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Record W2066977219 · doi:10.1155/2014/529392

Gate-Level Circuit Reliability Analysis: A Survey

2014· article· en· W2066977219 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueVLSI design · 2014
Typearticle
Languageen
FieldEngineering
TopicRadiation Effects in Electronics
Canadian institutionsUniversity of Windsor
Fundersnot available
KeywordsReliability (semiconductor)Reliability engineeringComputer scienceNanoelectronicsCircuit designElectronic circuitElectronic engineeringEngineeringElectrical engineeringEmbedded systemMaterials science

Abstract

fetched live from OpenAlex

Circuit reliability has become a growing concern in today’s nanoelectronics, which motivates strong research interest over the years in reliability analysis and reliability-oriented circuit design. While quite a few approaches for circuit reliability analysis have been reported, there is a lack of comparative studies on their pros and cons in terms of both accuracy and efficiency. This paper provides an overview of some typical methods for reliability analysis with focus on gate-level circuits, large or small, with or without reconvergent fanouts. It is intended to help the readers gain an insight into the reliability issues, and their complexity as well as optional solutions. Understanding the reliability analysis is also a first step towards advanced circuit designs for improved reliability in the future research.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.934
Threshold uncertainty score0.637

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.037
GPT teacher head0.223
Teacher spread0.186 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it