Volltext-Downloads (blau) und Frontdoor-Views (grau)
The search result changed since you submitted your search request. Documents might be displayed in a different sort order.
  • search hit 11 of 673
Back to Result List

Bayesian Optimized Mixture Importance Sampling for High-Sigma Failure Rate Estimation

  • In many application domains, in particular automotives, guaranteeing a very low failure rate is crucial to meet functional and safety standards. Especially, reliable operation of memory components such as SRAM cells is of essential importance. Due to aggressive technology downscaling, process and runtime variations significantly impact manufacturing yield as well as functionality. For this reason,In many application domains, in particular automotives, guaranteeing a very low failure rate is crucial to meet functional and safety standards. Especially, reliable operation of memory components such as SRAM cells is of essential importance. Due to aggressive technology downscaling, process and runtime variations significantly impact manufacturing yield as well as functionality. For this reason, a thorough memory failure rate assessment is imperative for correct circuit operation and yield improvement. In this regard, Monte Carlo simulations have been used as the conventional method to estimate the variability induced failure rate of memory components. However, Monte Carlo methods become infeasible when estimating rare events such as high-sigma failure rates. To this end, Importance Sampling methods have been proposed which reduce the number of required simulations substantially. However, existing methods still suffer from inaccuracies and high computational efforts, in particular for high-sigma problems. In this paper, we fill this gap by presenting an efficient mixture Importance Sampling approach based on Bayesian optimization, which deploys a surface model of the objective function to find the most probable failure points. Its advantages include constant complexity independent of the dimensions of design space, the potential to find the global extrema, and higher trustworthiness of the estimated failure rate by accurately exploring the design space. The approach is evaluated on a 6T-SRAM cell as well as a master-slave latch based on a 28nm FDSOI process. The results show an improvement in accuracy, resulting in up to 63× better accuracy in estimating failure rates compared to the best state-of-the-art solutions on a 28nm technology node.show moreshow less

Export metadata

Additional Services

Search Google Scholar

Statistics

frontdoor_oas
Metadaten
Document Type:Article (reviewed)
Zitierlink: https://opus.hs-offenburg.de/3972
Bibliografische Angaben
Title (English):Bayesian Optimized Mixture Importance Sampling for High-Sigma Failure Rate Estimation
Author:Dennis D. Weller, Michael Hefenbrock, Mohammad Saber Golanbari, Michael Beigl, Jasmin Aghassi-HagmannStaff MemberORCiDGND, Mehdi Baradaran Tahoori
Year of Publication:2019
Creating Corporation:IEEE
First Page:1
Last Page:11
Parent Title (English):IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems
ISSN:0278-0070 (Print)
ISSN:1937-4151 (Online)
DOI:https://doi.org/10.1109/TCAD.2019.2961321
Language:English
Inhaltliche Informationen
Institutes:Fakultät Elektrotechnik, Medizintechnik und Informatik (EMI) (ab 04/2019)
Institutes:Bibliografie
Formale Angaben
Open Access: Closed Access 
Licence (German):License LogoUrheberrechtlich geschützt