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Journal of the Korean Institute of Electrical and Electronic Material Engineers

Recent Advances in True Random Number Generators Based on Emerging Material-Based Semiconductor Devices: Toward Wearable and Flexible Electronics

Yooyeon Jo, Daniel Juhyung Joe, Joon Young Kwak

Published Sep 1, 2026• 0 citations
Publisher

Abstract

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.

DOI
10.4313/jeem.2026.39.5.1

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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.
This paper presents a lightweight true random number generator (TRNG) using beta radiation that is useful for Internet of Things (IoT) security. In general, a random number generator (RNG) is required for all secure communication devices because random numbers are needed to generate encryption keys. Most RNGs are computer algorithms and use physical noise as their seed. However, it is difficult to obtain physical noise in small IoT devices. Since IoT security functions are required in almost all countries, IoT devices must be equipped with security algorithms that can pass the cryptographic module validation programs of each country. In this regard, it is very cumbersome to embed security algorithms, random number generation algorithms, and even physical noise sources in small IoT devices. Therefore, this paper introduces a lightweight TRNG comprising a thin-film beta-radiation source and integrated circuits (ICs). Although the ICs are currently being designed, the IC design was functionally verified at the board level. Our random numbers are output from a verification board and tested according to National Institute of Standards and Technology standards.
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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. 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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.
One of the solutions to the problem of secure communication is the Quantum Random Number Generators (QRNGs) which generate true randomness based on quantum mechanical principles.QRNGs are different from conventional pseudo random number generator in that they use quantum uncertainty to generate outputs which are very unpredictable.This work attempts to examine different QRNG architectures, study the possible integration of cryptographic frameworks with these architectures and highlight potential vulnerabilities in the integration of cryptographic frameworks with QRNG architectures.Post-processing techniques, i.e.Zero-Phase Component Analysis (ZPCA) and neural network based techniques are also studied to increase entropy and suppress environmental noise in QRNG systems.Key findings show that system jitter models based QRNG architectures are more scalable and less expensive for use in real world applications.Additionally, incorporating cryptographic protocols with the QRNGs endows them with the ability to resist quantum threats, and hence, provide better key distribution in the secure IoT communication frameworks.However, as security problems such as side channel attacks and hardware manipulation are still big issues.It also points out some of the emerging research trends including hybrid QRNG architectures, i.e., QRNG with classical PRNG to increase the entropy rate.As such, the proposed methodologies attempt to enhance QRNG scalability, security, and energy efficiency in cloud infrastructures, in mobile systems and in critical security environments.The advancement of these technologies will assume a crucial function in the advancement of quantum-safe frames for current advanced computerized foundation.
Secure data transmission depends on unpredictable cryptographic keys. Conventional pseudorandom number generators are deterministic and vulnerable to prediction. This work proposes a Quantum True Random Number Generator (Q-TRNG) that uses quantum superposition and noise to produce truly random numbers. The generated quantum entropy is converted into high-quality random bits and applied to secure encryption, improving confidentiality and resistance to attacks. The proposed system offers a reliable and future- ready solution for quantum-grade secure communication.
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.