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Optimization of reflow soldering process for BGA packages by artificial neural network.

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Title: Optimization of reflow soldering process for BGA packages by artificial neural network.
Authors: Yu-Hsin Lin, Wei-Jaw Deng, Jie-Ren Shie, Yung-Kuang Yang
Source: Microelectronics International. Apr2007, Vol. 24 Issue 2, p64-70. 7p.
Subjects: Artificial neural networks, Solder & soldering, Ball grid array technology, Printed circuits, Electronics
Abstract: Purpose - This investigation applied a hybrid method combining a trained artificial neural network (ANN) and the sequential quadratic programming (SQP) method to determine an optimal parameter setting for a reflow soldering process of ball grid array packages in printed circuit boards. Design/methodology/approach - Nine experiments based on an orthogonal array table with three-controlled inputs and average shear forces of solder spheres as a quality target were utilized to train the ANN and then the SQP method was implemented to search for an optimal setting of parameters. Findings - The ANN can be utilized successfully to predict the shear force under different reflow soldering conditions after being properly trained and the identified optimal parameter setting are capable of striking the balance between the average shear forces and the manufacturing cycle time. Practical implications - The reflow time and the peak temperature were found to be the most significant factors for the reflow process via analysis of variance. Originality/value - This study provided an algorithm integrating a black-box modeling approach (i.e. the ANN predictive model) with the SQP method to resolve an optimization problem. This algorithm offered an effective and systematic way to identify an optimal setting of the reflow soldering process. Hence, the efficiency of designing the optimal parameters was greatly improved. [ABSTRACT FROM AUTHOR]
Copyright of Microelectronics International is the property of Emerald Publishing Limited and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Engineering Source
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Header DbId: egs
DbLabel: Engineering Source
An: 25594825
AccessLevel: 6
PubType: Academic Journal
PubTypeId: academicJournal
PreciseRelevancyScore: 0
IllustrationInfo
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  Data: Optimization of reflow soldering process for BGA packages by artificial neural network.
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  Data: <searchLink fieldCode="AR" term="%22Yu-Hsin+Lin%22">Yu-Hsin Lin</searchLink><br /><searchLink fieldCode="AR" term="%22Wei-Jaw+Deng%22">Wei-Jaw Deng</searchLink><br /><searchLink fieldCode="AR" term="%22Jie-Ren+Shie%22">Jie-Ren Shie</searchLink><br /><searchLink fieldCode="AR" term="%22Yung-Kuang+Yang%22">Yung-Kuang Yang</searchLink>
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  Data: <searchLink fieldCode="JN" term="%22Microelectronics+International%22">Microelectronics International</searchLink>. Apr2007, Vol. 24 Issue 2, p64-70. 7p.
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  Data: <searchLink fieldCode="DE" term="%22Artificial+neural+networks%22">Artificial neural networks</searchLink><br /><searchLink fieldCode="DE" term="%22Solder+%26+soldering%22">Solder & soldering</searchLink><br /><searchLink fieldCode="DE" term="%22Ball+grid+array+technology%22">Ball grid array technology</searchLink><br /><searchLink fieldCode="DE" term="%22Printed+circuits%22">Printed circuits</searchLink><br /><searchLink fieldCode="DE" term="%22Electronics%22">Electronics</searchLink>
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: Purpose - This investigation applied a hybrid method combining a trained artificial neural network (ANN) and the sequential quadratic programming (SQP) method to determine an optimal parameter setting for a reflow soldering process of ball grid array packages in printed circuit boards. Design/methodology/approach - Nine experiments based on an orthogonal array table with three-controlled inputs and average shear forces of solder spheres as a quality target were utilized to train the ANN and then the SQP method was implemented to search for an optimal setting of parameters. Findings - The ANN can be utilized successfully to predict the shear force under different reflow soldering conditions after being properly trained and the identified optimal parameter setting are capable of striking the balance between the average shear forces and the manufacturing cycle time. Practical implications - The reflow time and the peak temperature were found to be the most significant factors for the reflow process via analysis of variance. Originality/value - This study provided an algorithm integrating a black-box modeling approach (i.e. the ANN predictive model) with the SQP method to resolve an optimization problem. This algorithm offered an effective and systematic way to identify an optimal setting of the reflow soldering process. Hence, the efficiency of designing the optimal parameters was greatly improved. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
  Label:
  Group: Ab
  Data: <i>Copyright of Microelectronics International is the property of Emerald Publishing Limited and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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      – Type: doi
        Value: 10.1108/13565360710745610
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      – Code: eng
        Text: English
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        PageCount: 7
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      – SubjectFull: Artificial neural networks
        Type: general
      – SubjectFull: Solder & soldering
        Type: general
      – SubjectFull: Ball grid array technology
        Type: general
      – SubjectFull: Printed circuits
        Type: general
      – SubjectFull: Electronics
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            NameFull: Yu-Hsin Lin
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            NameFull: Wei-Jaw Deng
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            NameFull: Jie-Ren Shie
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            NameFull: Yung-Kuang Yang
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              M: 04
              Text: Apr2007
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              Y: 2007
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