DREAMS: DFM rule EvAluation using manufactured silicon

Authors:
R. D. Blanton;F. Wang;C. Xue;Pk Nag;Y. Xue;X. Li
Affiliations:
Carnegie Mellon University, Pittsburgh, PA;Carnegie Mellon University, Pittsburgh, PA;Carnegie Mellon University, Pittsburgh, PA;Carnegie Mellon University, Pittsburgh, PA;Carnegie Mellon University, Pittsburgh, PA;Carnegie Mellon University, Pittsburgh, PA
Venue:
Proceedings of the International Conference on Computer-Aided Design
Year:
2013

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Abstract

DREAMS (DFM Rule EvAluation using Manufactured Silicon) is a comprehensive methodology for evaluating the yield-preserving capabilities of a set of DFM (design for manufacturability) rules using the results of logic diagnosis performed on failed ICs. DREAMS is an improvement over prior art in that the distribution of rule violations over the diagnosis candidates and the entire design are taken into account along with the nature of the failure (e.g., bridge versus open) to appropriately weight the rules. Silicon and simulation results demonstrate the efficacy of the DREAMS methodology. Specifically, virtual data is used to demonstrate that the DFM rule most responsible for failure can be reliably identified even in light of the ambiguity inherent to a non-ideal diagnostic resolution, and a corresponding rule-violation distribution that is counter-intuitive. We also show that the combination of physically-aware diagnosis and the nature of the violated DFM rule can be used together to improve rule evaluation even further. Application of DREAMS to the diagnostic results from an in-production chip provides valuable insight in how specific DFM rules improve yield (or not) for a given design manufactured in particular facility. Finally, we also demonstrate that a significant artifact of DREAMS is a dramatic improvement in diagnostic resolution. This means that in addition to identifying the most ineffective DFM rule(s), validation of that outcome via physical failure analysis of failed chips can be eased due to the corresponding improvement in diagnostic resolution.