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arXiv:2002.02032 · Applied Physics Letters

Random number generation from a quantum tunneling diode

Kanin Aungskunsiri, Ratthasart Amarit, Kruawan Wongpanya, Sakdinan Jantarachote, Wittawat Yamwong, Siriporn Saiburee, Sataporn Chanhorm, Apichart Intarapanich, Sarun Sumriddetchkajorn

Published Aug 16, 2021• 8 citations
arXivHTMLPDF

Abstract

Random numbers are important in many activities, including communication, encryption, science, gambling, finance, and decision making. There is a strong demand for a hardware random number generator that could support cryptographic applications. In this work, we propose a quantum tunneling diode as a source of true randomness achieved by applying electrical current sweeps through the device and then harnessing a time-counting unit to measure fluctuation of current flows. Our approach can be implemented with inexpensive electronics and could be integrated into systems that require random numbers such as portable communication devices.

DOI
10.1063/5.0055955

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Random numbers are important in many activities, including communication, encryption, science, gambling, finance, and decision making. There is a strong demand for a hardware random number generator that could support cryptographic applications. In this work, we propose a quantum tunneling diode as a source of true randomness achieved by applying electrical current sweeps through the device and then harnessing a time-counting unit to measure fluctuation of current flows. Our approach can be implemented with inexpensive electronics and could be integrated into systems that require random numbers such as portable communication devices.
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Quantum random-number generators (QRNGs) are important for numerical simulations, communication protocols, and numerous algorithms, as the true randomness coming from quantum mechanics is indispensable. Commercial QRNGs are being held back, though, because many schemes require photon sources and high-precision measurements, which increase cost and limit portability and stability. In this study, the variable time interval between quantum tunneling events in silicon diodes yield a small, reliable, chip-integrable device that churns out random numbers at 6.89 Mb/s.
Random number generation is crucial in many aspects of everyday life, as online security and privacy depend ultimately on the quality of random numbers. Many current implementations are based on pseudo-random number generators, but information security requires true random numbers for sensitive applications like key generation in banking, defence or even social media. True random number generators are systems whose outputs cannot be determined, even if their internal structure and response history are known. Sources of quantum noise are thus ideal for this application due to their intrinsic uncertainty. In this work, we propose using resonant tunnelling diodes as practical true random number generators based on a quantum mechanical effect. The output of the proposed devices can be directly used as a random stream of bits or can be further distilled using randomness extraction algorithms, depending on the application.
Random numbers are indispensable resources for application in modern science and technology. Therefore, a dedicated entropy source is essential, particularly for cryptographic tasks and modern applications. In this work, we experimentally demonstrated a scheme to generate random numbers by multiplexing eight tunnel diodes onto a single circuit. As a result, the data rate of random number generation was significantly enhanced eightfold. In comparison to the original scheme that employed one diode, this multiplexing scheme produced data with higher entropy. These data were then post-processed with the Toeplitz-hashing extractor, yielding final outputs that achieved almost full entropy and passed the U.S. National Institute of Standards and Technology (NIST) Special Publication 800-90B validation. These data also passed the NIST Special Publication 800-22 statistical randomness examination and had no sign of patterns detected from an autocorrelation analysis.
This paper proposes a random number generator based on the quantum tunnel effect that takes place in transistors. The main system is divided into two basic components: the entropy source and the postprocessing block. The quantum tunnel effect is subjected to a random character, which means that electrons have a probability to pass through the potential barrier of the transistor. Theoretically, the measurable output current of the transistor should have a random variance in a predetermined interval, that results into a random sequence. Afterwards, the postprocessing block extracts the bias from the generated array. It is implemented through a hash function which employs the Toeplitz matrix, resulting into a true random sequence. This paper accounts the preliminary stages of development of an original random number generator. The focus was the physical phenomena, the tunnel effect in transistors, and the study of its probability with respect to the device parameters. In terms of postprocessing the main regard was choosing the adequate extraction method for the quantum entropy source. The novelty of the paper is represented by using the tunnel FET as an entropy source and finding a suitable postprocessing model for a higher generation rate.
Even if the output of a Random Number Generator (RNG) is perfectly uniformly distributed, it may be correlated to pre-existing information and therefore be predictable. Statistical tests are thus not sufficient to guarantee that an RNG is usable for applications, e.g., in cryptography or gambling, where unpredictability is important. To enable such applications a stronger notion of randomness, termed "true randomness", is required, which includes independence from prior information. Quantum systems are particularly suitable for true randomness generation, as their unpredictability can be proved based on physical principles. Practical implementations of Quantum RNGs (QRNGs) are however always subject to noise, i.e., influences which are not fully controlled. This reduces the quality of the raw randomness generated by the device, making it necessary to post-process it. Here we provide a framework to analyse realistic QRNGs and to determine the post-processing that is necessary to turn their raw output into true randomness.