MOS current mode circuits: analysis, design, and variability
Bibliographic record
Abstract
The interest in MOS current-mode logic (MCML) is increasing because of its ability to dissipate less power than conventional CMOS circuits at high frequencies, while providing an analog friendly environment. Moreover, automated design methodologies are gaining attention by circuit designers to provide shorter design cycles and faster time to market. This paper provides designers with an insight to the different tradeoffs involved in the design of MCML circuits to efficiently and systematically design MCML circuits. A comprehensive analytical formulation for the design parameters of MCML circuits using the BSIM3v3 model is introduced. In addition, a closed-form expression for the noise margin of two-level MCML circuits is derived. In order to verify the validity of the analytical formulations, an automated design methodology for MCML circuits is proposed to overcome the complexities of the design process. The effectiveness of the design methodology and the accuracy of the analytical formulations are tested by designing several MCML benchmarks built in a 0.18-/spl mu/m CMOS technology. The error in the required performance in the designed circuits is within 11% when compared to HSPICE simulations. A worst case parameter variations modeling is presented to investigate the impact of variations on MCML circuits as well as designing MCML circuits for variability. Finally, the impact of variations on MCML circuits is investigated with technology scaling and different circuit architectures.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.001 | 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".