True Random Number Generator for Reliable Hardware Security Modules Based on a Neuromorphic Variation-Tolerant Spintronic Structure

True random numbers are essential for ensuring information security and supporting simulations across various industries. With the exponential growth of data driven by advancements in artificial intelligence, robust encryption for communications has become increasingly important. While software-based deterministic random number algorithms are cost-effective and easy to use, they are vulnerable to attacks by powerful supercomputers, highlighting the need for more secure alternatives. As portable electronic devices and information-gathering sensors proliferate, portable true random number generators (TRNGs) are critical for maintaining security. In this work, hybrid material-based photodetectors composed of anionic polymers and perovskites that maximize stochastic photogeneration for TRNG applications are presented. By integrating perovskite photodetectors with simple electronic circuits, compact, low-power TRNG devices have been developed that are versatile and resilient to environmental factors. These devices generate 10000 bitss-1 without resets or delays, achieving significant miniaturization. The generated 10 Mbit random number is validated through US National Institute of Standards and Technology (NIST) testing. Using a 480000-bit random sequence, perfect image encryption, ensuring protection against hacking are demonstrated. Additionally, the perovskite TRNG can operate under external light even when embedded in pork skin, realizing its potential as an implantable device for personal security and authentication.

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.

DESSB-TRNG: A novel true random number generator using data encryption standard substitution box as post-processing

The pursuit of hardware-based security solutions has highlighted the true random number generator (TRNG). Various physical phenomena, from noise generation to quantum physics complexities, have been explored for random number generation. The arc discharge light-induced TRNG (ALTRNG) is introduced, featuring wavelength-dependent photocurrent generation and arc discharge irradiation. A bipolar photo-responsive photodetector (BPPD) differentiates "1" and "0" states, producing highly random bits validated by the National Institute of Standards and Technology (NIST) 15 tests. The BPPD's response to deep-ultraviolet (DUV) and blue light enables distinct photocurrent generation under arc discharge illumination. The ALTRNG generates true random signals, yielding bit streams with unpredictability, uniform distribution, and stability. With a readout circuit achieving 2-kbps, wireless random number transmission is demonstrated, highlighting potential for secure password systems and artificial X-ray image generation.

Random numbers are at the heart of diverse fields, ranging from simulations of stochastic processes to classical and quantum cryptography. The requirement for true randomness in these applications has motivated various proposals for generating random numbers based on the inherent randomness of quantum systems. The generation of true random numbers with arbitrarily defined probability distributions is highly desirable for applications, but it is very challenging. Here we show that single-photon quantum walks can generate multi-bit random numbers with on-demand probability distributions, when the required “coin” parameters are found with the gradient descent (GD) algorithm. Our theoretical and experimental results exhibit high fidelity for various selected distributions. This GD-enhanced single-photon system provides a convenient way for building flexible and reliable quantum random number generators. Multi-bit random numbers are a necessary resource for high-dimensional quantum key distribution.

Random numbers are widely used in the cryptography and security systems. However, most of the true random number generators (TRNG) which use physical randomness are with high complexity and high power consumption. This paper proposes a new TRNG circuit using magnetic tunnel junction (MTJ). As one of the reliability issues in MTJ based circuit, the stochastic switching behavior provides a perfect physical source of randomness. The functionality of proposed design is validated by transient simulations with 28nm fully depleted silicon-on-insulator (FDSOI) technology and an accurate MTJ compact model. Simulation results show that our design can generate accurate random bitstream stably. The reliability analysis concerning process variation of MTJs and transistors proves the good variability tolerance of our TRNG design. Furthermore, our design can output stable random bitstream around 30 tuning steps.

A physical random number generator, which generates truly random number trains by using the randomness of physical phenomena, is widely used in the field of cryptographic applications. We have developed an ultra high-speed superconductive physical random number generator that can generate random numbers at a frequency of more than 10 GHz by utilizing the high-speed operation and high-sensitivity of superconductive integrated circuits. In this study, we have statistically evaluated the quality of the random number trains generated by the superconductive physical random number generator. The performances of the statistical tests were based on a test method provided by National Institute of Standards and Technology (NIST). These statistical tests comprised several fundamental tests that were performed to evaluate the random number trains for their utilization in practical cryptographic applications. We have generated 230 random number trains consisting of 20,000-bits by using the superconductive physical random number generator fabricated by the SRL 2.5 kA/cm 2 Nb standard process. The generated random number trains passed all the fundamental statistical tests. This result indicates that the superconductive random number generator can be sufficiently utilized in practical applications.

Random ferroelectric domain nucleation and growth lead to the generation of numerous unpredictable microscopic states that collectively form a natural high-entropy system. Conventional electrical methods can directly measure reversible domain switching currents, offering a viable platform for true random number generation (TRNG). However, TRNG based on random ferroelectric switching events is limited by the noise amplitudes in electrical signals. In this study, TRNG is realized via the stochastic formation of conductive domain walls in single-crystal LiNbO3 thin films bonded to SiO2/Si wafers. This approach achieves a noise amplitude and cycling endurance >500nA and >1010, respectively. The interfacial-layer-based device exhibits self-reinitialized stochastic sub-10-ns domain switching operations, enabling ultrafast generation of bit outputs and flexible device scaling. The generated random bitstreams, validated via National Institute of Standards and Technology (NIST)tests, exhibit robust resistance against machine-learning-based predictive attacks. This pioneering study establishes ferroelectric conductive domain walls as groundbreaking platforms for CMOS-compatible entropy source extraction, effectively addressing the long-standing challenges in amplifying entropy signals with operational robustness.

