EAGER: Storage-Based Logic Built-In Self-Test with Magnified Test Data

Purdue University, West Lafayette IN

Investigators

Abstract

New technologies for manufacturing of electronic chips give rise to new defect mechanisms and increase the prevalence of defect occurrence. At the same time, applications such as automotive systems impose stricter requirements on reliability. Detecting the occurrence of defects is critical to the reliability of electronic chips, and the correct and safe operation of the many products that use them. Test sets used for manufacturing testing of electronic chips need to be comprehensive in their ability to detect and diagnose defects, but at the same time, satisfy cost constraints related to their storage and test application time. Each test needs to be utilized to the maximum to detect as many defects as possible of as many different types as possible to ensure that a cost-limited test set is able to achieve reliability goals. Such a test set is referred to as a focused test set, and is the subject of this project. The project will prepare students for the challenges of designing and manufacturing reliable electronic chips in current and future technologies, which is critical to the many US industries that rely on electronic chips. The PI will provide a role model and encourage women students to pursue degrees in the field of electrical and computer engineering. Through internship opportunities, students will get comprehensive training with both academic and industrial depth. To manage the complexity and variability in defect behaviors, fault models are used as abstract representations of defects, and a comprehensive set of tests targets the detection of faults from different models. However, faults from different models have similar tests, and the added value for defect detection diminishes as additional fault models are considered. Two complementary directions will be explored in this project for generating focused test sets. (1) Different fault models are used for providing comprehensive defect coverage. However, if faults of all types are considered equally important, a large number of tests results, and they are not all necessary for detecting or diagnosing defects. Instead, different faults should be used as complements of each other. The problem that will be considered in this project is how to partition target faults into subsets with similar (but not identical) detection conditions, and similar (but not identical) tests. A procedure for generating a focused test set will select non-similar representatives from each subset as targets for test generation, and thus produce tests that are well-utilized for defect detection and diagnosis. (2) In a basic test data compression scheme, each stored test is used for applying one test. Earlier works showed that the same stored test data can be used in more than one way to apply more tests. This allows the volume of test data to be reduced. The new observation that this project will utilize is that applying more tests based on the same stored test data is useful for improving the quality of a focused test set. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.