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Record W2123821144 · doi:10.1109/dftvs.2003.1250103

Power supply current test approach for resistive fault screening in embedded analog circuits

2004· article· en· W2123821144 on OpenAlexaff
M.S. Dragic, Martin Margala

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicVLSI and Analog Circuit Testing
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsResistive touchscreenAnalogue electronicsCMOSElectronic circuitElectronic engineeringMixed-signal integrated circuitFault (geology)ChipIntegrated circuitComputer scienceDigital electronicsFault coverageElectrical engineeringEngineering

Abstract

fetched live from OpenAlex

The feasibility of a non-specification based method for testing of analog integrated circuits in a 0.13 /spl mu/m CMOS process has been explored. The method is an extension of digital I/sub DD/ test to analog circuits. We investigated detection rate of resistive open and short faults within a MOSFET device in several analog circuits implemented in 0.13 /spl mu/m CMOS technology. Input test signals are optimized for maximum detectability of introduced faults. Stimuli required for defect screening are DC signals which can be easily produced on-chip. It is shown in this paper that with respect to the used fault models, the detection success rate for introduced faults is 100% for resistive shorts and 67% for resistive opens. This simple method is suitable for production testing, as a preliminary and complementary test of embedded analog circuits for early defect screening in highly integrated environment.

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: none
Teacher disagreement score0.003
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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 head0.275
Teacher spread0.243 · 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

Citations5
Published2004
Admission routes1
Has abstractyes

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