The need for hardware random number generators (HRNGs) that can be integrated in a silicon (Si) complementary-metal–oxide–semiconductor (CMOS) platform has become increasingly important in the era of the Internet-of-Things (IoT). Si MOSFETs exhibiting random telegraph signals (RTSs) have been considered as such a candidate for HRNG, though its application has been hindered by RTS’s variability and uncontrollable, unpredictable characteristics. In this paper, we report the generation and randomness evaluation of random numbers from RTSs in a Si single electron pump (SEP) device at room temperature. SEP devices are known to consistently produce RTSs due to a quantum dot electrically defined by multi-layer polycrystalline Si gates. Using RTSs observed in our devices, random numbers were extracted by a classifier supported by supervised learning, where part of data was used to train the classifier before it is applied to the rest to generate random numbers. The random numbers generated from RTSs were used as inputs for the Monte Carlo method to calculate the values of π, and the distribution was compared against the result obtained from the Mersenne Twister, a representative pseudo-random number generator (PRNG), under the same condition. π was estimated more than 80 000 times, and the distribution of the estimated values has a central value of 3.14 with a variance of 0.273, which is only twice as large as the result from PRNG. Our result paves a way to fully electronic CMOS compatible HRNGs that can be integrated in a modern system-on-a-chip in IoT devices.
The need for hardware random number generators (HRNGs) that can be integrated in a silicon (Si) complementary-metal–oxide–semiconductor (CMOS) platform has become increasingly important in the era of the Internet-of-Things (IoT). Si MOSFETs exhibiting random telegraph signals (RTSs) have been considered as such a candidate for HRNG, though its application has been hindered by RTS’s variability and uncontrollable, unpredictable characteristics. In this paper, we report the generation and randomness evaluation of random numbers from RTSs in a Si single electron pump (SEP) device at room temperature. SEP devices are known to consistently produce RTSs due to a quantum dot electrically defined by multi-layer polycrystalline Si gates. Using RTSs observed in our devices, random numbers were extracted by a classifier supported by supervised learning, where part of data was used to train the classifier before it is applied to the rest to generate random numbers. The random numbers generated from RTSs were used as inputs for the Monte Carlo method to calculate the values of π, and the distribution was compared against the result obtained from the Mersenne Twister, a representative pseudo-random number generator (PRNG), under the same condition. π was estimated more than 80 000 times, and the distribution of the estimated values has a central value of 3.14 with a variance of 0.273, which is only twice as large as the result from PRNG. Our result paves a way to fully electronic CMOS compatible HRNGs that can be integrated in a modern system-on-a-chip in IoT devices.
Quantum random number generators (QRNGs) are crucial components for security applications, but the on-chip integration is often blocked by the incompatibility of mainstream optical schemes with standard Complementary Metal-Oxide-Semiconductor (CMOS) processes. In this study, artificially designed quantum dots are built in a low-doped silicon nanowire using local doping method. Which is fully CMOS-compatible. By manipulating the gate voltage (Vg) of the silicon nanowire device with a drain voltage (Vd) of 50 mV, a 1 1 high/low ratio is almost achieved as the quantum-induced random telegraph signal (RTS) at Vg = -270 mV. The binary sequence generated through sampling exhibits near-ideal Shannon entropy (binary: 0.999998, quaternary: 1.999996) and passed 88.89% of the tests in the NIST statistical test suite. This work provides a feasible design and fabrication strategy for RTS devices used as QRNGs, facilitating the observation of RTS at room temperature and ensuring compatibility with CMOS fabrication processes.
