A small tamper-resistant anti-recycling IC sensor with a reused I/O interface and DC signalling
Bibliographic record
Abstract
Counterfeit electronic components are known to enter supply chains through recycling, with these already-aged components creating serious reliability risks, particularly for critical infrastructure systems. A number of recycled integrated circuit (IC) risk mitigation approaches have been proposed, but these generally lack pragmatic feasibility. This work proposes a novel real-world deployable on-chip sensor that: 1) is tamper-resistant by exploiting persistent changes caused by hot carrier injection (HCI); 2) generates a DC signal measurable by common low-cost test equipment; and 3) reuses an existing I/O interface, including existing pins; while 4) requiring a very small footprint. Combining this sensor with a random sample-based testing strategy allows for low-cost and time efficient detection of fraudulently recycled batches of ICs. Through simulation-based validation using process-accurate models of a 65 nm technology we show that employing a random sample size as small as 130 is sufficient for identifying such batches with a statistical significance level of 0.01.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".