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. 2022 Mar 8;15(5):4448–4456. doi: 10.1007/s12274-022-4109-9

A flexible and stretchable bionic true random number generator

Yongbiao Wan 1,2,#, Kun Chen 1,2,#, Feng Huang 1,2, Pidong Wang 1,2, Xiao Leng 1,2, Dong Li 1,2, Jianbin Kang 1,2, Zhiguang Qiu 3,✉, Yao Yao 1,2,✉
PMCID: PMC8902273  PMID: 35281218

Abstract

The volume of securely encrypted data transmission increases continuously in modern society with all things connected. Towards this end, true random numbers generated from physical sources are highly required for guaranteeing security of encryption and decryption schemes for exchanging sensitive information. However, majority of true random number generators (TRNGs) are mechanically rigid, and thus cannot be compatibly integrated with some specific flexible platforms. Herein, we present a flexible and stretchable bionic TRNG inspired by the uniqueness and randomness of biological architectures. The flexible TRNG film is molded from the surface microstructures of natural plants (e.g., ginkgo leaf) via a simple, low-cost, and environmentally friendly manufacturing process. In our proof-of-principle experiment, the TRNG exhibits a fast generation speed of up to 1.04 Gbit/s, in which random numbers are fully extracted from laser speckle patterns with a high extraction rate of 72%. Significantly, the resulting random bit streams successfully pass all randomness test suites including NIST, TestU01, and DIEHARDER. Even after 10,000 times cyclic stretching or bending tests, or during temperature shock (−25–80 °C), the bionic TRNG still reveals robust mechanical reliability and thermal stability. Such a flexible TRNG shows a promising potential in information security of emerging flexible networked electronics.

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Electronic Supplementary Material

Supplementary material (light path diagram of transmitted laser speckle, pseudo random pattern, statistical distribution of bionic microstructures, haze of the bionic TRNG film, multi-layer circular laser intensity pattern, percentage of bit 0/1 for different hashed images, Pearson correlation coefficient between 100 different speckle images, the whole process of randomness extraction, SEM images of the flexible TRNG film after 10,000 times stretching and bending, continuous work stability of the TRNG at low or high temperature, light path diagram of reflective laser speckle, and detailed randomness test results of NIST, TestU01, and DIEHARDER) is available in the online version of this article at 10.1007/s12274-022-4109-9.

Keywords: random number generator, flexible electronics, polydimethylsiloxane (PDMS), bionic microstructure, information security

Electronic Supplementary Material

12274_2022_4109_MOESM1_ESM.pdf (2.7MB, pdf)

A flexible and stretchable bionic true random number generator

Acknowledgements

This study was financially supported by the funds of the Science Challenging Project (No. TZ2018003) and the National Natural Science Foundation of China (Nos. 12175204, 61875178, 61805218, and 12104423).

Author contributions

Y. Wan and K. Chen contributed equally to this work. Y. Wan and Y. Yao conceived the idea. Y. Yao supervised the project. Y. Wan, K. Chen, and Z. Qiu implemented the main experiments. F. Huang, P. Wang, X. Leng, D. Li, and J. Kang participated in experiment discussions. Y. Wan and Z. Qiu designed the schematic diagrams. Y. Wan, K. Chen, and Y. Yao drafted the manuscript. All authors contributed to the revised manuscript.

Footnotes

Yongbiao Wan and Kun Chen contributed equally to this work.

Contributor Information

Zhiguang Qiu, Email: zhiguangqiu88@gmail.com.

Yao Yao, Email: yaoyao_mtrc@caep.cn.

