Physically-Aware Diagnostic Resolution

Physical failure analysis (PFA) is crucial to improving yield during the production life of an integrated circuit (IC). PFA begins with software-based diagnosis, which reports a set of likely failing candidates. A large set, which can result from test-set equivalent (TSE) faults, makes PFA success unlikely because each candidate may potentially have to be examined. Reducing the cardinality of a TSE fault class can reduce the time required for PFA since it likely decreases the number of candidates that must be examined. Additional (diagnostic) test vectors can be generated however to reduce class size. In this work, several layout-based physical metrics are defined and investigated for assessing the layout characteristics of a TSE fault class, which serves as an estimate of the time/difficulty required to perform PFA. In short, these metrics are used to define a physically-aware diagnostic resolution (PAR for short) for a fault class. A TSE fault class with a bad PAR likely requires more PFA effort than one with good PAR. To demonstrate the utility of PAR, a test-selection algorithm that chooses tests from an N-detect test pool based on the improvement in PAR is compared to techniques that use conventional definitions of diagnostic resolution. Comprehensive experiments reveal that fewer additional tests are needed when using PAR.