Efficient Evaluation of the Time-Dependent Threshold Voltage Distribution Due to NBTI Stress Using Transistor Arrays
Bibliographic record
Abstract
For the design of reliable ICs the application of accurate transistor aging models is indispensable. However, especially for modern deeply-scaled technologies modeling NBTI induced aging poses significant challenges as transistors can not be characterized individually but need to be described as a statistical ensemble. We make use of a dedicated array structure enabling the collection of a large data-set at reasonable experimental time. Note that by using an extensive data-set the statistical confidence can be maximized. We present a modeling approach based on physical considerations to describe the aging of SRAM-sized transistors under consideration of statistical effects. Our approach applies a modified defect-centric model under consideration of RTN to describe the time-dependent Vth distribution. Furthermore, we introduce a simple method to extract parameters describing the distribution. Additionally, we show that our approach provides great flexibility regarding stress conditions as well as scalability to different transistor dimensions, which makes it ideal for applications such as circuit simulation.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".