Artificial Neural Networks for Power Match Modeling and Verification With a Novel <i>D</i>-Band Vector Load—Pull Bench
Bibliographic record
Abstract
We present an artificial neural network (ANN) model that predicts high-frequency, large-signal hetero-junction bipolar transistor (HBT) performance as a function of the load reflection coefficient and input power trained on a set of load—pull (LP) data that include delivered source power, output power, power-added efficiency (PAE), gain compression, input voltage, and output current. The ANN models are trained with data measured using a novel$D$-band vector LP test bench at 130, 135, and 140 GHz. We analyze common-base (CB) and common-emitter (CE) indium phosphide (InP) HBTs and show that the CE HBT provides the highest impedance margin that maximizes PAE across a 10% bandwidth centered at 140 GHz. We fabricate CE HBT power cells and demonstrate state-of-the-art measured performance (36.2% PAE at 135 GHz at 4-dB compressed gain and 13.6 dBm). Using the ANN model trained at 130 GHz, transfer learning is accomplished for 135 GHz, 140 GHz, and prematched device datasets. The size of the ANN training sets is varied from 25% to 1% of the data collected, showing that our models are capable of predicting performance with an average rms error below 1.5% across sets. To our knowledge, this is the first demonstration of transfer learning within ANNs for transistor nonlinear modeling.
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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 | 0.000 |
| 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".