Hardware Trojan Prevention Through Limiting Access to the Active Region
Bibliographic record
Abstract
Hardware Trojan (HT) has become a serious threat to the semiconductor industry. Many solutions have been developed to detect Trojans and address hardware security concerns. However, due to the wide range of possible Trojans, specific solutions for certain Trojans have been presented and a comprehensive solution is yet to be developed. Trojan prevention methods have also been explored to ensure hardware security. This paper presents a new technique for HT prevention in which attackers are deprived of the resources to route their Trojans and connect them to the active region of the chip. In the proposed solution, the empty space of the die is filled with polysilicon to block unauthorized die connection by attackers. This will deprive attackers of the routing resources to layout their Trojan circuitry. A readout circuit is used to determine that the polysilicon layer is intact, and it has not been tampered with. Simulation results indicate that with proper design, even a reduction of around 14.85 $\mu m^{2}$ polysilicon can be detected. This technique can ensure 100% utilization of the empty spaces in the polysilicon layer.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".