Electro-Thermal Analysis of Microwave Limiter Based on the Time-Domain Impulse Response Method Combined With Physical-Model-Based Semiconductor Solver
Bibliographic record
Abstract
To effectively analyze the electro-thermal characteristics of a semiconductor p-i-n diode in the microwave limiter circuit, a cosimulation algorithm of the time-domain impulse response technique and physical-model-based semiconductor solver is proposed in this article. The physical-model-based semiconductor solver algorithm is based on the drift diffusion model (DDM). First, the multiphysical field coupling equations of the drift diffusion model and heat conduction model are used to analyze the electro-thermal behavior of a semiconductor p-i-n diode. Second, the time-domain impulse response technique based on the field-circuit coupling algorithm is used to extract the time-domain impulse response at each port of the electromagnetic field structure. Finally, the time-domain impulse response is combined with the volt-ampere characteristic relationship of the physical-model-based p-i-n diode. As a result, an efficient computation of the time-domain electro-thermal coupling characteristics of p-i-n diode in the microwave limiter can be obtained. The simulation results are in good agreement with those by the commercial software (COMSOL). The computation time and the memory requirement of the proposed algorithm are significantly reduced when compared with COMSOL.
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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) | 0.000 | 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 | 0.000 |
| 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".