A New Model for Designing Multiwindow Multipermeability Nonlinear LTCC Inductors
Bibliographic record
Abstract
Nonlinear inductors have wide applications in dc/dc converters. The multiwindow multipermeability nonlinear inductor based on low-temperature co-fired ceramic (LTCC) technology has been proven to have a gradually changing wide-range inductance value curve while requiring no extra dc bias, which makes them suitable in various high-efficiency dc/dc converters. This paper focuses on the design of such a multiwindow multipermeability inductor based on an innovative module. Currently, the design of the LTCC inductors requires complicated simulation when coupling matrices among all the windows are taken into consideration. A simplified model for calculating the inductance value is proposed in this paper to reduce complexity. In the proposed model, the complicated inductance simulation is simplified to calculating inductance of three basic units in a nine-window inductor. By simulating the nine-window inductor, inductance of a rectangle-shaped inductor with any number of evenly distributed windows on it can be calculated. Based on the proposed model, the design guideline of the nonlinear inductor is also summarized. A 16-window two-permeability prototype is presented to demonstrate the design process according to the new model. The complete simulation and the test results are also provided to verify the model. The proposed model correlates with the simulation results very well.
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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) | 0.001 | 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 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".