Neuro-Space Mapping technique for nonlinear device modeling and large signal simulation
Why this work is in the frame
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Bibliographic record
Abstract
A new Neuro-Space Mapping (Neuro-SM) approach is presented enabling the space mapping (SM) concept to be applied to nonlinear device modeling and large signal circuit simulation. Suppose that an existing device model (namely, the coarse model) cannot match the actual device behavior (namely, the fine model). Using the proposed technique, the voltage and current signals between the coarse and the fine device models are mapped by a neural network. This mapping automatically modifies the behavior of the coarse model such that the mapped model accurately matches the actual device behavior. New training methods for such mapping neural networks are proposed. Examples of SiGe HBT and GaAs MESFET modeling and use of the models in harmonic balance simulation demonstrate that Neuro-SM is a systematic method to allow us to exceed the present capabilities of the existing device models.
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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