Skip to main content

DFM evaluation using IC diagnosis data

Publication ,  Journal Article
Blanton, RDS; Wang, F; Xue, C; Nag, PK; Xue, Y; Li, X
Published in: IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems
March 1, 2017

Design for manufacturability rule evaluation using manufactured silicon (DREAMS) is a comprehensive methodology for evaluating the yield-preserving capabilities of a set of design for manufacturability (DFM) 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 nonideal 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.

Duke Scholars

Published In

IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems

DOI

ISSN

0278-0070

Publication Date

March 1, 2017

Volume

36

Issue

3

Start / End Page

463 / 474

Related Subject Headings

  • Computer Hardware & Architecture
  • 4607 Graphics, augmented reality and games
  • 4009 Electronics, sensors and digital hardware
  • 1006 Computer Hardware
  • 0906 Electrical and Electronic Engineering
 

Citation

APA
Chicago
ICMJE
MLA
NLM
Blanton, R. D. S., Wang, F., Xue, C., Nag, P. K., Xue, Y., & Li, X. (2017). DFM evaluation using IC diagnosis data. IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems, 36(3), 463–474. https://doi.org/10.1109/TCAD.2016.2587283
Blanton, R. D. S., F. Wang, C. Xue, P. K. Nag, Y. Xue, and X. Li. “DFM evaluation using IC diagnosis data.” IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems 36, no. 3 (March 1, 2017): 463–74. https://doi.org/10.1109/TCAD.2016.2587283.
Blanton RDS, Wang F, Xue C, Nag PK, Xue Y, Li X. DFM evaluation using IC diagnosis data. IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems. 2017 Mar 1;36(3):463–74.
Blanton, R. D. S., et al. “DFM evaluation using IC diagnosis data.” IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems, vol. 36, no. 3, Mar. 2017, pp. 463–74. Scopus, doi:10.1109/TCAD.2016.2587283.
Blanton RDS, Wang F, Xue C, Nag PK, Xue Y, Li X. DFM evaluation using IC diagnosis data. IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems. 2017 Mar 1;36(3):463–474.

Published In

IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems

DOI

ISSN

0278-0070

Publication Date

March 1, 2017

Volume

36

Issue

3

Start / End Page

463 / 474

Related Subject Headings

  • Computer Hardware & Architecture
  • 4607 Graphics, augmented reality and games
  • 4009 Electronics, sensors and digital hardware
  • 1006 Computer Hardware
  • 0906 Electrical and Electronic Engineering