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Logic diagnosis is a software-based methodology to identify the behavior and location of defects in failing integrated circuits, which is an essential step in yield learning. However, accurate diagnosis requires a sufficient amount of failing data, which is in contradiction to the requirement of reducing test time and cost. In this work, a dynamic test pattern reordering method is proposed to “recommend” which test patterns should be applied for a given failing chip, with the goal of maximizing failing data while minimizing test time. Unlike prior work that uses population statistics from already tested chips, this method uses a machine learning technique, namely k-Nearest Neighbors. Experiments using three industrial chips demonstrate the efficacy of the proposed methodology; specifically, the recommended test pattern order led to a 35% reduction, on average, while maximizing the amount of failure data collected. 

Software-based diagnosis analyzes the observed response of a failing circuit to pinpoint potential defect locations and deduce their respective behaviors. It plays a crucial role in finding the root cause of failure, and subsequently facilitates yield analysis, learning and optimization. This paper describes a twophase, physically-aware diagnosis methodology called LearnX to improve the quality of diagnosis, and in turn the quality of design, test and manufacturing. The first phase attempts to diagnose a defect that manifests as a well-established fault behavior (e.g., stuck or bridge fault models). The second phase uses machine learning to build a model (separate for each defect type) that learns the characteristics of defect candidates to distinguish correct candidates from incorrect ones. Results from 30,000 fault injection experiments indicate that LearnX returns an ideal diagnosis result (i.e., a single candidate correctly representing the injected fault) for 73.2% of faulty circuits, which is 86.6% higher than state-of-the-art commercial diagnosis. Silicon experiments further demonstrate the value of LearnX. 

Software-based diagnosis analyzes the observed response of a failing circuit to pinpoint potential defect locations and deduce their respective behaviors. It plays a crucial role in finding the root cause of failure, and subsequently facilitates yield analysis, learning and optimization. A two-phase, physically-aware diagnosis methodology called LearnX is developed to improve the quality of diagnosis, and in turn the quality of design, test and manufacturing. In the first phase, a set of deterministic rules are created to identify defects that manifests as well-established fault behaviors. The second phase uses machine learning to build a model that learns the characteristics of defect candidates to distinguish correct candidates from incorrect ones. Results from 30,000 fault injection experiments indicate that LearnX achieves a resolution of one for 75.6% of the injected faults, showing an improvement of 39.0% over state-of-the-art commercial diagnosis. Additionally, the average ideal accuracy of LearnX is 95.7%, which is 27.2% higher than commercial diagnosis. More importantly, LearnX returns an ideal diagnosis result (i.e., a single candidate correctly representing the injected fault) for 73.2% of faulty circuits, which is 86.6% higher than commercial diagnosis. Silicon experiments further demonstrate the value of LearnX.