MOSFET model assessment for submicron and nanometer bulk-driven applications
Bibliographic record
Abstract
Bulk-driven MOSFET technique meets the low-voltage and low-power requirements demanded in the modern analog circuit design. Due to submicron/nanometer technologies and critical short-channel effects, choosing a suitable MOSFET model for circuit design becomes increasingly important. However, the conventional MOSFET models normally set up for the typical gate-driven applications may not perform correctly and accurately for the bulk-driven applications in the advanced technologies. In this paper, three most widely used MOSFET models, including BSIM, EKV, and PSP, have been extracted for the modern technologies and used in the simulation of bulk-driven applications. Measurement data of fabricated devices are compared with simulation results from distinct models. Several critical MOSFET parameters have been chosen to compare and analyze MOSFET characteristics. The experimental results demonstrate the advantages of the bulk-driven technique compared with the gate-driven scheme. Finally, the performance of distinct MOSFET models is summarized in order to provide analog circuit designers with practical directives.
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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 | 0.000 | 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".