NBTI and Process Variations Compensation Circuits Using Adaptive Body Bias
Bibliographic record
Abstract
Reliability and variability have become big design challenges facing submicrometer high-speed applications and microprocessors designers. A low area overhead adaptive body bias (ABB) circuit is proposed in this paper to compensate for negative-bias temperature instability (NBTI) aging and process variations to improve the system reliability and yield. The proposed ABB circuit consists of a threshold voltage-sensing circuit and an on-chip analog controller. In this paper, post-layout simulation results, referring to an industrial hardware-calibrated STMicroelectronics 65-nm CMOS technology transistor model, are presented. The transistor model contains process variations and NBTI aging model cards, which are declared by STMicroelectronics to be Silicon verified. Cadence RelXpert, Virtuoso Spectre, and Virtuoso UltraSim tools are used to estimate the NBTI aging and process variations impacts on a circuit block case study, extracted from a real microprocessor critical path. These results show that the proposed ABB compensates effectively for NBTI aging and process variations. For example, the proposed ABB improves the timing yield from 74.4% to 99.7% at zero aging time and from 36.6% to 97.1% at 10 years aging time. In addition, the proposed ABB increases the total yield from 67% to 99.5% at zero aging time and from 35.9% to 97.1% at 10 years aging time.
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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 | 0.000 |
| 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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 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".