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Record W3007584751 · doi:10.1115/1.4046482

Improved Inkjet-Printed Pattern Fidelity: Suppressing Bulges by Segmented and Symmetric Drop Placement

2020· article· en· W3007584751 on OpenAlexafffund
Ragheb Abunahla, Md Saifur Rahman, Paria Naderi, Gerd Grau

Bibliographic record

VenueJournal of Micro and Nano-Manufacturing · 2020
Typearticle
Languageen
FieldEngineering
TopicNanomaterials and Printing Technologies
Canadian institutionsYork University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsInkwellDrop (telecommunication)Computer sciencePrinted electronicsInkjet printingMaterials sciencePrinted circuit board3d printedSubstrate (aquarium)3D printingNanotechnologyOptoelectronicsComposite materialEngineeringTelecommunicationsBiomedical engineering

Abstract

fetched live from OpenAlex

Abstract Inkjet printing is a promising technique for printed micro-electronics due to low cost, customizability and compatibility with large-area, flexible substrates. However, printed line shapes can suffer from bulges at the start of lines and at corner points in 2D line patterns. The printed pattern can be multiple times wider than the designed linewidth. This can severely impact manufacturing accuracy and achievable circuit density. Bulging can be difficult to prevent without changing the ink-substrate-system, the drying conditions or the circuit design, all of which can be undesirable. Here, we demonstrate a novel printing methodology that solves this issue by changing the order in which drops are placed on the substrate. The pattern is split up into segments of three drops where the central drop is printed last. This symmetric printing prevents the unwanted ink flow that causes bulging. Larger bulge-free patterns are created by successively connecting segments. Line formation in both traditional linear printing and our novel segmented and symmetric printing was analyzed to understand and optimize results. The printing of X-, T-, and L-shapes is considerably improved compared with the traditional linear printing methodology.

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 score0.034
Threshold uncertainty score0.738

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 head0.009
GPT teacher head0.200
Teacher spread0.191 · 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

Citations13
Published2020
Admission routes2
Has abstractyes

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