Analog Circuit Design Using Symbolic Math Toolboxes: Demonstrative Examples
Bibliographic record
Abstract
In this article, a synthesis methodology for analog circuit design is presented. This methodology utilizes symbolic math tools to systematically and exhaustively search for candidate analog circuits avoiding tedious manual work. The two-port network matrix representation of active devices, such as MOS transistors, paves the way for efficiently using advanced symbolic math toolboxes (e.g., in MAPLE or MATLAB) to automate the generation of new analog circuits and further investigate the effects of nonidealities and parasitics. Using this synthesis methodology new amplifiers, filters, and oscillators can be obtained starting from a predefined structure. This article aims to motivate and provide an overview of the current status of research in this area. In addition, two detailed design examples of a family of differential filters and a family of differential oscillators along with their simulations and measurement results are provided to illustrate and verify the synthesis methodology.
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How this classification was reachedexpand
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".