20 µm Micro‐LEDs Mass Transfer via Laser‐Induced In Situ Nanoparticles Resonance Enhancement
Bibliographic record
Abstract
Abstract Ultrafast laser is expected as a promising strategy for micro‐LEDs (µ‐LEDs) transfer due to its inherent property of suppressing thermal effects. However, its ultrahigh peak power and the unclear transfer mechanism make its transfer quality and efficiency unsatisfactory. Here, the study reports the high‐precision mass transfer of 20 µm fine‐pitch µ‐LEDs via in situ nanoparticles (NPs) resonance enhancement in burst mode ultraviolet picosecond laser irradiation. This technique suppresses the thermal melting effect and rapid cooling behavior of plasma by temporal modulation of the burst mode, generating NPs‐induced resonance enhancement that accurately and controllable drives a single unit up to tens of thousands of µ‐LEDs. The transfer of large µ‐LED arrays with more than 180 000 chips is also demonstrated, showing a transfer yield close to 99.9%, a transfer speed of 700 pcs s −1 , and a transfer error of <±1.2 µm. The transferred µ‐LEDs perform excellent optoelectronic properties and enable reliable device operation regardless of complex strain environments, providing a reliable strategy for preparing broader classes of 3D integrated photonics devices.
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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".