On-Chip Nanoscale Capacitor Decoupling Architectures for Hardware Security
Bibliographic record
Abstract
This paper presents new power analysis attack (PAA) countermeasures for nanoscale cryptographic devices. Specifically, three circuit level architectures called partial decoupling architecture, full decoupling architecture, and randomized switch box architecture are developed and analyzed. The architectures' primary feature is the use of on-chip nMOS gate capacitors as intermediate power storage elements to decouple the power supply from internal low-power modules processing sensitive data. The proposed countermeasures are algorithm independent and allow different tradeoffs between security protection and the incurred overheads. Test benches of the proposed architectures were simulated in 65-nm TSMC CMOS technology. A correlation PAA was performed for each test bench targeting a custom implementation of the advanced encryption standard subbytes operation. All architectures were found to resist the correlation PAA at the power supply, with the more complex architectures also offering protection against invasive attacks. The success value indicator was used to analyze the effectiveness of the countermeasures. It was found that all architectures provided a negative value at the power supply, showing protection against PAAs. We demonstrate that the use of nMOS gate capacitors can help to increase security and present a feasibility analysis focused on the needed decoupling capacitances.
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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 | 0.001 |
| 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".