Design for Slew-Rate in Multi-Stage CMOS OTAs
Bibliographic record
Abstract
Cascading gain stages in CMOS Operational Transconductance Amplifiers (OTAs) has become a necessity in applications with high gain requirements, where the contribution of each stage to the overall gain is well-known and carefully designed. Many of these applications also impose requirements on speed, including a minimum Slew-Rate (SR) to ensure signal fidelity, however the impact of individual gain stages on the overallSRin multi-stage OTAs has been difficult to characterize–let alone carefully design. The difficulty arises due to the complexity of the compensation networks involved in these OTAs. This paper presents a systematic design approach for achieving a targetSRin multi-stage CMOS OTAs, enabled through the utility of a novel analytical model for estimating the lower-bound Slew-Rate in multi-stage OTAs. The model evaluates individual currents and equivalent capacitances at the output node of each stage, providing insights on the dominant node slowing down the overallSR. For generality, the model establishes theSRanalysis based on N-stage designs, and considers widely employed compensation networks. Example designs, with post-layout simulations and measurements of a 3- and a 4-stage CMOS OTA, and with post-layout simulations of a 5-stage CMOS OTA, are presented for validating the model’s utility. The results show strong agreement between theoretical, simulated, and measuredSRvalues, confirming the model’s reliability in estimating the lower-boundSR, and its utility in a systematic design-for-SRapproach in multi-stage CMOS OTAs.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".