On-Chip Process Variations Compensation Using an Analog Adaptive Body Bias (A-ABB)
Bibliographic record
Abstract
An analog adaptive body bias (A-ABB) circuit is proposed in this paper. The A-ABB is used to compensate for die-to-die (D2D) and within-die (WID) parameter variations and accordingly, improves the circuit yield regarding the speed, the dynamic power, and the leakage power. The A-ABB consists of threshold voltage estimation circuits and analog control of the body bias performed by on-chip amplifier circuits. Circuit level simulation results of a circuit block case study, extracted from a real microprocessor critical path, referring to an industrial hardware-calibrated 65-nm CMOS technology transistor model, are demonstrated. This study shows that the proposed A-ABB reduces the standard deviations of the frequency, the dynamic power and the leakage power by factors of 6.6 X, 8.8 X, and 3.3 X, respectively, when both D2D and WID variations are considered. In addition, in this presented case study, initial total yields of 16.8% and 5.2% are improved to 99.9% and 84.1%, respectively. The advantage of the proposed A-ABB is its lower area overhead allowing it to be used at lower granularity level than that of the previously published ABB circuits.
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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 | 0.001 | 0.001 |
| Open science | 0.001 | 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".