On-chip replication of high-sag micro-optical components fabricated by direct laser writing
Bibliographic record
Abstract
This paper describes the fabrication of very high-sag (up to 42 μm) microlenses by direct laser writing and their integration onto a simple microoptical bench processed by conventional microfabrication technologies pertaining to MOEMS. At the heart of such a work is INO's laser writer. It is based on a He-Cd laser operating at 442 nm whose intensity can be modulated up to 1024 levels, and on a 40 nm accuracy X-Y translation stage. Laser writing into thick photoresist layers introduces however particular problems in terms of the roughness achievable. Simulations show that the writing beam diameter, the line-to-line spacing and the translation stage accuracy contribute to some unavoidable residual roughness. By applying optimized laser writing parameters, arrays of 1 x 5 aspherical microlenses were fabricated in a thick positive photoresist, along with alignment marks concurrently generated for on-chip alignment purposes. The microlenses were successfully integrated with a microoptical bench by first generating a UV-transparent mold from the photoresist laser written master. The microlenses imprinted in the mold were then replicated in a layer of hybrid glass material cast on the microoptical bench by UV-embossing with a modified MA6 mask aligner. The uniformity of focal lengths was approximately 3% as determined from best fits of profilometric traces. The replication with alignment of this array in a hybrid glass material was demonstrated on a 12 mm x 12 mm microoptical bench chip. An alignment accuracy of less than 5 μm was obtained. The replication error was less than about 4%. The measured surface roughness was 50-60 nm RMS, in good agreement with simulation results.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Bench or experimental | low |
| gpt | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Bench or experimental | high |
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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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, unvalidatedLabeled directly by 2 models reading the full record.
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".