P‐125: Maskless RGB Color Patterning via Dye Diffusion for Vacuum‐Deposited Small Molecule OLED Displays
Bibliographic record
Abstract
A maskless RGB color patterning technique based on dye diffusion is proposed here for vacuum‐deposited small molecule OLED displays. This approach utilizes selective diffusion of dyes through thermal diffusion via physical contact for color patterning. The proposed maskless color patterning technique enables us to overcome challenging issues in the conventional color patterning technique using fine metal shadow masks. The maskless color patterning technique based on dye diffusion has been suggested as a color patterning technique for polymer OLEDs. However, it has not yet been applied to vacuum‐deposited small molecule OLEDs likely due to several expected concerns such as limited diffusion and contact‐induced damage in small molecule films. The purpose of the present study is therefore to test whether the color pattering technique based on dye diffusion can be applied to vacuum‐deposited small molecule OLEDs. In order to investigate that, red, green, and blue OLEDs are fabricated side by side on one substrate by doping dyes into host through thermal diffusion via physical contact. Device performance of the fabricated devices, including electroluminescence spectra and IVL characteristics, is tested to investigate if molecular diffusion is sufficient for obtaining desired color spectrum and investigate effects of the physical contact.
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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 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 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".