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Shortening of Plasma Strip Process Resulting in Better Removal of Photo Resist after High Dose Implantation

2009· article· en· W2005707343 on OpenAlex
А.П. Захаров, M. Lenski, Sven Metzger, Christian Krüger

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 · 2009
Typearticle
Languageen
FieldEngineering
TopicMetal and Thin Film Mechanics
Canadian institutionsAdvanced Micro Devices (Canada)
Fundersnot available
KeywordsMaterials scienceAshingResistIon implantationPlasmaStripping (fiber)DopantComposite materialImplantLayer (electronics)IonDopingOptoelectronicsChemistrySurgery

Abstract

fetched live from OpenAlex

A layer of hardened material (crust) forms on the surface of photo resist (PR) during the implantation. This crust can be described as highly cross-linked polymer [1, 2]. Its thickness and composition depends on the type of PR, implant species, energy, dose, temperature during implantation and other factors. The crust is very resistant against chemical attack. Its chemical resistance tends to increase with the continuous shrink of technology nodes as implant doses increase. Moreover, even small residues of PR, left after cleaning, become more critical with shrinking device geometry. The usual process sequence for stripping a PR after high dose implantation (HDI) is a plasma strip (PS) followed by a wet clean. The drawback of plasma ashing is increased substrate loss and dopant bleach [3]. Plasma strip or plasma ash stand in this paper for the approach of complete PR consumption in the plasma process. Wet stripping alone often is not sufficient for stripping PR after implant doses of ≥ 1x1015 ions/cm2.

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)0.002
metaresearch head score (Gemma)0.000
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 score0.751
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.002
Open science0.0020.001
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 head0.024
GPT teacher head0.273
Teacher spread0.249 · 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