Scalable Multi-Stage CMOS OTAs With a Wide C<sub>L</sub>-Drivability Range Using Low-Frequency Zeros
Bibliographic record
Abstract
This work introduces a multi-stage CMOS OTA design technique that allows cascading identical gain stages (for arbitrarily scalable high DC gain) while driving an ultra-wide range of capacitive loads ($\text{C}_{\text {L}}\text{s}$). At the heart of the proposed design is a new frequency compensation technique (FCT) that relies on low-frequency left-half-plane zeros to allow the proposed OTA to operate for a desired closed-loop behavior. In this work, classical gain-stages (i.e., differential pair and common source transistors) are used to design fully-differential 2-, 3-, 4- and 5-stage CMOS OTAs. The proposed 2-to-4-stage designs have been fabricated in TSMC 65 nm CMOS process and the measurement results show that the 2-stage OTA is achieving a DC gain of 50 dB with a$\text{C}_{\text {L}}$-drivability ratio (i.e.,$\text{C}_{\text {L,max}}/\text{C}_{\text {L,min}}$) of$10,000\times $, the 3-stage OTA is achieving a DC gain of 70 dB with a$\text{C}_{\text {L}}$-drivability of$1,000,000\times $, and the 4-stage OTA is achieving a DC gain of 90 dB with a$\text{C}_{\text {L}}$-drivability of$1,000,000\times $. This is a 10-to-1000-time improvement in the state-of-the-art, as the highest$\text{C}_{\text {L}}$-drivability reported to date is$1000\times $. Accordingly, the proposed OTAs can cover a wider range of applications than any other reported works.
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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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".