On-Chip Structures for the Determination of the Dopant-Dependent Young’s Modulus of Heavily Phosphorus Doped Polysilicon With Stress Compensation
Bibliographic record
Abstract
Accurate knowledge of thin-film material properties, such as Young’s modulus, is imperative in proper design and operation of MEMS devices. The use of on-chip devices allows direct access to the material properties as they are known to change with fabrication process, temperature as well as location within the wafer. Resonant and pull-in structures have been designed and modeled for the measurement of the Young’s modulus of heavily doped polysilicon thin films. The cantilever and clamped-clamped beams allow us to extract the Young’s modulus through observing the resonant frequency and pull-in voltage and cross-referencing the results. Mechanical actuation using a calibrated piezoelectric shaker for some devices and electrostatic actuation for others ensures that the structural effects, rather than the actuation technique, are responsible for the varying response at different temperatures. Optical readout will be used in order to reduce readout-associated errors, which can occur with purely electrical techniques at higher temperatures. However, electrical readout is also possible for some of the devices. The devices have been designed and fabricated using a customized 1-mask process. In this paper, we present the modeling and numerical simulations obtained for heavily doped polysilicon microstructures and will describe the method used for the determination of the Young’s modulus with stress compensation. Although the method described here has been used for heavily doped polysilicon thin films, it can be easily modified for the determination of Young’s modulus of other MEMS structural materials.
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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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".