Bibliographic record
Abstract
Behavioral modeling of analog circuits is an important step of the integrated circuit design flow. Indeed, closed-box behavior modeling allows users to replicate the behavior of circuit elements and devices without explicitly knowing the inner workings of the device. Prior works have automated the generation of behavioral models using machine learning (ML) at both the device and circuit level. More specifically, a recent work has used high-order polynomial projection operators (HIPPOs) to augment gated recurrent unit (GRU)-based macro-models. This new HIPPO-based model has been shown to outperform state-of-the-art GRU-based circuit macro-models. In this article, we introduce a new type of modified recurrent neural network (RNN) circuit macro-model that uses the HIPPO framework, called HIPPO-RNN. Additionally, we present a modified HIPPO-RNN (stable-HIPPO-RNN) model that is more suitable for enforcing input-to-state stability (ISS), and derive corresponding stability constraints. These constraints effectively guarantee ISS stability of the macro-model during transient simulation. We show the validity and superior performance of our macro-models on two circuit modeling examples.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".