References

  • [1].Marx V. The big challenges of big data. Nature. 2013;498:255–260. doi: 10.1038/498255a. [DOI] [PubMed] [Google Scholar]
  • [2].Yang Y. Multi-tier computing networks for intelligent IoT. Nat. Electron. 2019;2:4–5. doi: 10.1038/s41928-018-0195-9. [DOI] [Google Scholar]
  • [3].Sfar A R, Natalizio E, Challal Y, Chtourou Z. A roadmap for security challenges in the internet of things. Digital Commun. Netw. 2018;4:118–137. doi: 10.1016/j.dcan.2017.04.003. [DOI] [Google Scholar]
  • [4].Wan Y B, Wang P D, Huang F, Yuan J, Li D, Chen K, Kang J B, Li Q, Zhang T P, Sun S, et al. Bionic optical physical unclonable functions for authentication and encryption. J. Mater. Chem. C. 2021;9:13200–13208. doi: 10.1039/D1TC02883A. [DOI] [Google Scholar]
  • [5].Jin C, Chen W X, Cao Y K, Xu Z W, Tan Z M, Zhang X, Deng L, Zheng C S, Zhou J, Shi H S, et al. Development and evaluation of an artificial intelligence system for COVID-19 diagnosis. Nat. Commun. 2020;11:5088. doi: 10.1038/s41467-020-18685-1. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • [6].Karaklajić D, Schmidt J M, Verbauwhede I. Hardware designer’s guide to fault attacks. IEEE Trans. Very Large Scale Integr. Syst. 2013;21:2295–2306. doi: 10.1109/TVLSI.2012.2231707. [DOI] [Google Scholar]
  • [7].Mao S F, Wolf T. Hardware support for secure processing in embedded systems. IEEE Trans. Comput. 2010;59:847–854. doi: 10.1109/TC.2010.32. [DOI] [Google Scholar]
  • [8].Pappu R, Recht B, Taylor J, Gershenfeld N. Physical one-way functions. Science. 2002;297:2026–2030. doi: 10.1126/science.1074376. [DOI] [PubMed] [Google Scholar]
  • [9].Fischer I, Gauthier D J. High-speed harvesting of random numbers. Science. 2021;371:889–890. doi: 10.1126/science.abg5445. [DOI] [PubMed] [Google Scholar]
  • [10].Wang P D, Chen F L, Li D, Sun S, Huang F, Zhang T P, Li Q, Chen K, Wan Y B, Leng X, et al. Authentication of optical physical unclonable functions based on single-pixel detection. Phys. Rev. Appl. 2021;16:054025. doi: 10.1103/PhysRevApplied.16.054025. [DOI] [Google Scholar]
  • [11].Shannon C E. Communication theory of secrecy systems. Bell Syst. Tech. J. 1949;28:656–715. doi: 10.1002/j.1538-7305.1949.tb00928.x. [DOI] [Google Scholar]
  • [12].Chen S. Random number generators go public. Science. 2018;360:1383–1384. doi: 10.1126/science.360.6396.1383. [DOI] [PubMed] [Google Scholar]
  • [13].Gisin N, Ribordy G, Tittel W, Zbinden H. Quantum cryptography. Rev. Mod. Phys. 2002;74:145–195. doi: 10.1103/RevModPhys.74.145. [DOI] [Google Scholar]
  • [14].Barreno M, Nelson B, Joseph A D, Tygar J D. The security of machine learning. Mach. Learn. 2010;81:121–148. doi: 10.1007/s10994-010-5188-5. [DOI] [Google Scholar]
  • [15].Varnava C. FinFETs for cryptography. Nat. Electron. 2020;3:732–732. doi: 10.1038/s41928-020-00521-5. [DOI] [Google Scholar]
  • [16].Skrzypczyk P. Predictably random. Nat. Phys. 2021;17:431–432. doi: 10.1038/s41567-021-01177-4. [DOI] [Google Scholar]
  • [17].Chen K, Huang F, Wang P D, Wan Y B, Li D, Yao Y. Fast random number generator based on optical physical unclonable functions. Opt. Lett. 2021;46:4875–4878. doi: 10.1364/OL.435221. [DOI] [PubMed] [Google Scholar]
  • [18].Jiang H, Belkin D, Savel’ev S E, Lin S Y, Wang Z R, Li Y N, Joshi S, Midya R, Li C, Rao M Y, et al. A novel true random number generator based on a stochastic diffusive memristor. Nat. Commun. 2017;8:882. doi: 10.1038/s41467-017-00869-x. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • [19].Woo K S, Wang Y M, Kim Y, Kim J, Kim W, Hwang C S. A combination of a volatile-memristor-based true random-number generator and a nonlinear-feedback shift register for high-speed encryption. Adv. Electron. Mater. 2020;6:1901117. doi: 10.1002/aelm.201901117. [DOI] [Google Scholar]
