Space Mapping Technique Using Decomposed Mappings for GaN HEMT Modeling
Bibliographic record
Abstract
A novel space mapping (SM) modeling approach for gallium nitride (GaN) high-electron-mobility transistors (HEMTs) with trapping effects is presented in this article, advancing the SM technique for nonlinear device modeling. Existing SM modeling approach uses an external mapping to map an existing device model onto device data. When different branches inside the existing device model need to address very different behaviors, such as trapping effects and frequency dispersion in GaN HEMTs, it is hard for one external mapping to simultaneously map different behaviors. The proposed SM technique develops separate mappings for different branches, such that different behaviors can be mapped separately. Each mapping module is formulated to map a specific behavior in the overall model. Each mapping module is developed through machine learning to systematically overcome the gap between each internal branch and each set of target data, accelerating the process of model development. The proposed SM technique is a fast and systematic modeling approach, compared with the existing empirical function/equivalent circuit approach. Compared with the pure neural network modeling approach, the proposed SM technique employs less training data. Measurement data of a 2 × 350 μm GaN HEMT device are employed for model training and verification. Good agreement can be achieved between the developed large-signal model and the measurement data, including dc, pulsed I-V (PIV) at seven quiescent biases, S-parameters, and power characteristics. Reasonably close predictions of load- pull figures of merit are achieved by the developed model. The model development illustrated in the example shows that the proposed SM technique is a fast modeling approach to develop an accurate large-signal model for GaN HEMTs.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".