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Reducing BGA defects with AXI inspection: this study established the ideal sensitivity setting for catching BGA voids.

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Inspection of BGAs and detection and measurement of BGA voids has become critical as use of BGAs on assemblies has increased. How large can a BGA void be and still be considered acceptable? First, we must ensure that we have an accurate method to measure BGA voids. Second, we must ensure that the criteria we use in setting BGA void rejection is in agreement with IPC standards or customer specifications.


The objective of our work was to:

* Assess the current setting for the Agilent AXI 5DX BGA2 void algorithm in detecting voids. (1)

* Compare and correlate the voiding measurement data between AXI laminography and transmission x-ray systems.

* Discuss how AXI technology can be used for SMT process improvement in reducing BGA defects.


Detection of BGA voids with the AXI system and the accuracy of the measurement made is a function of one of the machine's void algorithm thresholds called the "sensitivity setting." This threshold sets the level of grey-scale at which pixels are to be considered part of a void. The larger the setting, the larger the number of pixels that are considered to be part of a void. The AXI measurement of BGA void size and percentage is dependent on the AXI algorithm's sensitivity setting number. We studied this measurement data (BGA voiding diameter and area percentage) with the SPC tool MINITAB for analysis of variance (ANOVA). The ANOVA showed the mean of the standard deviation (StDev) for a selected BGA void diameter to be 0.6345[+ or -]0.3418 mils, 0.6273[+ or -]0.5435 mils and 1.1155[+ or -]0.5766 mils with sensitivity setting 0.25, 0.35 and 0.45, respectively (Figure 8). The ANOVA showed the mean of the StDev of the BGA void percentage to be 2.200[+ or -]0.953%, 2.834[+ or -]1.602% and 5.115[+ or -]2.546% with sensitivity setting 0.25, 0.35 and 0.45, respectively. Based on the data, it appears that an algorithm sensitivity setting of 0.25 is good starting number for BGA voiding. The transmission x-ray measurement data were used for comparison.

The AXI test reports attribute data, but the system can provide variable data as well. With variable measurement data from this automatic test method, the AXI is a good SMT process improvement tool, as well as a tool for process defect detection. (2-3) The variable data can help effectively control SMT processes. We also discuss how to reduce BGA defects using AXI measurement data to search optimized profiles. Decreases in BGA defects can lead to manufacturing cost savings. As a result of our use of this AXI defect detection data, we were able to significantly reduce the occurrence of BGA defects, which resulted in a cost savings of over $40,000 in a period of four months.

Zhen (Jane) Feng, Ph.D., Eduardo Toledo, Jonathan Jian and Murad Kurwa

Zhen (Jane) Feng is senior staff engineer-Americas; Eduardo Toledo is project manager, San Jose operation; Jonathan Jian is process engineer, San Jose operation; and Murad Kurwa is vice president engineering-Americas, Flextronics International (;
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Title Annotation:Process Improvement
Author:Kurwa, Murad
Publication:Circuits Assembly
Date:Jul 1, 2005
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