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Resist Stripping for Advanced FEOL Nodes: Improvements to Process Based on Ozone Diffusion by Use of Additives

2005· article· en· W2018620666 on OpenAlex
O. Louveau, E. Lajoinie, Olivier Pollet, Jean Philippe Odet, Sylviane Cêtre, Laurent Lachal, B. Icard, Evelyne Tabouret, M. Veillerot, Hervé Fontaine, Didier Louis

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

VenueDiffusion and defect data, solid state data. Part B, Solid state phenomena/Solid state phenomena · 2005
Typearticle
Languageen
FieldEngineering
TopicSilicon and Solar Cell Technologies
Canadian institutionsMitel (Canada)
Fundersnot available
KeywordsResistMaterials scienceStripping (fiber)DiffusionOzoneProcess (computing)Process engineeringNanotechnologyDiffusion processChemical engineeringEngineering physicsComputer scienceThermodynamicsComposite materialOrganic chemistryEngineeringInnovation diffusion

Abstract

fetched live from OpenAlex

The International Technology Roadmap for Semiconductors (ITRS) predicts a steady increase in the use of new materials and processes as feature size reduction continues. Presently, for 45nm node and below, new high-k dielectrics, metals for gates, and strained silicon are introduced. These require novel processes to achieve the necessary effectiveness and compatibility for resist stripping

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesMeta-epidemiology (narrow)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.493
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.002
Open science0.0020.002
Research integrity0.0000.001
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 head0.035
GPT teacher head0.296
Teacher spread0.261 · 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