A Compact Low-Voltage True Random Number Generator Based on Inkjet Printing Technology
Bibliographic record
Abstract
Printed electronics (PE) is a fast-growing field with promising applications in wearables, smart sensors, and smart cards, since it provides mechanical flexibility, and low-cost, on-demand, and customizable fabrication. To secure the operation of these applications, true random number generators (TRNGs) are required to generate unpredictable bits for cryptographic functions and padding. However, since the additive fabrication process of the PE circuits results in high intrinsic variations due to the random dispersion of the printed inks on the substrate, constructing a printed TRNG is challenging. In this article, we exploit the additive customizable fabrication feature of inkjet printing to design a TRNG based on electrolyte-gated field-effect transistors (EGFETs). We also propose a printed resistor tuning flow for the TRNG circuit to mitigate the overall process variation of the TRNG so that the generated bits are mostly based on the random noise in the circuit, providing a true random behavior. The simulation results show that the overall process variation of the TRNGs is mitigated by 110 times, and the generated bitstream of the tuned TRNGs passes the National Institute of Standards and Technology - Statistical Test Suite. For the proof of concept, the proposed TRNG circuit was fabricated and tuned. The characterization results of the tuned TRNGs prove that the TRNGs generate random bitstreams at the supply voltage of down to 0.5 V. Hence, the proposed TRNG design is suitable to secure low-power applications in this domain.
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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 | 0.001 |
| 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 | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".