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Record W2003420320 · doi:10.1088/0960-1317/19/6/065015

Solving the shrinkage-induced PDMS alignment registration issue in multilayer soft lithography

2009· article· en· W2003420320 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.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueJournal of Micromechanics and Microengineering · 2009
Typearticle
Languageen
FieldEngineering
TopicNanofabrication and Lithography Techniques
Canadian institutionsUniversity of Toronto
FundersCanadian Institutes of Health ResearchUniversity of TorontoNatural Sciences and Engineering Research Council of CanadaCanada Research Chairs
KeywordsFabricationPolydimethylsiloxaneMaterials scienceSoft lithographyLithographyMicrofabricationPhotolithographyOptoelectronicsNanotechnologyShrinkageComposite material

Abstract

fetched live from OpenAlex

Shrinkage of polydimethylsiloxane (PDMS) complicates alignment registration between layers during multilayer soft lithography fabrication. This often hinders the development of large-scale microfabricated arrayed devices. Here we report a rapid method to construct large-area, multilayered devices with stringent alignment requirements. This technique, which exploits a previously unrecognized aspect of sandwich mold fabrication, improves device yield, enables highly accurate alignment over large areas of multilayered devices and does not require strict regulation of fabrication conditions or extensive calibration processes. To demonstrate this technique, a microfabricated Braille display was developed and characterized. High device yield and accurate alignment within 15 µm were achieved over three layers for an array of 108 Braille units spread over a 6.5 cm2 area, demonstrating the fabrication of well-aligned devices with greater ease and efficiency than previously possible.

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)0.000
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 score0.151
Threshold uncertainty score0.579

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 head0.006
GPT teacher head0.209
Teacher spread0.203 · 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