  • [20].Kim G, In J H, Kim Y S, Rhee H, Park W, Song H C, Park J, Kim K M. Self-clocking fast and variation tolerant true random number generator based on a stochastic mott memristor. Nat. Commun. 2021;12:2906. doi: 10.1038/s41467-021-23184-y. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • [21].Wen C, Li X H, Zanotti T, Puglisi F M, Shi Y Y, Saiz F, Antidormi A, Roche S, Zheng W W, Liang X H, et al. Advanced data encryption using 2D materials. Adv. Mater. 2021;33:2100185. doi: 10.1002/adma.202100185. [DOI] [PubMed] [Google Scholar]
  • [22].Li X H, Zanotti T, Wang T, Zhu K C, Puglisi F M, Lanza M. Random telegraph noise in metal-oxide memristors for true Random number generators: A materials study. Adv. Funct. Mater. 2021;31:2102172. doi: 10.1002/adfm.202102172. [DOI] [Google Scholar]
  • [23].Gaviria Rojas W A, McMorrow J J, Geier M L, Tang Q Y, Kim C H, Marks T J, Hersam M C. Solution-processed carbon nanotube true random number generator. Nano Lett. 2017;17:4976–4981. doi: 10.1021/acs.nanolett.7b02118. [DOI] [PubMed] [Google Scholar]
  • [24].Brown J, Zhang J F, Zhou B, Mehedi M, Freitas P, Marsland J, Ji Z G. Random-telegraph-noise-enabled true random number generator for hardware security. Sci. Rep. 2020;10:17210. doi: 10.1038/s41598-020-74351-y. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • [25].Wali A, Ravichandran H, Das S. A machine learning attack resilient true Random number generator based on stochastic programming of atomically thin transistors. ACS Nano. 2021;15:17804–17812. doi: 10.1021/acsnano.1c05984. [DOI] [PubMed] [Google Scholar]
  • [26].Yu A F, Chen X Y, Cui H T, Chen L B, Luo J J, Tang W, Peng M Z, Zhang Y, Zhai J Y, Wang Z L. Self-powered random number generator based on coupled triboelectric and electrostatic induction effects at the liquid–dielectric interface. ACS Nano. 2016;10:11434–11441. doi: 10.1021/acsnano.6b07030. [DOI] [PubMed] [Google Scholar]
  • [27].Kim M S, Tcho I W, Park S J, Choi Y K. Random number generator with a chaotic wind-driven triboelectric energy harvester. Nano Energy. 2020;78:105275. doi: 10.1016/j.nanoen.2020.105275. [DOI] [Google Scholar]
  • [28].Kim K, Bittner S, Zeng Y Q, Guazzotti S, Hess O, Wang Q J, Cao H. Massively parallel ultrafast random bit generation with a chip-scale laser. Science. 2021;371:948–952. doi: 10.1126/science.abc2666. [DOI] [PubMed] [Google Scholar]
  • [29].Uchida A, Amano K, Inoue M, Hirano K, Naito S, Someya H, Oowada I, Kurashige T, Shiki M, Yoshimori S, et al. Fast physical random bit generation with chaotic semiconductor lasers. Nat. Photonics. 2008;2:728–732. doi: 10.1038/nphoton.2008.227. [DOI] [Google Scholar]
  • [30].Kanter I, Aviad Y, Reidler I, Cohen E, Rosenbluh M. An optical ultrafast random bit generator. Nat. Photonics. 2010;4:58–61. doi: 10.1038/nphoton.2009.235. [DOI] [Google Scholar]
  • [31].Bai B, Huang J Y, Qiao G R, Nie Y Q, Tang W J, Chu T, Zhang J, Pan J W. 18.8. Gbps real-time quantum random number generator with a photonic integrated chip. Appl. Phys. Lett. 2021;118:264001. doi: 10.1063/5.0056027. [DOI] [Google Scholar]
  • [32].Gabriel C, Wittmann C, Sych D, Dong R F, Mauerer W, Andersen U L, Marquardt C, Leuchs G. A generator for unique quantum random numbers based on vacuum states. Nat. Photonics. 2010;4:711–715. doi: 10.1038/nphoton.2010.197. [DOI] [Google Scholar]
  • [33].Avesani M, Marangon D G, Vallone G, Villoresi P. Source-device-independent heterodyne-based quantum random number generator at 17 Gbps. Nat. Commun. 2018;9:5365. doi: 10.1038/s41467-018-07585-0. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • [34].Liu Y, Zhao Q, Li M H, Guan J Y, Zhang Y B, Bai B, Zhang W J, Liu W Z, Wu C, Yuan X, et al. Device-independent quantum random-number generation. Nature. 2018;562:548–551. doi: 10.1038/s41586-018-0559-3. [DOI] [PubMed] [Google Scholar]
  • [35].Luo Q, Cheng Z D, Fan J K, Tan L J, Song H Z, Deng G W, Wang Y, Zhou Q. Quantum random number generator based on single-photon emitter in gallium nitride. Opt. Lett. 2020;45:4224–4227. doi: 10.1364/OL.396561. [DOI] [PubMed] [Google Scholar]
  • [36].Sznitko L, Chtouki T, Sahraoui B, Mysliwiec J. Bichromatic laser dye as a photonic Random number generator. ACS Photonics. 2021;8:1630–1638. doi: 10.1021/acsphotonics.0c01927. [DOI] [Google Scholar]
