Optimized reasoning-based diagnosis for non-random, board-level, production defects

The "back-end" costs associated with debug of functional test failures can be one of the highest cost adders in the manufacturing process. As boards become more dense and more complex, debug of functional failures will become more and more difficult. Test strategies try to detect and diagnose failures early on in the test process (component and structural tests), but inevitably some defects are not detected until functional testing is done on the board. Finding these defects usually requires an "expert", with engineering level skills in both hardware and software. Depending on the complexity of the product, it could take several months (even years) to develop this level of expertise. During the initial product ramp, this expertise is usually most needed and often unavailable. Debug time is usually very long and scrap rates are generally high. This paper will provide an overview of reasoning-based diagnosis techniques and how they can significantly decrease debug time, especially during new product introduction. Because these engines are "model-based", there is no guarantee how they will perform in real life. In almost all cases, the reasoning engine will have to be modified based on instances where the reasoning engine could not correctly identify the failing component. Making these adjustments to the reasoning is a very complex and sometimes risky endeavor. While the new model may correctly identify the previously missed failure, the reasoning may have been altered to a point where several other diagnoses have now been unknowingly compromised. This paper will propose enhancements to the reasoning engine that will allow a simpler approach to adapting to diagnostic escapes without risking compromises to the original diagnostic engine.