A highly linear low-passGm–Cfilter with a self-biasing transconductor for a digital TV tuner
Bibliographic record
Abstract
To improve the linearity of the transconductor in digital TV tuner application, a new technique of multiple gated transistors in self-biasing basis is presented. The proposed design decreases the bill-of-material (BOM) and offers less complexity of the structure. In addition, the proposed transconductor with utilizing by third-order Chebyshev introduces low-pass filter with low power consumption and the cutoff frequency of 50-200 MHz. The hybrid tracking low-pass filter is designed to overcome the issue of local oscillator harmonic-mixing for Advanced Television System Committee terrestrial digital TV tuner integrated circuit. The proposed operational transconductor amplifier (OTA) is designed and implemented in 90 nm CMOS technology. The simulation result with the two-tone test at 100 MHz center frequency proves the proposed OTA has 5 dBm Input-referred Third-Order Intercept Point (IIP3) in compare witha single-gate OTA in the third-order Chebyshev filter. The proposed OTA achieves maximum noise figure (NF) of 13 dB and maximum IIP3 of approximately 21.7 dBm at 100 MHz, whereas consuming 18 mA with 1.2 V supply voltage and it shows great improvement.
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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) | 0.000 | 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 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".