Piezoelectric Micromachined Ultrasonic Transducers (PMUTs): Performance Metrics, Advancements, and Applications
Bibliographic record
Abstract
With the development of technology, systems gravitate towards increasing in their complexity, miniaturization, and level of automation. Amongst these systems, ultrasonic devices have adhered to this trend of advancement. Ultrasonic systems require transducers to generate and sense ultrasonic signals. These transducers heavily impact the system's performance. Advancements in microelectromechanical systems have led to the development of micromachined ultrasonic transducers (MUTs), which are utilized in miniaturized ultrasound systems. Piezoelectric micromachined ultrasonic transducers (PMUTs) exhibit higher capacitance and lower electrical impedance, which enhances the transducer's sensitivity by minimizing the effect of parasitic capacitance and facilitating their integration with low-voltage electronics. PMUTs utilize high-yield batch microfabrication with the use of thin piezoelectric films. The deposition of thin piezoelectric material compatible with complementary metal-oxide semiconductors (CMOS) has opened novel avenues for the development of miniaturized compact systems with the same substrate for application and control electronics. PMUTs offer a wide variety of applications, including medical imaging, fingerprint sensing, range-finding, energy harvesting, and intrabody and underwater communication links. This paper reviews the current research and recent advancements on PMUTs and their applications. This paper investigates in detail the important transduction metrics and critical design parameters for high-performance PMUTs. Piezoelectric materials and microfabrication processes utilized to manufacture PMUTs are discussed. Promising PMUT applications and outlook on future advancements are presented.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| 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".