New design formulas for microstrip transmission lines using high-dielectric substrate
Bibliographic record
Abstract
Purpose Characterisation and use of dielectric materials with high permittivity are one of the most developed areas of research in microwave circuit simulation. This is mainly because of their various applications in VHF/UHF and microwave frequencies (correlators, instrumentation systems, …). The primary virtue of high-dielectric substrates for microwave circuits is the reduced size. Since the high dielectric microstrip line also exhibit low loss and useful impedance range, this class of circuits will undoubtedly find wide applications in microwave integrated circuitry. Design/methodology/approach Owing to the complexity of the electromagnetic problem, numerical methods become an indispensable tool for analysis and modeling of electromagnetic structures. They are the basis to set-up computer-aided design (CAD) packages. These models must be accurate, reliable, easily extracted and need limited computational requirements. Since there was a demand for a model able to describe these parameters accurately, an extension of the spectral domain approach (SDA) is proposed for microstrip lines with high permittivity. The analysis is based on the solution of a system of algebraic equations, which are derived from Galerkin's technique in the spectral domain. Findings Analytical expressions are deduced by curve-fitting techniques. These expressions can be easily implemented in a CAD simulation tool to design wireless communication components. In this paper, we have developed accurate and suitable general expressions for characteristic parameters for a wide range of εr between 1 and 500. The computed results were compared to those available in the literature when possible. In order to validate our models for high values of dielectric constant (128 < εr<500), neural models were generated for the characteristic impedance and effective permittivity. A very good agreement is demonstrated. Originality/value The originality of this paper consists on the development of design formulas to characterise the microstrip lines with high dielectric constant substrate. Closed form equations are almost non-existent in the technical literature since the available design formulas have been developed only for dielectric media value εr not exceed 128.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.006 | 0.003 |
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".