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Record W2046692117 · doi:10.1109/ipfa.2013.6599166

Recent advances in fault isolation for semiconductor industry

2013· article· en· W2046692117 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

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicIntegrated Circuits and Semiconductor Failure Analysis
Canadian institutionsAdvanced Micro Devices (Canada)
Fundersnot available
KeywordsRoot causeIsolation (microbiology)Fault (geology)Semiconductor industryWaferComputer scienceProduct (mathematics)Process (computing)Reliability engineeringsortWafer testingWafer fabricationEngineeringManufacturing engineeringElectrical engineering

Abstract

fetched live from OpenAlex

In semiconductor companies, failure analysis (FA) activities play a major role in all many areas. FA is deeply involved in new process technology development, 1st Silicon bring-up, wafer sort and backend yield improvement, product qualification and customer return analysis. RegardLeSS Of area that FA SUPPOrtS, there IS aLWaYS a need fOr faULt ISOLatIOn PrIOr tO the PhYSICaL Or deStrUCtIVe faILUre anaLYSIS. FaULt ISOLatIOn IS the SteP Where We narrOW dOWn the area Of a faILIng Part Or PrOdUCt tO a manageabLe area. ThIS aLLOWS FA engIneer tO ImPrOVe the SUCCeSS Of PhYSICaLLY fIndIng rOOt CaUSe Of the faILUre and SPeedS UP the TUrn-arOUnd tIme fOr the anaLYSIS. ThIS InVIted taLK WILL COVer reCent adVanCeS In faULt ISOLatIOn teChnIqUeS and tOOLS In deVICe/SILICOn and PaCKagIng. CaSe StUdIeS fOr thOSe teChnIqUeS WILL be COVered tO PrOVIde greater UnderStandIng fOr the teChnIqUeS tO the aUdIenCe.

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)0.000
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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.605
Threshold uncertainty score0.999

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 communication0.0000.001
Open science0.0000.000
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 head0.012
GPT teacher head0.233
Teacher spread0.220 · 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