Fuse-tethers in MEMS
Bibliographic record
Abstract
During the fabrication of freestanding micromechanical structures, the structures must often be attached to the substrate to prevent movement, particularly during the release process. The attachments are then removed, freeing the structures from the substrate when they are to be used. Tethers are long thin beams that mechanically anchor freestanding structures to the substrate during fabrication, but are easily broken afterwards. This paper focused on fuse-tether designs and the associated technique used to break the tethers, Joule heating. The breaking characteristics of two fuse-tether designs were investigated using different current pulses. For each design, the current pulse that produced the most desirable electrical and mechanical break was chosen for reliability testing. The reliability tests resulted in a 100% success rate. However, molten silicon splattered undesirably in 20% of the cases. In addition to empirical testing, ANSYS® was used to simulate the Joule heating process. The ANSYS® model produced results that closely matched the break characteristics observed in the empirical tests. This research demonstrated that a fuse-tether can be severed reliably with the Joule heating technique, and the fuse-breaking characteristics can be predicted by modeling.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".