Design and fabrication methodology for industrial broadband high power amplifiers
Bibliographic record
Abstract
This study presents a design and fabrication methodology for industrial broadband high power amplifiers (HPAs). The proposed method considers design constraints for broadband HPAs and parasitic effects arising from the fabrication process. This method is based on power combining networks (PCNs) whose characteristics such as power handling constraints, insertion loss, and bandwidth depend on the employed structure. Another important set of challenges is the technical faults and parasitics that occur during the fabrication process such as inequalities among different paths of PCNs, active and passive inherent mismatches, and many uncertainties in the fabrication and integration of a complicated large system. As such, the authors introduce a model for estimating the effects of path inequalities using a Gaussian function. For validation of the proposed methodology, a 2–6 GHz, 50 dBm output power broadband HPA system, which satisfies IEC‐61000‐4‐3 RF radiant immunity measurement, was designed and fabricated. Furthermore, the measurement results exhibit the power‐added efficiency of 10–28% and 48–51 dBm output power in the frequency bandwidth 2–6 GHz, which verify the capability of the proposed method for reliable estimation of the fabrication parasitics.
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 | 0.001 |
| 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 | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".