Bibliographic record
Abstract
With the breathtaking advance of technology, the modern analog/mixed-signal design needs to consider the requirements of low voltage/power and the effects of the MOSFET channel length shrinking. Although a few different schemes have been proposed, the bulk-driven technique, which uses bulk terminal (the fourth terminal of a MOSFET) for signal input, is a promising solution to the low-voltage and low-power applications. However, the conventional MOSFET models are normally set up for the typical gate-driven applications (i.e., using gate terminal for signal input). Besides, due to shrinking MOSFET channels, those MOSFET models may not perform correctly and accurately for the bulk-driven applications, especially in the moderate inversion region. In this paper, we evaluate two MOSFET models including BSIM3V3 and EKV for the bulk-driven applications in a sub-micron CMOS technology. BSIM3V3 is a widely used model in the semiconductor industry, while the EKV model is suitable for the small-channel-length simulation. We focus on several critical MOSFET parameters for bulk-driven application and conduct thorough experiments using the two aforementioned models. The simulation results are analyzed to demonstrate the advantages of the bulk-driven technique compared to the gate-driven scheme in the low-voltage/low-power applications. Finally the performance of the two MOSFET models in the bulk-driven applications is summarized.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| 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".