by example; quality control; unsupervised learning; ART2; Euclidean distance; IC leadframes; acceptance tolerance; adaptive resonance theory; inspection automation; learning by experience; patterns classification; quality assurance; similarity measure; unsupervised neural network; Adaptive systems; Data mining; Euclidean distance; Inspection; Integrated circuit measurements; Neural networks; Neurons; Pins; Production; Resonance;

fLanguage

English

Publisher

ieee

Conference_Titel

Industrial Electronics, Control, and Instrumentation, 1996., Proceedings of the 1996 IEEE IECON 22nd International Conference on

Conference_Location

Taipei

Print_ISBN

0-7803-2775-6

Type

conf

DOI

10.1109/IECON.1996.570598

Filename

570598