Stripline Y-Junction Circulators: Accurate Model and Electromagnetic Analysis Based on Gaussian Field Distribution Boundary Conditions
Bibliographic record
Abstract
The conventional analysis of the circulator usually ignores ferrite disk thickness by assuming no axial variation, where the outcomes are limited to the case of small thickness ferrite disks. However, a typical scenario of a stripline Y-junction circulator has a ferrite disk thickness equal to that of the substrate. As a result, the assumption of a constant field along the feed-linewidth does not account for a realistic boundary condition. Increasing the ferrite thickness results in a significant variation in the field along the perimeter of the ferrite disk, which must be considered for accurate modeling and analysis. In this article, modeling of the Y-junction stripline circulator is proposed and investigated using Gaussian field distribution (GFD) boundary conditions. The proposed analysis takes into account the effects of ferrite thickness that controls both the field distribution and the demagnetization factor. The proposed analysis is applied to the case of a single ferrite disk and validated through a comparison between the analytical and simulation results conducted using a 3-D electromagnetic solver in different frequency bands. An excellent agreement is observed between the simulated and analytical results, highlighting the limitations of the conventional analysis methodology.
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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".