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Record W2165607408 · doi:10.1109/ecc.1988.12617

Low-voltage failures in multilayer ceramic capacitors: a new accelerated stress screen

2003· article· en· W2165607408 on OpenAlexaff
R. Munikoti, Pinaki Dhar

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicSemiconductor materials and devices
Canadian institutionsNortel (Canada)
Fundersnot available
KeywordsCapacitorCeramic capacitorReliability (semiconductor)VoltageFilm capacitorMaterials scienceCeramicStress (linguistics)Electrical engineeringAccelerationHigh voltageElectronic engineeringReliability engineeringComputer scienceEngineeringComposite materialPhysics

Abstract

fetched live from OpenAlex

A method of screening out all potential low-voltage failures in manufactured lots of multilayer ceramic capacitors (MLCC) is presented. The method eliminates all the potential failures in capacitor lots. This test uses the technique of highly accelerated life-testing (HALT) previously developed by the authors (ibid., vol.37, p.129-34, 1987). In this technique, 50-V rated capacitors are subjected to accelerated testing at 140 degrees C and 400 V DC, instead of the standard 125 degrees C and 100 V DC environment. The approach is based on the concept that low-voltage failures are caused by extrinsic defects in MLCCs and that these defects can be eliminated in a very short using high voltage and temperature acceleration. It is shown that the HALT test can eliminate the 85/85 test, since a single short HALT test can completely characterize the quality and reliability of ceramic capacitors, both for rated voltage and low-voltage applications.>

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
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 score0.002
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.021
GPT teacher head0.231
Teacher spread0.210 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations7
Published2003
Admission routes1
Has abstractyes

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