A Temperature-Aware Time-Dependent Dielectric Breakdown Analysis Framework

Bekiaris, Dimitris; Papanikolaou, Apostolos; Papameletis, Christos; Soudris, Dimitrios; Economakos, George; Pekmestzi, Kiamal

doi:10.1007/978-3-642-17752-1_8

Cited by 1 publication

(1 citation statement)

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“…It manifests itself both in the gate stack of devices and across wires of an integrated system. We also assume two modeling papers: one for TDDB manifestation on wires [Bekiaris et al 2011] and one for devices [Kimura 1999]. In case we would like to systematically classify these two papers, we should further split the "irreversible mechanism" leaf of our classification framework (see Figure 10) into two complementary categories.…”

Section: Reusability and Extensibility Of The Proposed Classificationmentioning

confidence: 99%

Classification Framework for Analysis and Modeling of Physically Induced Reliability Violations

Rodopoulos¹,

Psychou

Sabry

et al. 2015

ACM Comput. Surv.

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Technology downscaling is expected to amplify a variety of reliability concerns in future digital systems. A good understanding of reliability threats is crucial for the creation of efficient mitigation techniques. This survey performs a systematic classification of the state of the art on the analysis and modeling of such threats, which are caused by physical mechanisms to digital systems. The purpose of this article is to provide a classification tool that can aid with the navigation across the entire landscape of reliability analysis and modeling. A classification framework is constructed in a top-down fashion from complementary categories, each one addressing an approach on reliability analysis and modeling. In comparison to other classifications, the proposed methodology approaches the target research domain in a complete way, without suppressing hybrid works that fall under multiple categories. To substantiate the usability of the classification framework, representative works from the state of the art are mapped to each appropriate category and are briefly analyzed. Thus, research trends and opportunities for novel approaches can be identified.

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Section: Reusability and Extensibility Of The Proposed Classificationmentioning

confidence: 99%