Improved Inkjet-Printed Pattern Fidelity: Suppressing Bulges by Segmented and Symmetric Drop Placement
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".