Automating Tolerance to Process Variation
No two printed circuit boards look exactly alike. Even across two adjacent boards on an assembly line,one can find
significant differences arising out of normal process variation – the components and the boards can change color,size and
surface markings. A key challenge for inspection systems is to automatically handle such allowable variation,and distinguish
it from other variations that constitute defects.
Automated optical inspection (AOI) systems have emerged as an important test strategy in printed circuit board
manufacturing to detect defects. Typical AOI systems depend in large measure on heuristics-based (trial and error
experimentation) data as the means to establish typical conditions,the degree of normal variation,the thresholds for nominal
pass/fail conditions,the lighting/camera conditions to best view the object,and the parameters for providing variable
measurement data. The use of empirical processes is a sound basis to make decisions where the sample population being
employed is large enough to mimic the whole. However,user assessments of heuristics require skill and experience of
programmers. As a result,the competency of empirical methods is built up over time and over volume by basing ‘goodness’
criteria on historical values and historical volumes,as seen through the filter of user judgment. Where time and volume are
insufficient to establish stable norms,where user judgment of good and bad are questionable,or where variation of the
elements of the printed circuit board is significant,it becomes difficult to effectively deploy heuristic-based programs.
In this paper,we present a new technology called ‘Configural Recognition’ that provides built-in tolerance to normal process
variation. The technology was initially developed at the MIT Artificial Intelligence Laboratory for applications such as
natural scene classification,face recognition,and trademark logo search. In these applications,normal variation is a
significant and challenging problem for standard computer-vision systems. The technology is grounded in studies of how
humans visually recognize objects.
Over the past six years the technology has been employed for the task of printed circuit board inspection and process control.
The benefits of the technology in the PCB domain are its native ability to recognize PCB artifacts without re-sensitizing the
objects under test,thus eliminating or reducing the necessity of establishing test norms A second benefit is that as variation is
a known entity and accepted as an inevitable but compensated activity,far fewer examples need be used to generate a PCB
test program. Finally,the technology allows for the optical-inspection system to make the leap from finding defects to
providing reliable and repeatable variable measurement data with the same ease of use.