A new total static leakage estimation model for UDSM-based transistor stacks
Bibliographic record
Abstract
This paper introduces a new input pattern dependent model for total static leakage estimation in ultra deep submicron processes. The model integrates gate tunnelling leakage, gate induced drain leakage (GIDL) and subthreshold leakage into a single leakage estimation framework. Subthreshold estimation is facilitated through the analytical estimation of nodal voltages between OFF transistors, while gate tunnelling leakage and GIDL are calculated based on simplified versions of their respective BSIM4 equations. The framework deals with all input patterns and accommodates scenarios where the various leakage currents interact. Similar approaches in the literature are either based on a look up table approach, and do not accommodate transistor stacks with varying widths, or are highly experimental and require a detailed knowledge of the transistor device physics. Several approaches also exist for modeling either subthreshold leakage or gate tunnelling leakage separately. Even those approaches use a lookup table approach, fix all widths in a transistor stack and/or limit the stack size to 2-3 transistors. The model proposed in this paper is tractable and almost completely analytical. It is capable of accommodating stacks with up to 4 transistors with varying transistor widths. A stack estimator function based on this model was coded in MatLab for the 65nm, 45nm and 32nm PTM process technologies. Compared with SPICE simulations the model exhibited an average error of 1.29%, 2.79%, 7.57% and 11.42% for stack sizes of 1, 2, 3 and 4 respectively across all three technologies. The model also exhibits significant runtime savings when compared with SPICE.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".