Curved OLED microdisplays
Bibliographic record
Abstract
Abstract Technical background for CMOS substrate thinning of CEA‐LETI (historically developed for through silicon via technology as well as for more recent activity to provide curved image sensors, for IR as well as for visible spectra) has been applied to realize curved OLED‐based microdisplays. It will be shown that test OLEDs made onto silicon wafers as well as 873 × 500 WVGA, 0.38″ diagonal, and an innovative 1920 × 1200 WUXGA, 1″diagonal, CMOS‐based microdisplays can be curved at R = 45 mm radius of curvature (1D) with no negative impact onto the circuit electrical characteristics. This feature can allow significant innovation on the system and application because it can help to redesign simpler and lighter optical engine systems, in the same manner as for curved image sensors. These results can be obtained owing to the integration of a new protective hard coat layer that has been used in conjunction with a robust thin‐film encapsulation to protect OLEDs from mechanical ingress (from process steps and handling) and oxidizing gas of the atmosphere, respectively. Results have been produced within the framework of the EU‐funded, H2020 project, called Large cost‐effective OLED MIcroDisplays (LOMID and their applications).
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 | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".