Size and Shape Effect in the Determination of the Fracture Strength of Silicon Nitride in MEMS Structures at High Temperatures
Bibliographic record
Abstract
Several applications require Micro electro mechanical systems MEMS devices to operate under high temperatures. Previous efforts to study the properties of MEMS materials have shown that an increase in temperature leads to a decrease in the fracture strength. In this paper, three different thin film silicon nitride dog-bone shaped structures suspended on a silicon substrate are used to determine their fracture strength as a function of temperature using tensile strength testing. Analytical modeling is used to determine the elastic resistance of the thin film dog-bones to stresses caused by the forces originated by the difference in coefficient of thermal expansion between the dog-bones and their substrate. The calculated forces incorporate the effect of temperature in these interactions and the mechanical properties of the two materials by considering the temperature dependence of elastic modulus and coefficient of thermal expansion. Analytical results show that the fracture strength of a 175nm silicon nitride thin film decreases from 289MPa to 146MPa as temperatures increase from 250 to 500 degrees Celsius. When compared to other studies, these values show a consistent trend, thus also presenting this as a simple and promising in-situ method for the determination of fracture strength of MEMS devices operating at high temperatures.
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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 | 0.001 |
| 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) | 0.001 | 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 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".