A general framework for evaluating nonlinearity, noise and dynamic range in continuous‐time OTA‐C filters for computer‐aided design and optimization
Bibliographic record
Abstract
Abstract Efficient procedures for evaluating nonlinear distortion and noise valid for any OTA‐C filter of arbitrary order are developed based on matrix description of a general OTA‐C filter model. Since those procedures use OTA macromodels, they allow us to obtain the results significantly faster than transistor‐level simulation. On the other hand, the general OTA‐C filter model allows us to apply matrix transforms that manipulate (rescale) filter element values and/or change topology without changing its transfer function. Due to this, the proposed procedures can be used in direct optimization of OTA‐C filters with respect to important characteristics such as noise performance, THD, IM3, DR or SNR. As an example, a simple optimization procedure using equivalence transformations is discussed. An application example of the proposed approach to optimal block sequencing and gain distribution of 8th order cascade Butterworth filter is given. Accuracy of the theoretical tools has been verified by comparing to transistor‐level simulation results and to experimental results. Copyright © 2006 John Wiley & Sons, Ltd.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".