Improving Diagnosis Efficiency via Machine Learning
This work was presented at the International Test Conference (ITC), 2018.
Authors: Qicheng Huang, Chenlei Fang, Soumya Mittal, Shawn Blanton
Summary: Resources can be easily wasted if diagnosis results in no meaningful information, or if the type of diagnostic result is not actionable. In this work, a methodology is developed to predict whether a fail log for a given design will result in a diagnosis outcome that is meaningful for the purpose at hand. Specifically, the aim is to predict the time required for diagnosis, and whether diagnosis produces any defect candidates, and if so, are those candidates the result of logic failure or chain failure. Random Forest classification algorithm is used for prediction.