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Journal of the Optical Society of America B

Multi-noise fusion TRNG using LED sources with genetic optimization for cryptographic randomness enhancement

Tawfik Ismail

Published Dec 1, 2025• 0 citations
Publisher

Abstract

This paper presents a novel, to our knowledge, true random number generator (TRNG) architecture based on multiple light-emitting diodes (LEDs), where each LED serves as an independent source of physical randomness. The system combines four distinct noise types: shot noise, thermal noise, flicker noise, and chaotic signals generated via a logistic map. The logistic map is selected for its computational efficiency and robust chaotic dynamics, ensuring high-entropy signal generation. A genetic algorithm optimizes the weights of these noise sources to maximize entropy and minimize autocorrelation and bias. The composite analog signal is normalized, passed through μ -law companding to enhance entropy distribution, and then digitized using median-based thresholding. A delayed XOR operation further improves randomness by reducing the residual autocorrelation. Statistical validation using NIST SP 800-22 and Dieharder test suites confirms near-ideal entropy (≈0.998), minimal bias (<0.001), and negligible autocorrelation (<9 −4 ). Normality analysis via Kolmogorov–Smirnov and Quantile–Quantile tests confirms that the noise sources are statistically consistent with normality, supporting balanced bit generation. A cryptographic security analysis demonstrates resilience against side-channel attacks, fault injection, and bias exploitation, attributed to the diversity of noise sources, robust post-processing, and optimized design parameters. These results demonstrate that the proposed TRNG is robust, high quality, and suitable for cryptographic applications.

DOI
10.1364/josab.574067

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Quantum random number generators (QRNGs) harness the inherent unpredictability of quantum processes to generate true randomness, a critical resource for cryptography, secure communications, and emerging quantum technologies. In this paper, I will present a compact and scalable QRNG architecture based on micro-light-emitting diodes (micro-LEDs), which serve as entropy sources by exploiting the intensity fluctuations of spontaneous emission. Our system, using a micro-LED, achieves a bandwidth-limited generation rate of 9.375 Gbit/s, while meeting the statistical randomness requirements specified by the NIST test suite. Our work lays the foundation for parallel QRNG integration on chip, enabling ultra-high-rate quantum random number generation in miniaturized, energy-efficient, and practical platforms. The implications of this advancement span from secure data and hardward to next-generation cryptographic systems.
Random numbers are important in diverse areas of science and technology. The requirements of randomness, however, are more stringent in cryptographic applications, where so-called true randomness is required. In particular, statistical randomness is not enough, and a degree of unpredictability stemmed in physical phenomenon is used to build a true random number generator (TRNG). A critical part in TRNG design is the entropy harvester, which extracts entropy from multiple entropy sources. Unfortunately, none of such sources provide true randomness in pure form, and the desired signal is heavily contaminated due to electrical coupling, electromagnetic interference, and other pseudo-random signals present in physical environment. Process variation and ageing affect the performance of noise sources. In this paper we propose an entropy extraction and estimation mechanism, which operates directly on raw randomness exhibited by analogue noise sources. The main strength of our design is its dynamic nature, i.e., it adapts as per the current state of source, and provides a robust mechanism to detect entropy failure. Our design can easily be implemented on commodity hardware in a cost effective manner.
Random Number Generators (RNGs) are crucial for applications ranging from cryptography to simulations. Depending on the source of randomness, RNGs are classified into Pseudo-Random Number Generators (PRNGs), True Random Number Generators (TRNGs), and Quantum Random Number Generators (QRNGs). This work presents the end-to-end development of a high-speed, high-efficiency, phase-noise-based QRNG system that taps into the quantum phase noise of a single-frequency laser, with randomness originating from spontaneous emission. Using a self-heterodyne measurement with a semiconductor laser (linewidth ≈\approx≈ 5.23 GHzGHzGHz) operated near threshold and a $\sim$48 cmcmcm fiber delay line, a raw data generation rate of 2.0 GbpsGbpsGbps is achieved. To ensure uniform randomness in the QRNG output, robust extraction techniques developed in-house, such as the Toeplitz Strong Extractor (TSE), are used. Randomness validation using the NIST and Diehard test suites confirms that all statistical tests pass at standard confidence levels. The developed system achieves a post-processed generation rate of 1.0 GbpsGbpsGbps in operation and attains a Technology Readiness Level (TRL) of 7, approaching TRL 8, making it suitable for real-time secure applications such as cryptographic key generation and stochastic modeling.
True random number generation is a critical capability for fault-tolerant quantum computing at millikelvin temperatures. Yet existing Josephson-junction-based TRNGs all rest on a widely accepted but untested assumption: that reliable entropy extraction requires precisely controlled device parameters. Here we show that this assumption does not always hold. We demonstrate a counterintuitive finding: a single current-biased Josephson junction, regardless of its parameter quality, can serve as a cryptographic-grade true random number generator. To establish the universality of this conclusion, we deliberately selected the most extremely deviated devices from fabrication, with critical currents three orders of magnitude away from theoretical predictions and IcRI_cRIc​R products an order of magnitude above conventional values, as the ultimate stress test. Even under these extreme conditions, the raw Shannon entropy reaches 0.9981~bit (99.8\% of the theoretical maximum), with a min-entropy of 0.9271~bit. Using a square-wave pulsed-bias scheme, we tune the switching probability to P≈0.5P\approx0.5P≈0.5. After SHA-256 post-processing, the bitstreams pass all 15 NIST SP 800-22 tests under a conservative m=3m=3m=3 criterion that is more demanding than the standard recommendation, and this certification holds across the entire 100-700~mK operating window of a dilution refrigerator.
A quantum random number generator (QRNG) based on a silicon nanocrystals (Si-NCs) light emitting device (LED) coupled with a silicon single photon avalanche photodiode (Si-SPAD) is presented. A simple setup is used for the generation of random bits. The modeled approach assures a negligible bias on datasets of 100 Mbits length. The raw data pass all the statistical tests in the National Institute of Standards and Technology (NIST) suite without any post-processing operations. The bit-rate of 0.6 Mbps is achieved. The information-theoretically provable randomness extractor of Toeplitz-hashing function is applied to longer datasets (1 Gbits) to extract the randomness, to minimize the bias, and consequently pass all the NIST tests. Stabilizing the temperature, resetting the applied current to the LED, or a feedback system can also be used as parameter control solutions to generate good quality, long datasets of random numbers suitable for cryptographic applications.
The paper proposes a hybrid entropy-controlled random number generator for information security systems that combines physical entropy sources, cryptographic conditioning, and adaptive feedback control. A comparative analysis of traditional pseudo-random and quantum generators was conducted, and experimental testing was performed in accordance with NIST SP 800-90B, NIST SP 800-90C, and ISO/IEC 18031:2025. According to statistical analysis of a large volume of bit sequences, minimum entropy close to unity was observed, with only a slight deviation, confirming the high randomness. The throughput of the hardware implementation is ~ 600 Mbit/s (over 500 Mbit/s) with an energy consumption of E ≈ 0.29 mJ/Mbit. Compared to classical generators on an uncontrolled cypher counter or hardware generators on ring oscillators without pre-mixing, a reduction in correlation and mean shift of more than a third has been achieved. The proposed architecture demonstrates resistance to source manipulation, fault injection, and side-channel attacks, confirming the effectiveness of the entropy approach for modern cryptographic systems, Internet of Things platforms, and security modules.