A general framework for evaluating nonlinearity, noise and dynamic range in continuous‐time OTA‐C filters for computer‐aided design and optimization
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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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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) | 0.000 | 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 it