Advanced Space Mapping and Artificial Neural Network-Based Techniques for Microwave Device Modeling
Bibliographic record
Abstract
Microwave device modeling is an important topic in microwave area.This thesis focuses on developing advanced space mapping and articial neural network (ANN)based techniques for challenging microwave device modeling, addressing both microwave active and passive component modelings.Specically, this thesis develops an advanced space mapping-based technique in combination with knowledge-based neural network (KBNN) for modeling gallium nitride (GaN) high-electron-mobility transistor (HEMT) devices.This thesis also develops advanced ANN-based transfer function mapping techniques for parametric modeling of microwave passive components.GaN HEMTs are important for next-generation wireless communication systems and microwave power device applications.However, GaN HEMT model development can be time-consuming as the devices exhibit strong trapping eects, which often require very sophisticated model.As a consequence, a fast modeling approach for GaN HEMT devices with trapping eects is of signicance for high-reliability microwave circuit design.In the rst part of this thesis, we propose a novel space mapping modeling technique for GaN HEMTs with trapping eects.The proposed space mapping technique develops separate mappings for dierent branches inside the existing device model, such that dierent behaviors (i.e., trapping eects and frequency dispersion) in GaN HEMTs can be mapped separately.Through supervised learning methods, each mapping module is systematically developed to overcome the gap between each internal branch and each set of target data, accelerating the process of model development.The KBNN model is proposed for characterizing drain i My deepest appreciation goes to my supervisor, Prof. Q.J. Zhang for his constant support, profound expertise, and technical discussions during the course of my research work.This thesis would not have been possible without his professional guidance, unwavering enthusiasm, innovative insights, great encouragement, and unfailing patience.
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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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".