A Systematic Approach Towards the Implementation of a Low-Noise Amplifier in Sub-Micron CMOS Technology
Bibliographic record
Abstract
Low-noise amplifiers (LNAs) are critical components for a wide variety of electronic circuits ranging from aerospace to Bluetooth applications. Typically, in the design phase preceding fabrication, an LNA needs to be designed/tuned for a given set of specifications (e.g. noise figure, power consumption, voltage gain etc.), which tend to be application-dependent. Traditional design based on simulation tools and trial-and-error is human-intensive and requires a high-degree of expertise from the circuit designer. As such, computer aided design (CAD) tools for LNA design are in great demand. This paper presents a new and systematic CAD approach for the design and tuning of LNAs in sub-micron CMOS technology. The approach has multiple phases. In the first phase, a detailed pre-analysis of the design specifications is carried out leading to knowledge-based ranking and selection of an appropriate LNA topology. In the design phase, concepts of single-stage design are exploited and the actual circuit is designed in a modular fashion leading to the initial design. Finally, as in any CAD approach, this design is put through a tuning phase so as to meet the given specifications. LNA design examples presented in the paper illustrate the proposed approach. Resulting circuits are shown to exceed the given specifications confirming its usefulness to designers
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".