Attention Mechanism Combined With Deep Recurrent Network for Nonlinear Circuit Macromodeling
Bibliographic record
Abstract
This article proposes a novel macromodeling method for high-frequency nonlinear circuits, utilizing an attention-based deep recurrent neural network (ATDRNN). This method leverages the attention mechanism within the RNN, comparing each time step with other time steps to determine their similarities. It then applies some coefficients as weights to the features of each time step based on these similarities, enhancing the RNN’s ability to focus on more informative features. Consequently, this approach allows for more accurate modeling of nonlinear circuits. Additionally, having comprehensive signal information and similarities between various time steps mitigates the vanishing gradient problem commonly faced by RNNs. The models derived from this method not only exhibit superior accuracy compared to the conventional RNNs, but also run much faster than existing transistor-level models in circuit simulators. The effectiveness of the proposed method is demonstrated by modeling two nonlinear circuits, namely 2-coupled and 3-coupled line high-speed interconnects driven by multi-stage buffers.
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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 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".