  • [37].Lee E C, Parrilla-Gutierrez J M, Henson A, Brechin E K, Cronin L. A crystallization robot for generating true random numbers based on stochastic chemical processes. Matter. 2020;2:649–657. doi: 10.1016/j.matt.2020.01.024. [DOI] [Google Scholar]
  • [38].Meiser L C, Koch J, Antkowiak P L, Stark W J, Heckel R, Grass R N. DNA synthesis for true random number generation. Nat. Commun. 2020;11:5869. doi: 10.1038/s41467-020-19757-y. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • [39].Pironio S, Acín A, Massar S, de la Giroday A B, Matsukevich D N, Maunz P, Olmschenk S, Hayes D, Luo L, Manning T A, Monroe C. Random numbers certified by Bell’s theorem. Nature. 2010;464:1021–1024. doi: 10.1038/nature09008. [DOI] [PubMed] [Google Scholar]
  • [40].Wan Y B, Qiu Z G, Huang J, Yang J Y, Wang Q, Lu P, Yang J L, Zhang J M, Huang S Y, Wu Z G, et al. Natural plant materials as dielectric layer for highly sensitive flexible electronic skin. Small. 2018;14:1801657. doi: 10.1002/smll.201801657. [DOI] [PubMed] [Google Scholar]
  • [41].Qiu Z G, Wan Y B, Zhou W H, Yang J Y, Yang J L, Huang J, Zhang J M, Liu Q X, Huang S Y, Bai N N, et al. Ionic skin with biomimetic dielectric layer templated from Calathea zebrineleaf. Adv. Funct. Mater. 2018;28:1802343. doi: 10.1002/adfm.201802343. [DOI] [Google Scholar]
  • [42].Wan Y B, Qiu Z G, Hong Y, Wang Y, Zhang J M, Liu Q X, Wu Z G, Guo C F. A highly sensitive flexible capacitive tactile sensor with sparse and high-aspect-ratio microstructures. Adv. Electron. Mater. 2018;4:1700586. doi: 10.1002/aelm.201700586. [DOI] [Google Scholar]
  • [43].Wan Y B, Wang Y, Guo C F. Recent progresses on flexible tactile sensors. Mater. Today Phys. 2017;1:61–73. doi: 10.1016/j.mtphys.2017.06.002. [DOI] [Google Scholar]
  • [44].Qin D, Xia Y N, Whitesides G M. Soft lithography for micro- and nanoscale patterning. Nat. Protoc. 2010;5:491–502. doi: 10.1038/nprot.2009.234. [DOI] [PubMed] [Google Scholar]
  • [45].Pappu R S. Physical one-way functions. Cambridge, MA, USA: Massachusetts Institute of Technology; 2001. [Google Scholar]
  • [46].Zhang J. Visualization for Information Retrieval. New York: Springer Science & Business Media; 2007. [Google Scholar]
  • [47].Rényi, A. On measures of entropy and information. In Proceedings of the Fourth Berkeley Symposium on Mathematical Statistics and Probability, Berkeley, USA, 1961, pp 547–561.
  • [48].Ma X F, Xu F H, Xu H, Tan X Q, Qi B, Lo H K. Postprocessing for quantum random-number generators: Entropy evaluation and randomness extraction. Phys. Rev. A. 2013;87:062327. doi: 10.1103/PhysRevA.87.062327. [DOI] [Google Scholar]
  • [49].Sadat M N, Aziz M M A, Mohammed N, Pakhomov S, Liu H F, Jiang X Q. A privacy-preserving distributed filtering framework for NLP artifacts. BMC Med. Inform. Decis. Mak. 2019;19:183. doi: 10.1186/s12911-019-0867-z. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • [50].Ji L, Gallo K. An agreement coefficient for image comparison. Photogramm. Eng. Rem. Sens. 2006;72:823–833. doi: 10.14358/PERS.72.7.823. [DOI] [Google Scholar]
  • [51].Rukhin, A.; Soto, J.; Nechvatal, J. R.; Smid, M. E.; Barker, E. B.; Leigh, S. D.; Levenson, M.; Vangel, M., Banks, D. L.; Heckert, N. A. et al. A statistical test suite for random and pseudorandom number generators for cryptographic applications. NIST Special Publication 800–22, Revision 1a, 2010.
  • [52].L’Ecuyer P, Simard R. TestU01: A C library for empirical testing of random number generators. ACM Trans. Math. Software. 2007;33:22. [Google Scholar]
  • [53].Brown, R. G. Dieharder: A Random Number Test Suite, Version 3.31.1 [Online]. Robert G. Brown, NC, 2022; pp. https://webhome.phy.duke.edu/∼rgb/General/dieharder.php (accessed)

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Supplementary Materials

12274_2022_4109_MOESM1_ESM.pdf (2.7MB, pdf)

A flexible and stretchable bionic true random number generator

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