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Record W2077155586 · doi:10.1021/nn4064659

Electron Beam Lithography on Irregular Surfaces Using an Evaporated Resist

2014· article· en· W2077155586 on OpenAlexaff
Jian Zhang, Celal Con, Bo Cui

Bibliographic record

VenueACS Nano · 2014
Typearticle
Languageen
FieldEngineering
TopicNanofabrication and Lithography Techniques
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsResistMaterials scienceNanolithographyElectron-beam lithographyPolystyreneLithographyNanotechnologyEvaporationRaman spectroscopyCantileverCathode rayOptoelectronicsOpticsElectronPolymerComposite materialFabrication

Abstract

fetched live from OpenAlex

An electron beam resist is typically applied by spin-coating, which cannot be reliably applied on nonplanar, irregular, or fragile substrates. Here we demonstrate that the popular negative electron beam resist polystyrene can be coated by thermal evaporation. A high resolution of 30 nm half-pitch was achieved using the evaporated resist. As a proof of concept of patterning on irregular surfaces, we fabricated nanostructures on the AFM cantilever and the optical fiber. Although an ice (H2O) resist has also been recently demonstrated as being capable of nanopatterning on irregular and fragile substrates, it requires specially designed accessories mounted inside a SEM chamber, whereas our process works with any thermal evaporator and is thus simpler and much more accessible. Nanofabrication on nonplanar surfaces may find applications in fields such as (AFM) tip-enhanced Raman spectroscopy for chemical analysis and lab-on-fiber technology.

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 score0.002
Threshold uncertainty score0.006

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)0.0020.001

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 head0.238
Teacher spread0.226 · 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

Citations53
Published2014
Admission routes1
Has abstractyes

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