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Record W1531479582 · doi:10.1109/ipfa.2015.7224371

Optimization and application of Electron Beam Absorbed Current technique

2015· article· en· W1531479582 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
KeywordsTungstenScanning electron microscopeMaterials scienceResistive touchscreenCurrent (fluid)OptoelectronicsElectromigrationElectronCathode rayContact resistanceBeam (structure)VoltageNanotechnologyOpticsElectrical engineeringPhysicsComposite materialEngineeringMetallurgy

Abstract

fetched live from OpenAlex

Advanced microprocessors are aggressively scaled with process technology rapidly advancing to 14nm technology node. This presents a challenging task to uncover subtle physical defects resulting from resistive via/contact & shorted tight pitch metal interconnects. Electron Beam Absorbed Current (EBAC) is a promising technique that can help to identify the defective vias or metal shorts in non-invasive manner. This technique is based on scanning electron microscopy (SEM) and pizeo manipulators of tungsten tips. Metal lines are probed with tungsten probes in SEM and electrons beam current absorbed by the metal lines are collected and used to form a current or voltage contrast map of the area. Any abnormal metal EBAC image would indicate metal line defects and can be correlated with layout images.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.972
Threshold uncertainty score0.195

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)0.0000.000

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 head0.008
GPT teacher head0.223
Teacher spread0.214 · 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