The generation of random numbers is crucial for practical implementations of cryptographic algorithms. In this sense, hardware security modules (HSMs) include true random number generators (TRNGs) implemented in hardware to achieve good random number generation. In the case of cryptographic algorithms implemented on FPGAs, the hardware implementation of RNGs is limited to the programmable cells in the device. Among the different proposals to obtain sources of entropy and process them to implement TRNGs, those based in ring oscillators (ROs), operating in parallel and combined with XOR gates, present good statistical properties at the cost of high area requirements. In this paper, these TRNGs are revisited, showing a method for area optimization independently of the FPGA technology used. Experimental results show that three ring oscillators requiring only three LUTs are enough to build a TRNG on Artix 7 devices from Xilinx with a throughput of 33.3 Kbps, which passes NIST tests. A throughput of 50 Kbps can be achieved with four ring oscillators, also requiring three LUTs in Artix 7 devices, while 100 Kbps can be achieved using an structure with four ring oscillators requiring seven LUTs.

Pseudo random number generators (PRNG) play a key role in various security and cryptographic applications where the performance of these applications is directly related to the quality of generated random numbers. The design of such random number generators is a challenging task. In this paper, we propose an application of Hopfield Neural Networks (HNN) as pseudo random number generator. This is done based on a unique property of HNN, i.e., its unpredictable behavior under certain conditions. We compare the main features of ideal random number generators with those of PRNG based on Hopfield Neural Networks. We use a battery of statistical tests developed by National Institute of Standards and Technology (NIST) to measure the performance, and to evaluate the quality of the proposed Hopfield random number generator.

The need for robust cryptographic measures has become increasingly important in various applications, including secure communication, artificial intelligent model training, and stochastic artificial neural networks. Among these measures, true random number generators (TRNGs) are essential for creating unpredictable cryptographic keys. In this work, we utilize BaF2 as the active layer of the memristors. The switching mechanism of these memristors originates from the randomly formed and ruptured conductive filaments, but the devices possess a random telegraph noise (RTN)-type random current spiking at a single operation voltage. This spontaneous spiking behavior features a very high on/off ratio of 103, several orders higher than traditional RTN-based TRNGs. Moreover, we confirm that the resistive switching mechanism results from Ag filament formation through time-of-flight secondary ion mass spectrometry and provide a comprehensive understanding of the Ag diffusion during spiking thorough a Monte Carlo simulation with results well-consistent with the experimental data. The randomness of the random number string generated from the BaF2 memristors is confirmed by passing all 15 National Institute of Standards and Technology (NIST) randomness tests. The high on/off ratio, simple operation, and room-temperature fabrication of the BaF2 TRNG device provide great potential for stochastic hardware implementation in various applications.

Random numbers are a fundamental resource in science and engineering, and are also widely used in many fields, such as simulation, cryptography, lotteries, fundamental science. There are algorithmically generated numbers that look like random numbers but are not truly random. These pseudo random numbers are generated from a computer, using deterministic algorithms. Because, we cannot use pseudo random generators in situations, where true randomness is necessary, we use true random number generators. In this case, we use unpredictable random events as a random source. Quantum random number generators (QRNG) output true random numbers based on inherent randomness in quantum measurements. Our goal is to generate fast random numbers at a lower cost. At the same time, a high level of randomness is essential. We present the improved novel quantum random number generator, which is based the on time of arrival QRNG. It is rather efficient, as it uses the simple version of the detectors with rather few requirements. The novel QRNG produces more than one random bit per each photon detection.

A reconfigurable PUF structure with dual working modes based on entropy separation model

The requirement of encryption is everywhere whether data is being transmitted or being stored in some devices. Random numbers can be used in data encryption. Every IoT device which is connected to a network needs secure and low cost techniques for encryption. In modern day the introduction of cryptocurrency wallet and other wallet services need more secure and random key for encryption which cannot be generated through algorithm-based Pseudo Random Number Generator (PRNG). The advanced video games require true random numbers. The connected portable IoT devices also transfer sensitive data. So, a low cost and simple circuit of True Random Number Generator (TRNG) effectuate the requirements. A non-deterministic signal or a noise signal is a source of true randomness. Detection of non-deterministic signal using sensors is a simple approach which is used in this paper to generate True Random Numbers. The TRNG proposed in this paper have simple hardware setup which includes an Arduino Uno board, LDR sensor and sound sensor. A simple modular equation and EXOR logic is used as processing algorithm. The random number generated through this setup passes all the tests of randomness given in the NIST-STS test.

The transition effect ring oscillator (TERO) based true random number generator (TRNG) was proposed by Varchola and Drutarovsky in 2010. There were several stochastic models for this advanced TRNG based on ring oscillator. This paper proposed an improved TERO based TRNG and implements both on Altera Cyclone series FPGA platform and on a 0.13um CMOS ASIC process. FPGA experimental results show that this balanced TERO TRNG is in good performance as the experimental data results past the national institute of standards and technology (NIST) test in 1M bit/s. The TRNG is feasible for a security SoC.