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Using Cell Aware Diagnostic Patterns to Improve Diagnosis Resolution for Cell Internal Defects

The industry is encountering an increasing number of front-end-of-line defects in the most advanced FinFET technology nodes due to extremely small feature size and complex manufacturing processes. Traditional diagnostic pattern generation based on stuck-at faults becomes less effective for these complex cell internal defects due to various reasons. In this work, we propose a new method to generate cell aware diagnostic patterns for cell internal defects based on accurate defect models extracted by analog simulation. Special UDFM faults covering all possible input combinations and all fault effect propagation scenarios can be used to maximize the diagnosis resolution. The enhanced diagnostic patterns can effectively detect and differentiate cell internal defects, and thus dramatically improve the diagnosis resolution. Experimental results for real silicon cases showed that the number of suspects can be reduced by 14.9X on the average. Furthermore, detailed analysis for extra failure data from cell aware diagnostic patterns provides valuable information for physical failure analysis.