3D MOEMS-based optical micro-bench platform for the miniaturization of sensing devices
Bibliographic record
Abstract
As we enter into the 21st century, the need for miniaturized portable diagnostic devices is increasing continuously. Portable devices find important applications for point-of-care diagnostics, patient self-monitoring and in remote areas, such as unpopulated regions where the cost of large laboratory facilities is not justifiable, underdeveloped countries and other remote locations such as space missions. The advantage of miniaturized sensing optical systems includes not only the reduced weight and size but also reduced cost, decreased time to results and robustness (e.g. no need for frequent re-alignments). Recent advances in micro-fabrication and assembly technologies have enabled important developments in the field of miniaturized sensing systems. INO has developed a technology platform for the three dimensional integration of MOEMS on an optical microbench. Building blocks of the platform include microlenses, micromirrors, dichroic beamsplitters, filters and optical fibers, which can be positioned using passive alignment structures to build the desired miniaturised system. The technology involves standard microfabrication, thick resist UV-lithography, thick metal electroplating, soldering, replication in sol-gel materials and flip-chip bonding processes. The technology is compatible with wafer-to-wafer bonding. A placement accuracy of ± 5 μm has been demonstrated thanks to the integration of alignment marks co registered with other optical elements fabricated on different wafers. In this paper, the building blocks of the technology will be detailed. The design and fabrication of a 5x5 channels light processing unit including optical fibers, mirrors and collimating microlenses will be described. Application of the technology to various kinds of sensing devices will be discussed.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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, 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".