Fabrication of Piezoresistive Sensors in Standard MEMS Foundry Processes
Bibliographic record
Abstract
A technique for fabrication of piezoresistive sensors in standard MEMS processes is introduced. A pair of beams from different structural layers are designed such that when one beam is under tension, the other one is under compression. By properly routing an electrical current through the structure, it is possible to measure the change in the resistance of beams as a result of applied stress. The proposed method does not require electrical isolation of piezoresistors from structural layers, and as confirmed by experiments, can be practically used for small deflections. Sample structures were fabricated in the MUMPs process and were employed to prove the validity of the design principle. Using the Maxwell-Mohr method, an analytical model is developed for the proposed structure and is verified by finite element simulations. Using modeling and experimental results, the piezoresistive coefficient of the top polysilicon layer in MUMPs process was calculated to be $11.5times10-11nPa-1. Having the proper structure, its model, and the piezoresistive coefficient of the material, it is now possible to design and optimize a wide variety of piezoresistive sensors, such as accelerometers and magnetic field sensors, in low-cost standard MEMS processes
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".