Scalable nanopatterning of organic light-emitting diodes beyond the diffraction limit
Bibliographic record
Abstract
Miniaturization of light-emitting diodes below the diffraction limit of the emission wavelength can enable super-resolution imaging and on-chip light sources for ultrabroadband chiplet communication. Organic light-emitting diodes, although suitable for miniaturization due to their emission from localized excitons, suffer from the limited compatibility of organic materials with traditional photolithographic patterning. Here we develop a method for the scalable fabrication of nanoscale organic light-emitting diodes with pixel densities up to 100,000 pixels per inch, periodicity of 250 nm and the smallest pixel size in the order of 100 nm. We realize the direct nanoscale patterning of organic semiconductors by self-aligned nanostencil etching and lithography. The process is resist-free and involves etching and evaporation through nanoapertures in a free-standing film adhering to the substrate. A nanoscale organic light-emitting diode surface with over 1 megapixel exhibits an average external quantum efficiency of 13.1%. We also demonstrate electroluminescent metasurfaces with subwavelength-scale meta-atoms that can electrically modulate the emitted light. The diffractive coupling between nanopixels enables control over the far-field emission properties of light, including directionality and polarization. These results pave the way for hybrid integrated photonics technologies, including visible-light communication, lasing and high-resolution displays.
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 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 | 0.001 |
| 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".