Noise and sensitivity analysis of periodically switched linear circuits in frequency domain
Bibliographic record
Abstract
This paper presents new theories and efficient computational methods for noise and sensitivity analysis of multiphase periodically switched linear (PSL) circuits in frequency domain. Tellegen's theorem for PSL circuits in the phasor domain, frequency reversal theorem, and transfer function theorem, are introduced. The adjoint network of PSL circuits is developed using a frequency-domain approach. An adjoint network-based noise analysis algorithm for PSL circuits is proposed. It is shown that the computational overhead associated with multiple noise sources is eliminated by using the transfer function theorem. It is also shown that the excessive cost of computation due to aliasing effects is significantly reduced when the frequency reversal theorem is employed. In sensitivity analysis, the incremental form of Tellegen's theorem for PSL circuits in the phasor domain is introduced and frequency-domain sensitivity of PSL circuits Is obtained. It is shown that frequency-domain sensitivity of PSL circuits is a series summation of the network variables. Both the baseband and sideband frequency components of the network variables contribute to baseband sensitivity. The method yields sensitivities of one output with respect to all circuit elements in one frequency analysis. Sensitivity networks of PSL circuits are introduced. It is demonstrated that both the adjoint and sensitivity network approaches give the same sensitivity. Numerical results computed using the proposed methods are compared with measurement data and those from other CAD tools.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".