On the Feasibility of a New Technique for Applying and Sensing a Pre-Strain State for Strain Engineering
Bibliographic record
Abstract
This paper reports a successful design, fabrication, and characterization of a new microstructure, that utilizes piezoresistivity, for locally applying a pre-strain state onto silicon substrates and measuring it. This technique allows for applying tensile and compressive transverse local strain for the first time, using the same stressing layer rather than using nitride capping for tensile or silicon germanium for compressive strain. It is well-known that the only strain that enhances both electron and hole mobility simultaneously is the transverse uniaxial strain. Moreover, the utilized sensing structure evaluates directly the actual stress transmitted to the substrate. Therefore, it is a valuable tool for strain engineering applications, where modulating and determining the pre-strain state onto the silicon at infinitesimal areas are required. The proposed structure composed of highly compressive plasma enhanced chemical deposition nitride layer that was patterned in a way allowing for inducing both local tensile and compressive uniaxial pre-strain onto the silicon. Using this method, the stress transferred to silicon is around 40 to 150 MPa, however the developed sensing structure was capable of detecting it for both tensile and compressive stresses.
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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 | 0.000 |
| 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 | 0.001 |
| 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".