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Design factors and their effect on PCB assembly yield-statistical and neural network predictive models

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3 Author(s)
Y. Li ; Center for Adv. Manuf. & Packaging of Microwave, Opt. & Digital Electron., Colorado Univ., Boulder, CO, USA ; R. L. Mahajan ; J. Tong

This study relates circuit board design features to assembly yields. Data used were collected over a period of one year from two circuit board assembly shops at AT&T. Design parameters that may affect the assembly yield were identified using knowledge of the assembly process. These parameters were then quantified for a set of board designs and related to the actual assembly yield by the statistical regression models and the artificial neural network (ANN) models. These models are able to predict the assembly yield with a root mean square (RMS) error of less than 5%. They can be used to predict the assembly yield for new board designs on the same line. Alternatively, they can be used to compare the performance of different lines by comparing the expected yield for a given design with the actual yield

Published in:

IEEE Transactions on Components, Packaging, and Manufacturing Technology: Part A  (Volume:17 ,  Issue: 2 )