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Three-Step Room Temperature Wet Cleaning Process for Silicon Substrate

2009· article· en· W2011480885 on OpenAlex
Rui Hasebe, Akinobu Teramoto, Tomoyuki Suwa, Rihito Kuroda, Shigetoshi Sugawa, Tadahiro Ohmi

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
TopicAdvanced Surface Polishing Techniques
Canadian institutionsHatch (Canada)
Fundersnot available
KeywordsWaferMaterials scienceSiliconWet cleaningSubstrate (aquarium)MiniaturizationSurface roughnessVolume (thermodynamics)OptoelectronicsNanotechnologyProcess engineeringComposite materialChemistry

Abstract

fetched live from OpenAlex

With a progress of device dimension miniaturization, an ultraclean wafer surface is continuously increasing its importance crucial for high quality processing in Silicon Technologies [1]-[8]. Cleaning of silicon wafer surface has been accomplished by RCA wet cleaning in the past [9], where there exists high temperature processes consisting of H2SO4/H2O2/H2O, NH4OH/H2O2/H2O and HCl/H2O2/H2O treatments. Thus, RCA cleaning requires a large number of processing steps, resulting in the consumption of a huge volume of liquid chemicals and UPW, and simultaneously consuming a large volume of clean air exhaust to suppress chemical vapor from getting into the clean room. Moreover, RCA cleaning is used at high temperature and contain alkali solutions, which increase the roughness of the silicon wafer surface [10].

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), Scholarly communication
Consensus categoriesMeta-epidemiology (narrow)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.378
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0020.002
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.004
Open science0.0040.002
Research integrity0.0000.002
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 head0.309
Teacher spread0.275 · 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