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Record W4283780494 · doi:10.1109/ets54262.2022.9810415

Prediction of Thermally Accelerated Aging Process at 28nm

2022· article· en· W4283780494 on OpenAlexaff
Parvez Anwar Chanawala, Ian E. J. Hill, S. Arash Sheikholeslam, A. Ivanov

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicSemiconductor materials and devices
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsProcess (computing)Computer scienceMaterials scienceReliability engineeringProcess engineeringEngineering

Abstract

fetched live from OpenAlex

We introduce a methodology to predict degradation in an SoC device undergoing a thermally accelerated aging process. SoCs are usually stressed at high temperatures and voltages (above nominal) to accelerate their aging so that their reliability under nominal conditions can be predicted. Here we focus on the thermal acceleration process. We implement a ring oscillator-based test structure and consider its free-running frequency as our reference parameter to measure degradation. We analyze 500 hours of BTI-induced degradation behavior at different temperatures and observed that the final degradation can be confidently predicted from the measurements in first half of the experiment. This observation provides a new research avenue to predict reliability test results, such as HTOL, which lasts for 1000 hours and has a negative impact on the product’s time to market.

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.

How this classification was reachedexpand

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.009
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 integrity0.0000.000
Insufficient payload (model declined to judge)0.0040.000

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 head0.032
GPT teacher head0.225
Teacher spread0.193 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designBench or experimental
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations0
Published2022
Admission routes1
Has abstractyes

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