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Record W2767337205 · doi:10.1116/1.4998561

Oxidation sharpening of silicon tips in the atmospheric environment

2017· article· en· W2767337205 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

VenueJournal of Vacuum Science & Technology B Nanotechnology and Microelectronics Materials Processing Measurement and Phenomena · 2017
Typearticle
Languageen
FieldEngineering
TopicIntegrated Circuits and Semiconductor Failure Analysis
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsThermal oxidationSiliconWaferMaterials scienceMicroelectronicsNanotechnologySharpeningOxideGloveboxOptoelectronicsChemistryMetallurgyComputer science

Abstract

fetched live from OpenAlex

Sharp tips are essential for high-resolution atomic force microscopy (AFM) imaging and high-performance electron emitters in vacuum microelectronic devices. Thermal oxidation at high temperature followed by oxide removal is widely used in the nanofabrication of sharp silicon AFM/emitter tips. This method relies on the fact that oxide grows slower on areas with a smaller radius of curvature. Thermal oxidation is commonly carried out in a dedicated oxidation furnace that is costly, and the tips or wafer of tips must be cleaned thoroughly using Radio Corporation of America (RCA) cleaning. Here, the authors report that oxidation sharpening can also be attained using a very low-cost generic box furnace in the atmospheric environment that does not require the tips to go through an RCA cleaning process. As is apparent, such cleaning is not convenient for millimeter-scale AFM probes. The minimum tip apex radius of 2.5 nm was obtained by oxidation at 950 °C in the atmospheric environment. The obvious application of this approach is the regeneration of sharp tips out of worn out and thus blunt AFM probes at very low cost.

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 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 score0.023
Threshold uncertainty score0.464

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
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 head0.012
GPT teacher head0.209
Teacher spread0.197 · 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