ViTaL: Verifying Trojan-Free Physical Layouts through Hardware Reverse Engineering
Bibliographic record
Abstract
The semiconductor industry is heavily relying on outsourcing of design, fabrication, and testing to third parties. The threat of possibly malicious actors in this ramified supply-chain poses a risk for the integrity of integrated circuits (ICs) and hardware Trojans (HTs) are a heavily discussed topic in academia and the industry. A variety of pre- and post-silicon HT prevention and detection techniques has been suggested in prior works. Hardware reverse engineering has the potential to detect potential modification in physical layouts. Yet, there is no model to qualitatively and quantitatively rate the complex and expensive reverse engineering (RE) process addressing its inherent process aberrations and consequently provide a tool for layout verification. The ViTaL framework introduces a statistical validation technique, based on physical layout verification through RE and considers all potential sources of errors. The golden-model based framework is technology-agnostic, scaleable, and user input is optional. For the first time, results of fine pitch metallization layers of a CMOS 40nm process node IC are presented quantitatively and the limitations and possibilities are discussed.
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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".