A surface micromachining fabrication process for aluminium MEMS micromirrors /
Bibliographic record
Abstract
This thesis focuses on the implementation of a surface micromachining fabrication process for electrostatically actuated MEMS micromirrors in the McGill University's Nanotools microfabrication laboratory. The process consists in fabricating the devices out of aluminum using photoresist as a sacrificial material. To this effect simple cantilever micromirror structures were designed. They were then modeled and simulated using finite-element analyses from the commercially-available software ANSYS. Finally, in order to validate the results of the new process, the same structures were fabricated out of polysilicon using the Multi-User MEMS Processes (PolyMUMPS) technology available through the Canadian Microelectronics Corporation (CMC). The theoretical and experimental results from the PolyMUMPS micromirrors were compared. The results at low voltages were similar, but they diverged for larger voltages and deflections, with the simulations usually predicting stiffer structures. The characterization of the structures fabricated with the Nanotools process indicated that they remained stuck to the substrate after the release process. Manipulation during testing caused some of them to be partially released, at which point they could be electrostatically actuated. With a better understanding of the aluminum properties and modifications to the original designs, one can fabricate viable aluminum structures using this process. Different areas of improvement as well as future directions for MEMS fabrication in this laboratory were also identified.
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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 | 0.001 |
| 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, 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".