scholarly journals High-Density Physical Random Number Generator Using Spin Signals in Multidomain Ferromagnetic Layer

2015 ◽  
Vol 2015 ◽  
pp. 1-8 ◽  
Author(s):  
Sungwoo Chun ◽  
Seung-Beck Lee ◽  
Masahiko Hara ◽  
Wanjun Park ◽  
Song-Ju Kim
Keyword(s):  
Room Temperature ◽  
Random Number ◽  
Random Number Generator ◽  
Ferromagnetic Layer ◽  
Magnetic Tunnel Junction ◽  
Statistical Test ◽  
High Density ◽  
Number Generator ◽  
Test Results ◽  
Applied Current

A high-density random number generator (RNG) based on spin signals in a multidomain ferromagnetic layer in a magnetic tunnel junction (MTJ) is proposed and fabricated. Unlike conventional spin-based RNGs, the proposed method does not require one to control an applied current, leading to a time delay in the system. RNG demonstrations are performed at room temperature. The randomness of the bit sequences generated by the proposed RNG is verified using the FIPS 140-2 statistical test suite provided by the NIST. The test results validate the effectiveness of the proposed RNGs. Our results suggest that we can obtain high-density, ultrafast RNGs if we can achieve high integration on the chip.

scholarly journals Silicon single-electron random number generator based on random telegraph signals at room temperature

AIP Advances ◽  
2020 ◽  
Vol 10 (11) ◽  
pp. 115101
Author(s):  
Kouta Ibukuro ◽  
Fayong Liu ◽  
Muhammad Khaled Husain ◽  
Moïse Sotto ◽  
Joseph Hillier ◽  
...  
Keyword(s):  
Room Temperature ◽  
Random Number ◽  
Random Number Generator ◽  
Single Electron ◽  
Number Generator ◽  
Random Telegraph Signals

Interlaboratory proficiency-testing programs: a computer model to assess their capability to correctly characterize intralaboratory performance.

1987 ◽  
Vol 33 (6) ◽  
pp. 784-787 ◽  
Author(s):  
S S Ehrmeyer ◽  
R H Laessig
Keyword(s):  
Proficiency Testing ◽  
Computer Model ◽  
Random Number ◽  
Random Number Generator ◽  
Performance Criterion ◽  
Survey Result ◽  
Test Laboratory ◽  
Number Generator ◽  
Test Results ◽  
Simulation Process

Abstract We developed a computer model of an interlaboratory survey program to study the ability of proficiency testing (PT) programs to detect intralaboratory errors (total, random, and systematic). It uses a base interlaboratory PT population of 400 laboratories and one test laboratory each with uniquely defined intralaboratory characteristics, i.e., mean, standard deviation (SD), and bias. A gaussian random-number generator uses these parameters to simulate 401 test results analogous to the analysis of one PT sample by each laboratory. The test laboratory's intralaboratory error is expressed as various combinations of bias and coefficient of variation (CV); its simulated survey result is evaluated by a performance criterion derived from the group statistics. To eliminate statistical artifacts, the computer model repeats the complete simulation process 400 times and determines the percentage of the test laboratory's results that fail to meet a specified performance criterion. The computer model can use assigned values or actual intralaboratory data.

scholarly journals Demonstration of Three True Random Number Generator Circuits Using Memristor Created Entropy and Commercial Off-the-Shelf Components

Entropy ◽  
2021 ◽  
Vol 23 (3) ◽  
pp. 371
Author(s):  
Scott Stoller ◽  
Kristy A. Campbell
Keyword(s):  
Random Number ◽  
Random Number Generator ◽  
Statistical Test ◽  
Test Suite ◽  
Successful Implementation ◽  
Number Generator ◽  
Simple Circuit ◽  
True Random Number Generator ◽  
Commercial Off The Shelf ◽  
Circuit Components

In this work, we build and test three memristor-based true random number generator (TRNG) circuits: two previously presented in the literature and one which is our own design. The functionality of each circuit is assessed using the National Institute of Standards and Technology (NIST) Statistical Test Suite (STS). The TRNG circuits were built using commercially available off-the-shelf parts, including the memristor. The results of this work confirm the usefulness of memristors for successful implementation of TRNG circuits, as well as the ease with which a TRNG can be built using simple circuit designs and off-the-shelf breadboard circuit components.

Development of “spin dice” — A Scalable Random Number Generator Based on Spin-Torque Switching

SPIN ◽  
2019 ◽  
Vol 09 (03) ◽  
pp. 1940009
Author(s):  
Akio Fukushima ◽  
Kay Yakushiji ◽  
Hitoshi Kubota ◽  
Hiroshi Imamura ◽  
Shinji Yuasa
Keyword(s):  
Random Number ◽  
Tunnel Junctions ◽  
Random Number Generator ◽  
Magnetic Tunnel Junctions ◽  
Magnetic Tunnel Junction ◽  
Random Numbers ◽  
Spin Torque ◽  
Number Generator ◽  
Security Applications ◽  
Semiconductor Fabrication

We have developed a random-number-generator (RNG) named “spin dice,” which employs the stochastic nature of spin-torque switching (STS) in a magnetic tunnel junction. The principle of the idea is that the switching probability first tuned around 0.5 is varied linearly with the applied current. After that, the switching results are converted into binary random numbers. We fabricated several types of “spin dice” by combining magnetic tunnel junctions and single-board microcomputer, and achieved generation speed of random numbers up to several hundred kbit/sec. Because STS is scalable and magnetic tunnel junctions have compatibility to semiconductor fabrication process, “spin dice” can be considered as a promising candidate for truly random-number-generator (TRNG) for security applications.

