Large improvement of the electrical impedance of imaging and high-intensity focused ultrasound (HIFU) phased arrays using multilayer piezoelectric ceramics coupled in lateral mode
Bibliographic record
Abstract
With a change in phased-array configuration from one dimension to two, the electrical impedance of the array elements is substantially increased because of their decreased width (w)-to-thickness (t) ratio. The most common way to compensate for this impedance increase is to employ electrical matching circuits at a high cost of fabrication complexity and effort. In this paper, we introduce a multilayer lateral-mode coupling method for phased-array construction. The direct comparison showed that the electrical impedance of a single-layer transducer driven in thickness mode is 1/(n²(1/(w/t))²) times that of an n-layer lateral mode transducer. A large reduction of the electrical impedance showed the impact and benefit of the lateral-mode coupling method. A one-dimensional linear 32-element 770-kHz imaging array and a 42-element 1.45-MHz high-intensity focused ultrasound (HIFU) phased array were fabricated. The averaged electrical impedances of each element were measured to be 58 Ω at the maximum phase angle of -1.2° for the imaging array and 105 Ω at 0° for the HIFU array. The imaging array had a center frequency of 770 kHz with an averaged -6-dB bandwidth of approximately 52%. For the HIFU array, the averaged maximum surface acoustic intensity was measured to be 32.8 W/cm² before failure.
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 | 0.000 | 0.001 |
| 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".