Random numbers are fundamental elements in different fields of science and technology such as computer simulation like Monte Carlo-method simulation, statistical sampling, cryptography, games and gambling, and other areas where unpredictable results are necessary. Random number generators (RNG) are generally classified as “pseudo”-random number generators (PRNG) and "truly" random number generators (TRNG). Pseudo random numbers are generated by computer algorithms with a (random) seed and a specific formula. The random numbers produced in this way (with a small degree of unpredictability) are good enough for some applications such as computer simulation. However, for some other applications like cryptography they are not completely reliable. When the seed is revealed, the entire sequence of numbers can be produced. The periodicity is also an undesirable property of PRNGs that can be disregarded for most practical purposes if the sequence recurs after a very long period. However, the predictability still remains a tremendous disadvantage of this type of generators. Truly random numbers, on the other hand, can be generated through physical sources of randomness like flipping a coin. However, the approaches exploiting classical motion and classical physics to generate random numbers possess a deterministic nature that is transferred to the generated random numbers. The best solution is to benefit from the assets of indeterminacy and randomness in quantum physics. Based on the quantum theory, the properties of a particle cannot be determined with arbitrary precision until a measurement is carried out. The result of a measurement, therefore, remains unpredictable and random. Optical phenomena including photons as the quanta of light have various random, non-deterministic properties. These properties include the polarization of the photons, the exact number of photons impinging a detector and the photon arrival times. Such intrinsically random properties can be exploited to generate truly random numbers. Silicon (Si) is considered as an interesting material in integrated optics. Microelectronic chips made from Si are cheap and easy to mass-fabricate, and can be densely integrated. Si integrated optical chips, that can generate, modulate, process and detect light signals, exploit the benefits of Si while also being fully compatible with electronic. Since many electronic components can be integrated into a single chip, Si is an ideal candidate for the production of small, powerful devices. By complementary metal-oxide-semiconductor (CMOS) technology, the fabrication of compact and mass manufacturable devices with integrated components on the Si platform is achievable. In this thesis we aim to model, study and fabricate a compact photonic quantum random number generator (QRNG) on the Si platform that is able to generate high quality, "truly" random numbers. The proposed QRNG is based on a Si light source (LED) coupled with a Si single photon avalanche diode (SPAD) or an array of SPADs which is called Si photomultiplier (SiPM). Various implementations of QRNG have been developed reaching an ultimate geometry where both the source and the SPAD are integrated on the same chip and fabricated by the same process. This activity was performed within the project SiQuro—on Si chip quantum optics for quantum computing and secure communications—which aims to bring the quantum world into integrated photonics. By using the same successful paradigm of microelectronics—the study and design of very small electronic devices typically made from semiconductor materials—, the vision is to have low cost and mass manufacturable integrated quantum photonic circuits for a variety of different applications in quantum computing, measure, sensing, secure communications and services. The Si platform permits, in a natural way, the integration of quantum photonics with electronics. Two methodologies are presented to generate random numbers: one is based on photon counting measurements and another one is based on photon arrival time measurements. The latter is robust, masks all the drawbacks of afterpulsing, dead time and jitter of the Si SPAD and is effectively insensitive to ageing of the LED and to its emission drifts related to temperature variations. The raw data pass all the statistical tests in national institute of standards and technology (NIST) tests suite and TestU01 Alphabit battery without a post processing algorithm. The maximum demonstrated bit rate is 1.68 Mbps with the efficiency of 4-bits per detected photon. In order to realize a small, portable QRNG, we have produced a compact configuration consisting of a Si nanocrystals (Si-NCs) LED and a SiPM. All the statistical test in the NIST tests suite pass for the raw data with the maximum bit rate of 0.5 Mbps. We also prepared and studied a compact chip consisting of a Si-NCs LED and an array of detectors. An integrated chip, composed of Si p+/n junction working in avalanche region and a Si SPAD, was produced as well. High quality random numbers are produced through our robust methodology at the highest speed of 100 kcps. Integration of the source of entropy and the detector on a single chip is an efficient way to produce a compact RNG. A small RNG is an essential element to guarantee the security of our everyday life. It can be readily implemented into electronic devices for data encryption. The idea of "utmost security" would no longer be limited to particular organs owning sensitive information. It would be accessible to every one in everyday life.
