A SYSTEMATIC COMPUTER-AIDED APPROACH TO LOW-NOISE AMPLIFIER DESIGN
Bibliographic record
Abstract
Low-noise amplifiers (LNAs) are critical to a wide variety of electronic circuits. In the design phase preceding fabrication, an LNA needs to be designed for a given set of specifications (e.g., gain, noise-figure, power consumption, etc.), which tend to be application-dependent. Typically, LNA design using commercial computer-aided design (CAD) tools can be human-intensive and requires a certain degree of expertise. This paper presents a systematic multi-phase CAD approach for the design of LNAs. In the first phase, a quick pre-analysis of the given LNA specifications is carried out leading to the selection of an appropriate LNA topology. In the second phase, an initial design of the LNA is generated employing an appropriate design procedure. Finally, the initial design is adjusted/fine-tuned so as to meet/exceed the given specifications, where necessary. The advantages of the proposed approach are shown through several practical LNA design examples in 0.18 μm CMOS technology.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".