A 1-V Process-Insensitive Current-Scalable Two-Stage Opamp With Enhanced DC Gain and Settling Behavior in 65-nm Digital CMOS
Bibliographic record
Abstract
A pseudo-cascode compensation technique is proposed to enable a process-insensitive and current-scalable design of the classical two-stage opamp at low supply voltages, without requiring any additional power dissipation. Furthermore, a bulk-biasing technique is proposed to enhance the dc gain of the two-stage opamp, without affecting its output-voltage swing and without requiring any additional power dissipation. To compare the performance advantages of the proposed pseudo-cascode compensation technique versus classical Miller compensation in a two-stage opamp with/without applying the proposed bulk-biasing technique, four opamps were fabricated on the same die in a 1-V 65-nm CMOS process. The corresponding transistors in all four opamps had equal sizes. Furthermore, all four opamps had equal total compensation capacitance and the same total power dissipation. Accordingly, compared to using Miller compensation, by applying the proposed pseudo-cascode-compensation and bulk-biasing techniques in a two-stage opamp, the opamp's dc gain is increased by a factor of 4 (12 dB), its unit-gain frequency is increased by 40%, and its phase margin is maintained over a factor of 100 scaling in its bias current. Furthermore, the overshoot in its large-signal step response is eliminated and the rise/fall settling times are improved by 33%. The trade-off is a minimal decrease in the opamp's phase margin. Importantly, this is all achieved without affecting the opamp's output-voltage swing and without requiring any additional power dissipation.
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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) | 0.001 | 0.000 |
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".