A high linear broadband cascode LNA employing common-gate linearity enhancing technology
Bibliographic record
Abstract
This paper presents a low power, high linearity wideband cascode low noise amplifier (LNA) targeting the multi-standard wireless communication applications including LTE, GSM and Zigbee. Analyzing a cascode amplifier shows the common source (CS) stage is the main nonlinearity source for the LNA. The modified derivative superposition (MDS) technology is widely adopted to improve the linearity. In addition, when the operating frequency increases, the nonlinearity influence coming from the common gate (CG) increases and limits the linearity performance of the LNA. Based on the modified derivative superposition (MDS) technique, this work investigates the nonlinearity effects of the common gate (CG) stage on the cascode amplifier and employs both the derivative superposition (DS) and a terminal LC resonator on the CG stage. The nonlinearity coming from the CG stage is degraded in higher frequency and the high linear bandwidth is also increased. A cascode LNA was designed, analyzed, and implemented in the IBM 0.13-μm CMOS technology. The LNA achieved a third-order intercept point (IIP3) of +13.6dBm, 3dB NF and a 12dB gain in a wide frequency from 700MHz to 1.1GHz.
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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 | 0.000 |
| 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) | 0.001 | 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".