Studying the Effect of N-Type Strained Silicon on the Temperature Coefficient of Resistance
Bibliographic record
Abstract
As a step in employing the strained silicon in enhancing a MEMS piezoresistive based 3D stress sensor performance, this paper studies the influence of pre-stretching silicon atoms on the temperature coefficient of resistance (TCR). Extracting accurately the TCR is very influential for the piezoresistive based stress sensor. For this purpose, a piezoresistive sensing rosette was fabricated on strained and unstrained silicon substrates. The pre-strained state was integrated during microfabrication using an intrinsic stress produced by highly compressive plasma enhanced chemical vapor deposition (PECVD) silicon nitride layer, which induces global biaxial tensile pre-strain onto the substrate. Under a stress free thermal loading, the TCR for both strained and unstrained chips were calibrated using an environmental chamber. Comparing the calibration results in both strained and unstrained silicon, the tensile pre-strained silicon has larger TCR than that in unstrained silicon. Moreover, over the surface concentration range used in this work, the strained silicon shows the same unstrained silicon trend, which is, the TCR is increased proportionally with the surface concentration.
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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".