A Low-power High-gain Inverter Stacking Amplifier with Rail-to-Rail Output
Bibliographic record
Abstract
In this article, a rail-to-rail low-power amplifier is presented based on stacking inverter-based amplifiers. The output voltages of each inverter-based amplifier are converted to a current and then mirrored to the output so that a rail-to-rail output is achieved. Besides, extensive simulations have been carried out to show the effect of drain-source voltage on the intrinsic gain of a transistor. Based on these simulations, a minimum supply voltage is chosen to achieve high open-loop gain and low closed-loop gain error. All the simulations are carried out in a commercially available 0.18 μm CMOS technology. The proposed amplifier achieves 88 dB open-loop gain. It is exploited in a capacitively-coupled amplifier structure. The closed-loop gain is 40 dB in the bandwidth of 0.1 Hz to 10 kHz when the power consumption is 0.54 μW at a 1.2 V supply voltage. The total input-referred noise is 4.7 μVrmsin the whole bandwidth. The proposed neural amplifier achieved 0.02 SEF in the bandwidth from 200 Hz to 10 kHz. The proposed amplifier achieved a rail-to-rail output swing while the SEF is among the best reported SEF in the literature. Besides, to show the robustness of the proposed structure in the presence of process and mismatch variation, 500 Monte Carlo simulations are carried out. The PSRR and CMRR mean values are 89 dB and 68 dB, respectively. Finally, the proposed neural amplifier area consumption is 0.03 mm2without pads.
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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 | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".