Microfabrication : using bulk wet etching with TMAH
Bibliographic record
Abstract
In November 2002 a Microfabrication Lab was established in the physics department of McGill University to support research in nanoscience and technology. At the same time, I arrived at McGill to begin my graduate study. So I was assigned to do research on microfabrication, especially bulk wet etching of silicon using TetraMethyl Ammonium Hydroxide (TMAH). The content of microfabrication is quite broad, and also very useful in both industry and academic. Since our fab is a newly built one and I had no experience in this area before, this thesis mainly included some basic processes in microfabrication, such as the photolithography, wet etching, reactive ion etching, and soon. Also it compared the wet etching with dry etching. Some results of TMAH wet etching were showed in the thesis, which agreed well with that of the other groups. A simulation program was developed to predict the etching result of TMAH and it appeared to work well. Finally, based on the knowledge and experience acquired, processes in making cantilever and tip structures, which are critical in the scanning probe microscopes, were developed. Silicon oxide cantilevers with length of 100-200 mum, width of 30-50 mum, and thickness of 1 mum were obtained. Pyramid like silicon tips were also fabricated using the wet etching.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".