Anti-Series Varactor Network With Improved Linearity Performances in the Presence of Inductive and Capacitive Parasitics
Bibliographic record
Abstract
This paper proposes a varactor-based circuit technique intended for amplitude and phase control, with improved linearity in the presence of parasitic capacitances and parasitic inductances. The mechanism causing linearity degradation in an anti-series varactor network that includes significant parasitic elements - a key aspect that, to our knowledge, has never been reported - is first studied using an analytical approach based on multi-tone excitation. It is demonstrated that simply optimizing the ratio of diode sizes is insufficient to circumvent this linearity degradation. The underlying linearity degradation concept serves as the basis for the introduction of a modified anti-series controllable capacitance, followed by a design and practical implementation. Experimental validations with multi-tone and modulated signals demonstrate improved linearity performances with respect to the state-of-the-art when parasitic capacitances and inductances are significant. Moreover, it is shown that the complete varactor-based circuit topology proposed here, which uses the proposed modified anti-series controllable capacitance in conjunction with a second-harmonic trap filter, constitutes a very attractive alternative to the state-of-the-art anti-series/anti-parallel topology, since it reduces the required number of diodes by a factor of 2. Measurements on discrete-component designs operating at 3.6GHz, hence with significant parasitic effects, demonstrate that the proposed circuit topology improves the 3rdorder intermodulation distortion levels by 10.6dB and 6.6dB at output powers of 10dBm and 18dBm respectively, in comparison with the state-of-the-art topology. Measurements with a 16QAM modulated signal also show 3.9dB improvement in ACPR at 18dBm. These performances constitute improved state-of-the-art results in anti-series hyper-abrupt varactor-based electronic control.
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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) | 0.000 | 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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 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".