Molecular-Scale Hardware Encryption Using Tunable Self-Assembled Nanoelectronic Networks
Bibliographic record
Abstract
Nanomaterials are promising alternatives for creating hardware security primitives that are considered more robust and less susceptible to physical attacks compared to standard CMOS-based approaches. Here, nanoscale electronic circuits composed of tunable ratios of molecules and colloidal nanoparticles formed via self-assembly on silicon wafers are investigated for information and hardware security by utilizing device-level physical variations induced during fabrication. Two-terminal electronic transport measurements show variations in current through different parts of the nanoscale network, which are used to define electronic physically unclonable functions. By comparing different current paths, arrays of binary bits are generated that can be used as encryption keys. Evaluation of the keys using Hamming inter-distance values indicates that performance is improved by varying the ratio of molecules to nanoparticles in the network, which demonstrates self-assembly as a potential path toward implementing molecular-scale hardware security primitives. These nanoelectronic networks thus combine facile fabrication with a large variety of possible network building blocks, enabling their utilization for hardware security with additional degrees of freedom that is difficult to achieve using conventional systems.
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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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".