Design and Modeling of Piezoelectric Micromachined Ultrasonic Transducer (PMUT) using a Multi-User MEMS Process for Medical Imaging
Bibliographic record
Abstract
According to the Canadian Cancer Society, 2020, “1 in 8 women will be affected by breast cancer and 1 in 33 will die from it.” There has been a decline in breast cancer causalities due to the early detection using advanced imaging technologies. This signifies the importance of early detection of breast cancer that increases the survival rate and treatment options for the patients. One of the platforms which are aiding the early detection is Microelectromechanical Systems (MEMS)-base imaging system. In this thesis, a Piezoelectric Micromachined Ultrasonic Transducer (PMUT) is proposed to work at lower frequency ranges for higher penetration aiding imaging applications while operating at a lower voltage. In this work, a comprehensive study based on the Multi-User MEMS Process (MUMPs) has been conducted to investigate the effect of critical design parameters on output performance. Three sets of PMUTs are fabricated based on the investigated parameters. The resonant frequency and acoustic output pressure of these fabricated devices are evaluated and compared based on their respective areas of the piezo layer using COMSOL Multiphysics. The resonant frequency of the fabricated PMUT ranges from 0.5 MHz to 2 MHz. Keysight Impedance Analyzer E4990A has been utilized for the electrical characterization of the fabricated PMUT devices to determine their respective resonant frequencies and validate the COMSOL simulation results. It is shown that the fabricated individual circular PMUT achieves a high acoustic output pressure of 39 kPa at 1.3 MHz and the rectangular PMUT provides 4.7 kPa of acoustic pressure at 1.4 MHz. The results indicate that the proposed PMUT design can deliver acoustic pressure at a lower frequency
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".