Recent progress on the vacuum deposition of OLEDs with feature sizes ≤ 20um using a contact shadow mask patterned in-situ by laser ablation
Bibliographic record
Abstract
We report progress on the development of a new technique that has the potential to enable the vacuum deposition of OLEDs with feature sizes ≤ 20um, and hence high resolution OLED displays. An OLED device with 16um by 130um sub-pixel size has been successfully demonstrated utilizing the novel idea of the in-situ shadow mask patterning method showing the capability of this method to achieve high resolution OLED patterning. In the approach proposed here, two sheets of polyimide film are mounted on an electrode, and the top sheet of the two stacked sheets is patterned in-situ by laser ablation to create apertures. The protective under sheet is then removed, and OLED materials are deposited through the patterned slits. Since mask alignment is not required in this approach, the technique circumvents the resolution limitations imposed by the difficulty of aligning shadow masks in the conventional techniques, and allows achieving high resolution pixel patterning. Furthermore, shadow effects, another factor that limits resolution in conventional techniques, can be reduced due to the thin plastic shadow mask (~7.5um) that is directly held on the substrate by electrostatic force. In principle, by applying this technique to the standard three color side-by-side sub-pixel matrix scheme, a resolution and aperture ratio of 338ppi and 60%, respectively, can be expected, which is estimated based on the fact that the width of the deposited material is 25um for the 16um wide electrode. In addition, the OLED device fabricated using this technique shows uniform emission and reasonable I-V characteristics similar to those of the OLED fabricated through the standard technique, which indicates that this method does not compromise device quality.
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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 | 0.001 | 0.000 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".