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Integration of Probing Capability into Plasma FIB for In-Situ Delayering, Defect Inspection, and EBAC on BEOL Defects of Sub-20nm FinFET Devices

2018· article· en· W2954108375 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

VenueProceedings - International Symposium for Testing and Failure Analysis · 2018
Typearticle
Languageen
FieldEngineering
TopicIntegrated Circuits and Semiconductor Failure Analysis
Canadian institutionsAdvanced Micro Devices (Canada)
Fundersnot available
KeywordsMaterials scienceChemical-mechanical planarizationPolishingPlasmaIntegrated circuitControllabilityOptoelectronicsNanotechnologyFocused ion beamProcess (computing)Computer scienceIonChemistryPhysicsComposite material

Abstract

fetched live from OpenAlex

Abstract Deprocessing and probing are two quintessential steps in the physical failure analysis (PFA) and competitive analysis of integrated circuits (ICs). Typically, these steps are accomplished using multiple tools, which include polishers, electron microscopes, and probers. To combat the aggressive back-end-of-line (BEOL) scaling which has significantly decreased the controllability of manual polishing, gas-assisted Xe plasma FIB has been employed to achieve large area uniform delayering. Combined with an in-situ probing capability within the plasma FIB, the iterative process of juggling between tools is streamlined into a seamless process. In this paper, the successful integration of Prober Shuttle and plasma FIB to isolate and visualize real defects on sub-20 nm microprocessor chips are presented.

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)0.001
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 score0.367
Threshold uncertainty score0.905

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
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 head0.012
GPT teacher head0.233
Teacher spread0.221 · 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