Synthesis of Wideband High-Quality Factor Delay- Tunable Fully Differential All-Pass Filters
Bibliographic record
Abstract
In this article, all possible wideband fully differential second-order voltage-mode active all-pass filters (APFs) based on a common-source differential amplifier are systematically derived using two-port network modeling techniques and symbolic math CAD tools. The proposed APF structure is based on the generic two-transistor differential-pair and all possible combinations of the surrounding impedances. In particular, 26 transfer functions and a total of 180 APF circuits, all of which can have a pole quality factor (Q) value greater than unity, are found to be possible. Hence, the proposed topology can be used to produce APFs with dispersive delay as well as flat delay for true time delay elements. The operation of some APFs is verified with simulations and a selected circuit with tunable peak delay is designed and fabricated in a 65-nm CMOS process. The implemented filter achieved a maximum peak delay of 88.4 ps at 31 GHz, a constant delay of 11.5 ps over the frequency band 5-20 GHz, and a delay Q-factor (QD) value of 3.74, while consuming 1.74 mW from a 1-V supply voltage. The fabricated circuit occupies an area of 0.11 × 0.348 mm2and experimentally demonstrates an APF with the highest Q value of any published second-order APF known to the authors.
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 | 0.000 |
| 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 | 0.000 |
| Open science | 0.000 | 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".