As technologies such as artificial intelligence, autonomous driving, the Internet of Things, wearable electronics, and edge computing continue to spread in the era of the Fourth Industrial Revolution, the importance of hardware security for the safe storage, transmission, and processing of large volumes of data has grown substantially. One of the key components of such security systems is the true random number generator (TRNG), which produces unpredictable random numbers for cryptographic use. In recent years, research on TRNGs has increasingly moved beyond conventional CMOS-based approaches toward semiconductor devices built from emerging materials. These material-based TRNGs offer several advantages, including high integration density, low power consumption, compact form factors, and strong suitability for next-generation edge and IoT environments, because they can directly exploit the intrinsic stochasticity of the device itself as an entropy source. In this review, recent studies on TRNGs based on emerging material-based semiconductor devices are examined from the perspectives of entropy sources, device structures, randomness validation, and wearable/flexible extensions. By bringing together the key physical mechanisms, device platforms, evaluation criteria, and prospects for wearable and flexible electronics in edge and IoT environments, this review aims to provide a useful framework for future research on hardware security devices.
Since the security of all modern cryptographic techniques relies on unpredictable and irreproducible digital keys generated by random-number generators (RNGs), the realization of high-quality RNG is essential for secure communications. In this report, a new RNG, which utilizes single-electron phenomena, is proposed. A room-temperature operating silicon single-electron transistor (SET) having nearby an electron pocket is used as a high-quality, ultra-small RNG. In the proposed RNG, stochastic single-electron capture/emission processes to/from the electron pocket are detected with high sensitivity by the SET, and result in giant random telegraphic signals (GRTS) on the SET current. It is experimentally demonstrated that the single-electron RNG generates extremely high-quality random digital sequences at room temperature, in spite of its simple configuration. Because of its small-size and low-power properties, the single-electron RNG is promising as a key nanoelectronic device for future ubiquitous computing systems with highly secure mobile communication capabilities.
Generating reliable random and pseudo-random sequences is important in many electronic and signal processing systems, such as secure communications, radar, spread-spectrum methods, and autonomous platforms. Although true and quantum random number generators provide stronger unpredictability, classical pseudo-random number generators, including Linear Feedback Shift Registers (LFSRs) and the Mersenne Twister (MT), are still widely used because they are efficient and easy to implement. This work introduces a user-friendly software platform for generating, analyzing, and evaluating the predictability of pseudo-random bit sequences. The software supports two main functions: generating sequences using classical PRNGs and hybrid combinations, and analyzing input sequences through statistical measures and data-driven methods. In particular, hybrid LFSR-MT structures are studied to examine how they affect sequence complexity and resistance to prediction. The platform also includes machine-learning and deep-learning tools to investigate when deterministic PRNGs may remain partially predictable, even when their structure becomes more complex. The results show that algorithmic random sequence generators have inherent limitations in terms of unpredictability, which supports the use of quantum random sequences in security-critical applications. A comparative study between classical LFSR-MT sequences and quantum random sequences shows that quantum randomness offers higher unpredictability due to its non-deterministic physical origin. The potential use of quantum random sequences in jamming applications is also discussed, highlighting their improved robustness against prediction-based attacks. Overall, the proposed software provides a practical tool for analyzing, comparing, and benchmarking random sequence generators in modern electronic, sensing, and quantum-enabled communication systems.
Abstract Random numbers play a crucial role in numerous scientific applications. Source-independent quantum random number generators (SI-QRNGs) can offer true randomness by leveraging the fundamental principles of quantum mechanics, eliminating the need for a trusted source. Silicon photonics shows great promise for QRNG due to its benefits in miniaturization, cost-effective device manufacturing, and compatibility with CMOS microelectronics. In this study, we experimentally demonstrate a silicon-based discrete variable SI-QRNG. Using a well-calibrated chip and an optimized parameter strategy, we have achieved the highest quantum random number generation rate of 7.9 Mbits/s. Our research paves the way for integrated SI-QRNGs.