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Record W2035508719 · doi:10.1002/nav.20023

A unified model incorporating yield, burn‐in, and reliability

2004· article· en· W2035508719 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

VenueNaval Research Logistics (NRL) · 2004
Typearticle
Languageen
FieldEngineering
TopicReliability and Maintenance Optimization
Canadian institutionsUniversity of Alberta
FundersNational Science Foundation
KeywordsReliability (semiconductor)Yield (engineering)Reliability engineeringBurn-inFunction (biology)Poisson distributionComputer scienceMathematicsApplied mathematicsStatisticsEngineeringPhysicsThermodynamics

Abstract

fetched live from OpenAlex

Abstract The correlated improvement in yield and reliability has been observed in the case studies on integrated circuits and electronic assemblies. This paper presents a model that incorporates yield and reliability with the addition of a burn‐in step to explain their correlated improvement. The proposed model includes as special cases several yield and reliability models that have been previously published and thus provides a unifying framework. The model is used to derive a condition for which yield functions can be multiplied to obtain the overall yield. Yield and reliability are compared as a function of operation time, and an analytical condition for burn‐in to be effective is also obtained. Finally, Poisson and negative binomial defects models are further considered to investigate how reliability is based on yield. © 2004 Wiley Periodicals, Inc. Naval Research Logistics, 2004.

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)0.005
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 score0.585
Threshold uncertainty score0.595

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.077
GPT teacher head0.323
Teacher spread0.246 · 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