Securing computer hardware using 3D integrated circuit (IC) technology and split manufacturing for obfuscation
Bibliographic record
Abstract
The fabrication of digital Integrated Circuits (ICs) is increasingly outsourced. Given this trend, security is recognized as an important issue. The threat agent is an attacker at the IC foundry that has information about the circuit and inserts covert, malicious circuitry. The use of 3D IC technology has been suggested as a possible technique to counter this threat. However, to our knowledge, there is no prior work on how such technology can be used effectively. We propose a way to use 3D IC technology for security in this context. Specifically, we obfuscate the circuit by lifting wires to a trusted tier, which is fabricated separately. This is referred to as split manufacturing. For this setting, we provide a precise notion of security, that we call k-security, and a characterization of the underlying computational problems and their complexity. We further propose a concrete approach for identifying sets of wires to be lifted, and the corresponding security they provide. We conclude with a comprehensive empirical assessment with benchmark circuits that highlights the security versus cost trade-offs introduced by 3D IC based circuit obfuscation.
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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| 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".