On-Chip CMOS Self-Decoupling Battery Cell System for Security Protection
Bibliographic record
Abstract
This article presents an effective on-chip power analysis attack countermeasure based on a new CMOS self-decoupling battery cell system that uses a self-decoupling circuit. The self-decoupling circuit dynamically controls an on-chip virtual power supply point,$V_{\mathrm {ddv}}$, that can be used to power security-sensitive modules. The circuit automatically decouples an on-chip CMOS battery cell from powering a sensitive module when its voltage level reaches a designed minimum threshold level$V_{\mathrm {dd-min}}$and connects it for a very short charging cycle to the chip’s main voltage supply,$V_{\mathrm {dd}}$. The charging cycles for the experiments presented in this article are less than 10 ns and are designed to support the CMOS battery cell size and the minimum designed threshold voltage level$V_{\mathrm {dd-min}}$. Simulation results of test designs implemented in the 45-nm CMOS technology process show that the proposed countermeasure is efficient when used with battery cell sizes that can power the protected cryptographic module for more than ten data operation cycles before recharging. In addition, using the on-chip self-decoupling battery cell system allows for power consumption savings within the protected module of up to 43 % due to the dynamic voltage scaling generated at the virtual power supply point.
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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) | 0.003 | 0.001 |
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".