SymXplorer: A Designer's Toolbox for Automated Analog Circuit Topology Exploration
Bibliographic record
Abstract
The advancement of circuit parameter optimization has allowed achieving the optimal configuration of a pre-defined set of topologies. This project introduces SymXplorer, a topology exploration framework designed to systematically synthesize novel circuit topologies through symbolic modeling of analog circuit components. The framework is component-agnostic, capable of exploring architecture-level circuits. SymXplorer integrates stability checks and high-order filter exploration within a userfriendly application program interface (API). Moreover, we include Python wrappers to automatically size the circuits using open-source circuit simulators with Bayesian optimization or evolutionary algorithms. We conclude that Bayesian optimization performs better for circuits where the simulations take a long time, whereas simple schematic-level sizing problems converge faster with evolutionary algorithms. In a case study, we identify and design novel third-order low-pass filters (LPF) using a customized multi-feedback transimpedance amplifier topology. The prototype filter is designed for direct conversion wireless receivers and has a bandwidth of 10 MHz. The toolbox is opensourced<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup><sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup>https://github.com/NooriDan/SymXplorer and includes example circuit templates for promising topologies to encourage further innovation.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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 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".