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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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, 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".