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Record W3198058735 · doi:10.1088/1361-6528/ac244d

Constrained shrinking of nanoimprinted pre-stressed polymer films to achieve programmable, high-resolution, miniaturized nanopatterns

2021· article· en· W3198058735 on OpenAlexafffund
Shady Sayed, P. Ravi Selvaganapathy

Bibliographic record

VenueNanotechnology · 2021
Typearticle
Languageen
FieldEngineering
TopicNanofabrication and Lithography Techniques
Canadian institutionsMcMaster University
FundersNatural Sciences and Engineering Research Council of CanadaOntario Research Foundation
KeywordsMaterials scienceNanoimprint lithographyMicroscale chemistryNanoscopic scaleNanotechnologyLithographyPolymerNanostructureShrinkageOptoelectronicsComposite materialFabrication

Abstract

fetched live from OpenAlex

Nanoimprint lithography is an emerging technology to form patterns and features in the nanoscale. Production of nanoscale patterns is challenging particularly in the sub-50 nm range. Pre-stressed polymer films with embedded microscale pattern can be miniaturized by shrinking induced due to thermal stress release. However, when pre-stressed films are thermally nanoimprinted with sub-micron features and shruken, they lose all the topographical features due to material recovery. Here we report a new approach that prevents recovery and allows retention of shrunken patterns even at the scale of <50 nm. We have discovered that when the shrinking process is mechanically constrained in one direction, the thermal treatment only relieves the stress in the orthogonal direction leading to a uniaxial shrinkage in that direction while preserving the topographical features. A second step, with the constraint in the orthogonal direction leads to biaxial shrinkage and preservation of all of the topographical features. This new technique can produce well defined and high resolution nanostructures at dimensions below 50 nm. The process is programmable and the thermal treatment can be tuned to shrink features to various dimension below the original imprint which we use to produce tunable and gradient plasmonic structures.

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 categoriesMeta-epidemiology (narrow)
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 score0.020
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
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 head0.005
GPT teacher head0.219
Teacher spread0.213 · 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.

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

Citations2
Published2021
Admission routes2
Has abstractyes

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