Truly Random Number Generator Based on a Ring Oscillator Utilizing Last Passage Time
Bibliographic record
Abstract
This brief covers the design and fabrication of a ring oscillator-based truly random number generator (TRNG), which was fabricated in 0.13-μm CMOS technology. The randomness originates from the phase noise in a ring oscillator. Timing jitter resulting from crossing the threshold multiple times, i.e., last passage time (LPT), is exploited. Previously, the jitter model was developed and applied to the core delay cell of the slow VCO, part of the ring oscillator, where a slow slew rate phase was introduced to greatly increase phase noise. In this brief, the successful design of the entire TRNG was performed. This includes designing the circuit to avoid introducing correlation in the TRNG. Toward this end, novel timing circuitry is designed to properly control both the beginning and termination of this slow slew rate phase by tapping into the previous stage's output. 1/f noise also has to be minimized. Furthermore, the entire TRNG is now designed/implemented and fabricated, and experimental results are shown. The fabricated ring oscillator was shown to possess a timing jitter of 1.5 ns. Simulation under PVT variations of the entire cell shows that jitter variations are within 30%, showing that the designed control circuit was able to perform under such PVT variations. Entropy simulation with power supply variations applied to the TRNG was also run to assess its effectiveness as the biasing condition is changing. The randomness of the entire TRNG was assessed by applying the National Institute of Standards and Technology (NIST) tests. On those tests recommended by NIST to have longer bit streams, additional test measurements were performed on bit streams with increased length. Entropy tests for 20 k, 200 k, and 400 k measured bits were performed, resulting in entropy values all close to 1.
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 | 0.000 | 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 | 0.000 |
| Open science | 0.001 | 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".