Design of the Input Matching Network of RF CMOS LNAs for Low-Power Operation
Bibliographic record
Abstract
Optimum design of input matching network of CMOS low-noise amplifiers (LNAs) for low-power applications is discussed in this paper. This is done through an investigation of the effect of four different matching methodologies on the gain of radio frequency CMOS LNAs by means of compact analytical expressions. It is demonstrated that methods that convert the MOSFET's input impedance to 50 Omega for power matching are more suitable for low-power applications than methods that create a real 50-Omega resistance at the input of the LNA, such as source inductive degeneration. As it is analytically shown, this is because the former methods enhance the gain of the LNA by a factor that is inversely proportional to MOSFET's input resistance. The impact of each matching methodology on the noise figure (NF) of the LNA is also discussed in detail and design guidelines for optimum gain-NF performance are developed using analytical models of MOSFET's noise parameters. It is demonstrated that all four methods could achieve very good NF values, provided that the size of active and passive components are chosen carefully based on the given guidelines. Measured results of two monolithic 5.7-GHz LNAs, designed and fabricated in a 0.18-mum CMOS technology, are also presented. The input matching networks of these LNAs are optimized for low-power operation based on the theory presented in this paper. It is experimentally shown that this optimization results in approximately 60% reduction in the dc power consumption and up to 300% improvement in the overall performance of the LNA when compared with some of the most recently published LNAs
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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 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".