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Bi2O2Se-Based True Random Number Generator for Security Applications.

Bo Liu1, Ying-Feng Chang2, Juzhe Li1

  • 1Faculty of Information Technology, College of Microelectronics, Beijing University of Technology, Beijing 100124, People's Republic of China.

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|March 25, 2022
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Summary
This summary is machine-generated.

This study introduces a novel Bi2O2Se-based memristive true random number generator (TRNG) for enhanced Internet of Things (IoT) security. This device offers intrinsic stochasticity for secure data encryption and trusted communication.

Keywords:
Bi2O2SeDiffie−Hellman key exchangeShapley valuelong short-term memorymemristorrandom telegraph noisetrue random number generator

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Area of Science:

  • Materials Science
  • Cybersecurity
  • Semiconductor Devices

Background:

  • The proliferation of Internet of Things (IoT) devices generates vast amounts of data, creating significant security vulnerabilities.
  • Current encryption methods often rely on software-based pseudorandom number generators susceptible to attacks.
  • True Random Number Generators (TRNGs) utilizing physical phenomena offer a more robust alternative for secure data handling.

Purpose of the Study:

  • To demonstrate a novel Bi2O2Se-based memristive device as a True Random Number Generator (TRNG) for advanced security applications.
  • To investigate the intrinsic stochasticity and complexity of Bi2O2Se memristors for reliable random number generation.
  • To evaluate the resilience of the generated random numbers against machine learning-based prediction.

Main Methods:

  • Fabrication and characterization of a Bi2O2Se-based memristive device.
  • Utilizing the device's vertical surface resistive switching for analogue/digital random number generation.
  • Assessing the randomness quality and unpredictability of the generated numbers.

Main Results:

  • The Bi2O2Se memristor exhibits intrinsic stochasticity and complexity due to its unique material properties and interface.
  • The device successfully generates both analogue and digital random numbers.
  • The random number generation process demonstrated resilience against machine learning prediction, indicating high security potential.

Conclusions:

  • Bi2O2Se-based memristive devices represent a promising platform for developing highly secure TRNGs.
  • The unique properties of Bi2O2Se contribute to intrinsic randomness, enhancing security for IoT and communication systems.
  • This approach offers a hardware-based solution to overcome the limitations of software-based security protocols.