An Automated Topology Synthesis Framework for Analog Integrated Circuits
Why this work is in the frame
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Bibliographic record
Abstract
This article presents an analog integrated circuit automated topology synthesis framework, where circuit topology synthesis can be efficiently realized by encoding circuit topology generation process as tree structure construction. Then the tree structures are decoded into circuit topologies. Our proposed method can not only handle large circuit designs but also generate creative topologies. To ensure only unique circuit topologies to be generated, two levels of isomorphism checks are performed at both tree structure level and circuit topology level. Then the generated un-sized circuit topologies are efficiently evaluated through a new method, which integrates topological symbolic analysis with g <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">m</sub> /I <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">D</sub> methodology and curve-fitting technique. Along with the small-signal analysis, both linear and nonlinear programming techniques are utilized for topology feasibility checking. With only a small number of circuit topologies through the fast evaluation stage toward the subsequent detailed sizing and further evaluation, the efficiency of the whole circuit synthesis process can be significantly improved. The experimental results demonstrate high efficiency, strong reliability, and wide applicability of our proposed methods.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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 it