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Record W2611256551 · doi:10.1021/acs.langmuir.7b00412

Grafted Polystyrene Monolayer Brush as Both Negative and Positive Tone Electron Beam Resist

2017· article· en· W2611256551 on OpenAlexaff
Ferhat Aydinoglu, Hirotaka Yamada, Ripon Kumar Dey, Bo Cui

Bibliographic record

VenueLangmuir · 2017
Typearticle
Languageen
FieldEngineering
TopicMolecular Junctions and Nanostructures
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsResistPolystyreneMaterials scienceMonolayerBrushLayer (electronics)PhotoresistCoatingSiliconNanolithographyElectron-beam lithographyScanning electron microscopeSubstrate (aquarium)NanotechnologyComposite materialPolymer chemistryPolymerOptoelectronicsFabrication

Abstract

fetched live from OpenAlex

Although spin coating is the most widely used electron-beam resist coating technique in nanolithography, it cannot typically be applied for nonflat or irregular surfaces. Here, we demonstrate that monolayer polystyrene brush can be grafted on substrates and used as both positive and negative electron-beam resist, which can be applied for such unconventional surfaces. Polystyrene is a popular negative resist when using solvent developer but solvent cannot be used for grafted polystyrene brush that is firmly bonded to the substrate. Instead, we employed two unconventional development methods to lead polystyrene brush to positive or negative tone behavior. Negative tone was achieved by thermal development at 300 °C because exposed thus cross-linked polystyrene brush is more thermally stable against vaporization than unexposed linear one. Surprisingly, positive tone behavior occurred when the brush was grafted onto an aluminum (Al) layer and the film stack was developed using diluted hydrofluoric acid (HF) that etched the underlying Al layer. By transferring the patterns into the silicon (Si) substrates using the thin Al layer as a sacrificial hard mask for dry etch, well-defined structures in Si were obtained in two different electron-beam resist tones as well as in nonflat surfaces.

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 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)0.000
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 score0.459
Threshold uncertainty score0.466

Codex and Gemma teacher scores by category

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)0.0000.000

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 head0.004
GPT teacher head0.228
Teacher spread0.224 · 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 teacher head, 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

Citations11
Published2017
Admission routes1
Has abstractyes

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