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Technologies Automate X-Ray Inspection Continued from page 50
of test access. With SFT, during the process of inspecting side A, a master image or dynamic image of side A can be stored in digital memory. Side B can be tested independently of side A, by subtracting side B from the master or dynamic image of side A or by using 2D imaging. The SFT approach can be used for all views or selectively. The three-dimensional (3D)
simultaneous algebraic reconstruc- tion technique (3D-SART) models the entire physical process of x-ray pro- jections by a system of linear equa- tions. This is an iterative approach,
hanced processing speeds; these improvements will continue as GPUs gain in speed. The latest AXI system from MatriX, the X3, employs trans- mission imaging, off-axis or oblique viewing, slice filter technology, and 3D-SART reconstruction techniques. The flexible system allows operators to deploy the technologies selectively to provide optimum AXI inspection capabilities.
Software Automation Software automation is impor-
tant for shortening the total program development time from computer- aided-design (CAD) input data and model creation to establishing inspection strategies based on the CAD data and defect detection through advanced algorithmic “solder feature” measurements. Matrix has engineered
This is a double-sided circuit board with BGA on one side and capacitors and resistors on the other side.
slower than laminography or DT, but suffering fewer artifacts than those other approaches and therefore bet- ter solder joint defect detection with fewer false calls. By implementing 3D-SART with a graphical process- ing unit (GPU), MatriX has achieved fast image reconstruction and en -
two different approaches for program development. The first, Smart Rules, allows quick program development for even NPI environments. The AXI program can be trained with only one circuit board. For higher board vol- umes, mainly in environments in which more time is avail- able to develop a more robust and deeper defect detection protocol for the highest quality standards, the second ap - proach for program develop-
ment created by MatriX is called AutoRule. An AXI programmer can implement either one of these strate- gies during program development. By applying the Smart Rule
approach, the AXI software automat- ically finds the upper and lower limit
“rule thresholds” by isolating the data, taking into account the average and standard deviations. Smart Rule includes a software tool to auto- matically measure process stability. This tool automatically grades, dis- plays, and highlights which meas- urements have the lowest variation (are stable) from those with larger variations (are less stable). To make Smart Rules more robust, measure- ments can be combined into logical statements such that a solder joint would only fail only if meas- urement A “and” measure- ment “B” fail. This level of software automation worked to separate the lowest solder measurements (marginal or defective) from the normal histogram data distribution considered normal variation, yet good solder joint.
Systematic Approach In the case of an OEM
electronics assembler, where longer production runs are more of the norm compared to an EMS environment, pro- gram development can take a more systematic approach to achieve the highest defect detection and lowest false calls. By using a technique called the Defect Tree Classificator (DTC), MatriX has developed an effective technique for defect detection. It requires preclassified defect data to be processed, such as:
l
Insufficient or no solder conditions created by taping components onto
components. l
Missing conditions created by reflowing solder paste without
September, 2013
the circuit board without solder. l
ing parts on top. l
Standard production process boards.
The DTC approach allows rules to be automatically generated to separate good from defective solder joints. After “defective” boards have
been run through the inspection sys- tem, normally 5 to 10 times, the
ed by reflowing solder paste, placing tape over those pads, and then tap-
Lifted conditions which are creat-
The Co-axial Toplight approach helps to differentiate (a) normal solder,
(b) cold or missing solder, and (c) lack of solder by the amount and quality of the reflected light.
entire data set can be run through the DTC to automatically separate the defective data from the good data to establish detection guidelines, such as shorts, opens, insufficient, excess, no reflow, missing, off-posi- tion, voiding, and solder balls.
Continued on page 56
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