On-Chip Young’s Modulus Characterization of the Effect of Phosphorus Heavy Doping on Polysilicon Thin Films
Bibliographic record
Abstract
Information on material properties of structural thin films for MEMS fabrication is very limited. The small information available in the literature suggests that the Young’s modulus of structural thin films such as polysilicon can change up to 30% with heavy doping at room temperature. Accurate knowledge of these variations is critical for proper design as well as operation of MEMS devices, especially for applications that require them to be exposed to harsh environmental conditions. In this paper, devices for the on-chip characterization of the Young’s modulus of polysilicon as a function of the doping concentration conditions are presented. Analytical modeling has been performed to predict the change in the devices’ pull-in voltage as a function of doping concentration. The devices were fabricated using the PolyMUMPs process on two different polysilicon layers on the same chip separated by a layer of oxide. The top layer devices are heavily doped while the bottom layer devices are left lightly doped. The lightly doped devices serve as a reference, allowing some account for fabrication uncertainties in order to ensure consistent results. Devices for measuring in-plane stresses, out-of-plane stress gradients and specially designed resistor structures that account for the effect of contact resistance have also been fabricated to monitor these quantities while testing. The devices will be tested using a customized vacuum chamber to study the effect of phosphorus concentration on these structures.
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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 | 0.000 |
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".