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Record W2115366588 · doi:10.1186/1556-276x-8-139

SML resist processing for high-aspect-ratio and high-sensitivity electron beam lithography

2013· article· en· W2115366588 on OpenAlexafffund
Mohammad Mohammad, S. K. Dew, Maria Stepanova

Bibliographic record

VenueNanoscale Research Letters · 2013
Typearticle
Languageen
FieldEngineering
TopicAdvancements in Photolithography Techniques
Canadian institutionsNational Institute for NanotechnologyUniversity of Alberta
FundersAlberta InnovatesNatural Sciences and Engineering Research Council of CanadaUniversity of ManchesterUniversity of Alberta
KeywordsResistMaterials scienceMethyl isobutyl ketoneElectron-beam lithographyLithographyXyleneNanolithographyIsopropyl alcoholNanotechnologyOptoelectronicsTolueneKetoneFabricationOrganic chemistryChemistry

Abstract

fetched live from OpenAlex

A detailed process characterization of SML electron beam resist for high-aspect-ratio nanopatterning at high sensitivity is presented. SML contrast curves were generated for methyl isobutyl ketone (MIBK), MIBK/isopropyl alcohol (IPA) (1:3), IPA/water (7:3), n-amyl acetate, xylene, and xylene/methanol (3:1) developers. Using IPA/water developer, the sensitivity of SML was improved considerably and found to be comparable to benchmark polymethylmethacrylate (PMMA) resist without affecting the aspect ratio performance. Employing 30-keV exposures and ultrasonic IPA/water development, an aspect ratio of 9:1 in 50-nm half-pitch dense grating patterns was achieved representing a greater than two times improvement over PMMA. Through demonstration of 25-nm lift-off features, the pattern transfer performance of SML is also addressed.

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: Methods · Consensus signal: none
Teacher disagreement score0.001
Threshold uncertainty score0.004

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)0.0010.001

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 head0.014
GPT teacher head0.288
Teacher spread0.274 · 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
GenreMethods

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

Citations19
Published2013
Admission routes2
Has abstractyes

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