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Record W2106092081 · doi:10.1109/ipfa.2008.4588164

Device-level fault isolation of advanced flip-chip devices using scanning SQUID microscopy

2008· article· en· W2106092081 on OpenAlex
Calvin Wei Liang Teo, H.E. Lwin, V. Narang, J.M. Chin

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
KeywordsFlip chipMicroprocessorMaterials scienceChipFault detection and isolationFault (geology)Isolation (microbiology)Die (integrated circuit)SquidOptoelectronicsComputer scienceElectronic engineeringComputer hardwareElectrical engineeringEngineeringNanotechnologyGeology

Abstract

fetched live from OpenAlex

This article describes how a scanning SQUID microscope (SSM) enhances the capability of device-level fault isolation on advanced 90 nm and 65 nm flip-chip microprocessor devices. SSM has proved to be very useful in isolating bump shorts and shorts in copper interconnects. For improved resolution and analyzing bumped dies, a front-side SSM technique is developed that has greatly increased success rates and analysis turn-around time. In this paper, we focus on die-level fault isolation on advanced microprocessor devices with numerous metal layers.

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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.183
Threshold uncertainty score0.716

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 head0.040
GPT teacher head0.263
Teacher spread0.224 · 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