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Year : 2000 Volume : 23

Year : 1999 Volume : 22

Neural network modeling with confidence bounds: a case study on the solder paste deposition process

Content Provider	IEEE Xplore Digital Library
Author	Ho, S.L. Xie, M. Tang, L.C. Xu, K. Goh, T.N.
Copyright Year	1999
Abstract	The formation of reliable solder joints in electronic assemblies is a critical issue in surface mount manufacturing. Stringent control is placed on the solder paste deposition process to minimize soldering defects and achieve high assembly yield. Time series process modeling of the solder paste quality characteristics using neural networks (NN) is a promising approach that complements traditional control charting schemes deployed on-line. We present the study of building a multilayer feedforward neural network for monitoring the solder paste deposition process performance. Modeling via neural networks provides not only useful insights in the process dynamics, it also allows forecasts of future process behavior to be made. Data measurements collected on ball grid array (BGA) and quad flat pack (QFP) packages are used to illustrate the NN technique and the forecast accuracies of the models are summarized. Furthermore, in order to quantify the errors associated with the forecasted point estimates, asymptotically valid prediction intervals are computed using nonlinear regression. Simulation results showed that the prediction intervals constructed give reasonably satisfactory coverage percentages as compared to the nominal confidence levels. Process control using NN with confidence bounds provides more quality information on the performance of the deposition process for better decision making and continuous improvement.
Sponsorship	IEEE Components, Packaging, and Manufacturing Technology Society International Microelectronics Assembly and Packaging Society (IMAPS)
Starting Page	323
Ending Page	332
Page Count	10
File Size	238050
File Format	PDF
ISSN	1521334X
Volume Number	24
Issue Number	4
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2001-10-01
Publisher Place	U.S.A.
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Neural networks Predictive models Electronics packaging Soldering Assembly Multi-layer neural network Manufacturing Feedforward neural networks Monitoring Computational modeling
Content Type	Text
Resource Type	Article
Subject	Industrial and Manufacturing Engineering Electrical and Electronic Engineering

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