Stress anisotropy compensation of the sputter-deposited metal thin films by variable bias voltage
Bibliographic record
Abstract
We introduce a new technique for compensating stress anisotropy across the thickness of sputter-deposited metal films. Our technique balances the film vertical stress gradient by altering the substrate bias during sputtering and by controlling the ion flux and energy that bombards the growing film. Sputter-deposited metal films are appealing materials for microfabrication of freestanding and out-of-plane structures, especially because of their low thermal budget. These microstructures extend the design space of micro-electro-mechanical systems (MEMS)-based devices, and they overcome some of the limitations of in-plane processing. Unfortunately, most elemental metals and alloys when sputter deposited have a substantial stress gradient across their thickness that can deteriorate their mechanical properties and severely distort the shape of the fabricated freestanding microstructures. The stress gradient across the thickness of a sputtered film can be compensated (balanced) by embedding a layer in the film with the opposite stress polarity compared to that of the bulk of the film. The force exerted by the stress mismatch between this layer and the bulk of the film easily overcomes the film's vertical stress gradient. This compensating force guarantees that a released freestanding structure remains flat and does not curl upward. This virtual layer is introduced to the growing film by altering the substrate bias voltage during the sputtering process. The substrate bias voltage controls the ion flux and energy that bombards the film, and it enables tailoring the film stress parameters. This technique has enabled us to fabricate freestanding microstructures up to 500 µm long with negligible stress-related deformation.
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 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 | 0.000 | 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 | 0.000 |
| 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, unvalidatedMachine predicted; a candidate call from one teacher head, 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".