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 opensourced11https://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 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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.136 | 0.037 |
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".