An Improved Active Decoupling Capacitor for “Hot-Spot” Supply Noise Reduction in ASIC Designs
Bibliographic record
Abstract
On-chip decoupling capacitors (decaps) are widely used to reduce power supply noise by placing them at the appropriate locations on the chip between blocks. While passive decaps can provide a certain degree of protection against IR drop, if a problem is found after the physical design is completed, it is difficult to implement a quick fix to the problem. In this paper, we investigate the use of an active decap as a drop-in replacement for passive decaps to provide noise reduction for these so-called ldquohot-spotrdquo IR drop problems found late in the design process. A modified active decap design is proposed for ASIC applications operating up to 1 GHz. Our improvement uses latch-based comparators as the sensing circuit, which provides a better power/delay tradeoff than previous designs and incorporates hysteresis to minimize unnecessary switching. It is implemented in a 1 V-core 90 nm CMOS process with a total area of 0.085 mm2and static power of 2.8 mW. Measurements from a number of test chips show that using an active decap can provide between 10%-20% noise reduction in the 200 MHz-1 GHz frequency range over its passive counterpart. Sizing and placement analyses are also carried out using circuit simulation. The active decap is most effective when placed in close proximity to the hot-spot, as compared to the passive decap which is less sensitive to the exact location. Overall, if sized and placed properly, active decaps can provide an additional 20% reduction in supply noise over passive decaps.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".