Spontaneous High-Dynamic-Range Random Current Spiking in BaF2 Memristors

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.

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.

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.

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

A true random number generator (TRNG) is suitable for generating secure keys and nonces. TRNGs implemented in IoT devices must be small in scale and have low power consumption. The random number sequence generated by TRNG needs to have high entropy immediately after startup and a stable state. In this paper, three types of ring oscillator type TRNGs, TERO-based, COSO-based, and STR-based TRNG, are implemented on Zynq-7010. When these TRNGs are implemented as a single entropy source, it is challenging to implement them because it is necessary to evaluate the layout and wiring for each FPGA. This paper proposes a TRNG configuration, which exclusively ORs the outputs of multiple entropy sources. We show that this configuration can reduce the implementing difficulty and realize high entropy. For the random number sequence evaluation, we use the statistical test of NIST SP800-90B and BSI AIS 20/31. In addition, the random number sequence immediately after the startup is also statistically evaluated. As a result, our proposed TRNGs generate high entropy random numbers and are easy to implement on FPGA when we implement TRNGs with eight single entropy sources for TERO-based TRNG, 48 for COSO-based TRNG, and two for STR-based TRNG, respectively.

The generation of true random numbers is one of the most important tasks in a hardware security module (HSM), particularly for cryptography applications. The stochastic behavior of electronic devices can be used to generate random numbers. In this paper, a reliable neuromorphic true random number generator (TRNG) relying on stochastic switching of the magnetic tunnel junction (MTJ) in the subcritical current regime is proposed. Thanks to the efficient structure of the proposed design as well as the fascinating features of the MTJs and carbon nanotube field-effect transistors (CNTFET), the proposed TRNG consumes low power. Moreover, the neuromorphic structure of the proposed design leads to variation tolerance and guarantees the truly random number generation even in the presence of the process variations. HSPICE simulations verify the functionality of the proposed TRNG. Furthermore, by considering the corners of the fabrication process, the randomness of the bitstream, generated by the proposed TRNG, is validated by the statistical randomness test provided by the U.S National Institute of Standards and Technology (NIST).

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.

A chaos-based true random number generator based on OTA sharing and non-flipped folded Bernoulli mapping for high-precision ADC calibration

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 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.

1. IntroductionRandom numbers are an important resource in a science and engineering, such as numerical simulations, cryptography and machine learning. A Faster and simpler true random number generators (TRNG), which employs physical phenomena as a noise source, is needed. However, the TRNGs that is compact and faster than GHz is not realized yet.We focused on the magnetic-domain patterns in magnetic garnets as a noise source for TRNG. The magnetic domain can be generated repeatedly and chaotically, and converted to digital value at few Gbit/s by image sensors. The random numbers that generated from the labyrinth magnetic-domain patterns were passed the statistic randomness test (NIST SP800-22) [1]. In this study, other types of magnetic domains were investigated as the noise source.2. ExperimentalSputtered-polycrystalline bismuth substituted yttrium iron garnet (Bi:YIG) films on glass and gadolinium gallium garnet (GGG) substrates, and liquid phase epitaxy (LPE) holmium iron garnet (HoIG) film were investigated. About 3,000 magnetic domain images of each garnet were captured using a polarized light microscope, as shown in Fig. 1.The obtained images were processed to random sequence. The data process was composed of binarization, stable-pattern removal, bit-shift, and XOR. A hundred of the 10 Mbit sequences were generated and evaluated by the randomness test (NIST SP800-22) [2].3. Result and discussionA hundred of the 10 Mbit sequences were generated and tested. The results of the randomness test is shown in table 1. The NIST SP800-22 includes 15 periodicity tests. The p-value more than 0.01 indicates that the sequence is non-periodic (random). The pass rates on the table 1 is a ratio of the statistically random sequence. The pass rate more than 0.97 is passed the randomness test. The all samples passed almost of all tests. The low-score with the FFT test could be improved by optimization of the data processing. These results suggest that the random sequences could be generated from many types of magnetic domains.  Fig. 1 Polarized microscope image of the garnet films  Table. 1 Result of SP800-22 for 100×10Mbit sequences

This paper shows the research on hardware random number generator (HRNG). As truly random numbers are strongly required in encryption and computer simulation areas, developing a simple and inexpensive HRNG has significant value. The whole system is divided into the noise generating module and the processing module. After the numbers are generated, a randomness test has been carried out which indicates that the random numbers generated are truly random. It is concluded that the final product of this HRNG meets the requirements of the objectives.

We present a circuit technique to extract true random numbers from carrier capture and emission in oxide traps in the emerging redox-based resistive memory (ReRAM). This phenomenon that appears as small changes in current magnitude passing through the device is known as random telegraph noise (RTN) and is increasingly becoming a source of reliability issues in nanometer-scale devices. We demonstrate a circuit that exploits TRN suitable for a true random number generator (TRNG) in security applications, where the system is secure from different adversarial attacks, including side-channel monitoring and machine learning analysis. We experimentally characterize RTN in ReRAMs and extract its dependency to temperature, voltage, and area. We introduce an RTN harvesting circuit to mitigate sensitivities to temperature fluctuations, injected supply noise, and power signal monitoring. We reduced bias and imbalance in data due to high-speed sampling via von Neumann whitening. The circuit is compared to conventional non-differential readout approach. Our approach shows a 7.26 times improvement in autocorrelation and significant resilience against the injected supply noise. We also demonstrate the TRNG’s quality and robustness using statistical tests and machine learning attacks. The output of the generator satisfies statistical tests for randomness and is immune to modeling attacks based on the machine learning methods.

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.