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Doping Profile Measurements in a 65-nm Commercial Product Using Atom Probe Tomography

2008· article· en· W2743252552 on OpenAlexaff
L. Klibanov, Dick James, Dieter Isheim

Bibliographic record

VenueProceedings - International Symposium for Testing and Failure Analysis · 2008
Typearticle
Languageen
FieldEngineering
TopicAdvanced Materials Characterization Techniques
Canadian institutionsChipworks (Canada)
Fundersnot available
KeywordsAtom probeDopingDopantMaterials scienceResolution (logic)Atom (system on chip)TransistorImage resolutionOptoelectronicsMicroscopyTomographySemiconductorAnalytical Chemistry (journal)SiliconOpticsNanotechnologyComputer sciencePhysicsChemistryArtificial intelligence

Abstract

fetched live from OpenAlex

Abstract Doping profile measurements in extremely small features like transistor gates or source/drain regions is a challenging task for the semiconductor industry. In our article, we successfully used an atom probe tomography (APT) tool to measure the doping concentration and profile of the dopant elements in a commercial 65 nm product. APT not only delivers doping concentrations but also gives the highest spatial resolution (sub-1 nm) three-dimensional compositional information of any microscopy technique.

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.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

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)0.0010.000

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 head0.038
GPT teacher head0.258
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

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations1
Published2008
Admission routes1
Has abstractyes

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