Application of adaptive multilevel substructuring technique to model CMOS micromachined thermistor gas sensor, part (II): effect of manufacturing uncertainties in the reliability of MEMS
Bibliographic record
Abstract
For pt.I, see ibid., no.54, p.279-84 (2003). A study has been conducted to investigate the effect of the manufacturing uncertainties on the reliability of MEMS. The parameters investigated, include the uncertainties in the characterization of the Young's modulus and the values of the residual stresses generated during the deposition process of MEMS thin films. The study was conducted on a CMOS micromachined thermistor gas sensor, recently proposed in the literature. A novel technique called adaptive multilevel substructuring was used to reduce the computational cost of the analysis. The numerical results suggest that, the uncertainty in the characterization of Young's modulus has a reduced effect on the fatigue life. At the other hand the change in the value of the residual stress has a significant effect in the maximum operational stress level encountered during the operation, equivalent alternating stress value and consequently on the expected operational life of the MEMS component. The maximum expected life was found to occur at residual stresses values ranging from 0 to 400 Mpa. At these residual stresses values, the equivalent alternating stress is found to be lower than the endurance limit of the material. These values of the residual stresses correspond to a deposition temperature of 850/spl deg/C and a SiH/sub 2/ Cl/sub 2//NH/sub 3/ ratio ranging from 2 to 4 for the Si/sub 3/N /sub 4/ film deposition process. The achieved results emphasize the important role that can be played by the numerical modeling of the end product. Using the numerical modeling, conclusions for the process parameters can be evaluated before proceeding to the actual microfabrication process.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".