State-Space Dynamic Neural Network Technique for High-Speed IC Buffer Modeling
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Bibliographic record
Abstract
Artificial neural networks (ANN) have been recently recognized as useful tools for RF/microwave modeling and design. In this paper, a recent state-space dynamic neural network (SSDNN) approach for transient behavior modeling of high-speed nonlinear circuit is summarized. This technique extends the existing dynamic neural network (DNN) approach into a more generalized and robust state-space formulation. A training algorithm exploiting the adjoint sensitivity computation is utilized to enable SSDNN to efficiently learn from the transient input and output waveform data without relying on the circuit internal details. Through an exact circuit representation, the trained SSDNN model can be conveniently implemented and used in SPICE-like circuit simulators. We also review a set of stability criteria for checking local and global stabilities of the SSDNN model. An example of SSDNN modeling of physics-based high-speed driver circuit is presented. It's demonstrated that the SSDNN model can offer fast and accurate transient responses for high-speed interconnect design.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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 it