Modeling the economics of testing: a DFT perspective

Decision-makers typically make test tradeoffs using models that mainly represent direct costs such as test generation time and tester use. Analyzing a test strategy´s impact on other significant factors such as test quality and yield learning requires an understanding of the dynamic nature of the interdomain dependencies of test, manufacturing, and design. Our research centers on modeling the tradeoffs between these domains. To answer the DFT question, we developed the Carnegie Mellon University Test Cost Model, a DFT cost-benefit model, derived inputs to the model for various IC cases with different assumptions about volume, yield, chip size, test attributes, and so forth; and studied DFT´s impact on these cases. We used the model to determine the domains for which DFT is beneficial and for which DFT should not be used. The model is a composite of simple cause-and-effect relationships derived from published research. It incorporates many factors affecting test cost, but we don´t consider it a complete model. Our purpose is to illustrate the necessity of using such models in assessing the effectiveness of various test strategies