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Precision Xe Plasma FIB Delayering for Physical Failure Analysis of Sub-20 nm Microprocessor Devices

2017· article· en· W2755307068 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 · 2017
Typearticle
Languageen
FieldEngineering
TopicIntegrated Circuits and Semiconductor Failure Analysis
Canadian institutionsAdvanced Micro Devices (Canada)
Fundersnot available
KeywordsPolishingMaterials scienceChemical-mechanical planarizationPlasmaPlanarDielectricMicroprocessorOptoelectronicsPorosityNanotechnologyComputer scienceComposite materialEmbedded systemPhysics

Abstract

fetched live from OpenAlex

Abstract Deprocessing is an essential step in the physical failure analysis of ICs. Typically, this is accomplished by techniques such as wet chemical methods, RIE, and mechanical manual polishing. Manual polishing suffers from highly non-uniform delayering particularly for sub 20nm technologies due to aggressive back-end-of-line scaling and porous ultra low-k dielectric films. Recently gas assisted Xe plasma FIB has demonstrated uniform delayering of the metal and dielectric layers, achieving a planar surface of heterogeneous materials. In this paper, the successful application of this technique to delayer sub-20 nm microprocessor chips with real defects to root cause the failure is 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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.869
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.000
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 head0.015
GPT teacher head0.252
Teacher spread0.237 · 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