scholarly journals A multi-levels RNG permutation

2020 ◽  
Vol 18 (1) ◽  
pp. 412
Author(s):  
Ammar Khaleel Abdulsadah ◽  
Abdullah Aziz Lafta ◽  
Mohammad Dosh
Keyword(s):  
Random Number ◽  
Random Number Generator ◽  
Suggested Method ◽  
Period Length ◽  
Number Generator ◽  
Test Results ◽  
Correlation Measure ◽  
Tree Traversal ◽  
General Method

<p><span>The paper proposes a new general method for producing a multilevel permutation functioning as an m-tree traversal. It is composed of two basic steps: a random number generator of period length equal m to determine which child to traverse, and recursive permutation in which permutated the subtree if found. The test results proved that the suggested method of permutation is successful depending on the correlation measure.</span></p>

scholarly journals Testing a hardware random number generator using NIST statistical test suite

Doklady BGUIR ◽  
2021 ◽  
Vol 19 (4) ◽  
pp. 37-42
Author(s):  
M. O. Pikuza ◽  
S. Yu. Mikhnevich
Keyword(s):  
Data Acquisition ◽  
Random Number ◽  
Random Sequence ◽  
Random Number Generator ◽  
Reverse Current ◽  
Statistical Test ◽  
Test Suite ◽  
Number Generator ◽  
Random Number Generators ◽  
Output Sequence

Random number generators are required for the operation of cryptographic information protection systems. For а correct application of the generator in the field of information security, it is necessary that its output sequence to be indistinguishable from a uniformly distributed random sequence. To verify this, it is necessary to test the generator output sequence using various statistical test suites such as Dihard and NIST. The purpose of this work is to test a prototype hardware random number generator. The generator is built on the basis of the ND103L noise diode and has a random digital sequence of binary numbers at the output. In the prototype there is a possibility of regulating the amount of reverse current through the noise diode, as well as setting the data acquisition period, i.e. data generation frequency. In the course of operation, a number of sequences of random numbers were removed from the generator at various values of the reverse current through the noise diode, the period of data acquisition and the ambient temperature. The resulting sequences were tested using the NIST statistical test suite. After analyzing the test results, it was concluded that the generator operates relatively stably in a certain range of initial parameters, while the deterioration in the quality of the generator's operation outside this range is associated with the technical characteristics of the noise diode. It was also concluded that the generator under study is applicable in certain applications and to improve the stability of its operation, it can be improved both in hardware and software. The results of this work can be useful to developers of hardware random number generators built according to a similar scheme.

scholarly journals Implementation of the Binary Random Number Generator Using the Knight Tour Problem

2016 ◽  
Vol 10 (4) ◽  
pp. 35 ◽  
Author(s):  
Ali Shakir Mahmood ◽  
Mohd Shafry Mohd Rahim ◽  
Nur Zuraifah Syazrah Othman
Keyword(s):  
Random Number ◽  
Random Number Generator ◽  
Security Analysis ◽  
Random Numbers ◽  
Number Generator ◽  
Test Results ◽  
Numerical Function ◽  
Randomness Test ◽  
Network Administrators ◽  
Strong Capacity

<p>A random number can be defined as a set of numbers produced by a numerical function, in which the next number is unpredictable and a relationship between successive occurrences is lacking. Moreover, these sequences cannot be reproduced unless the same generator function with an exact initial value is used. The design of a random number generator must overcome the previous problems of a low periodic and the capacity to reproduce the same sequence. This paper proposes the knight tour as a tool for generating pseudo random numbers. These random numbers can be use in the encryption process or in a password generator for network administrators. The randomness test suite is used to ensure the randomness of outcome sequences. Roughly, 75% of the test results obtained is better than the results from other works. The statistical properties and security analysis indicate that the knight tour application is highly successful in generating a pseudo random number with good statistical results, high linear complexity and strong capacity to withstand attacks.</p>

scholarly journals An Asynchronous and Low-Power True Random Number Generator Using STT-MTJ

2020 ◽  
Author(s):  
Ben Perach ◽  
Shahar Kvatinsky
Keyword(s):  
Low Power ◽  
Random Number ◽  
Random Number Generator ◽  
Low Frequency ◽  
Magnetic Tunnel Junction ◽  
Equation System ◽  
Spin Transfer Torque ◽  
Number Generator ◽  
Stochastic Behavior ◽  
Basic Module

<div>The Spin Transfer Torque Magnetic Tunnel Junction</div><div>(STT-MTJ) is an emerging memory technology whose interesting</div><div>stochastic behavior might benefit security applications. In this</div><div>paper, we leverage this stochastic behavior to construct a true</div><div>random number generator (TRNG), the basic module in the</div><div>process of encryption key generation. Our proposed TRNG</div><div>operates asynchronously and thus can use small and fast STT</div><div>MTJ devices. As such, it can be embedded in low-power and</div><div>low-frequency devices without loss of entropy. We evaluate</div><div>the proposed TRNG using a numerical simulation, solving the</div><div>Landau–Lifshitz–Gilbert (LLG) equation system of the STTMTJ</div><div>devices. Design considerations, attack analysis, and process</div><div>variation are discussed and evaluated. The evaluation shows that</div><div>our solution is robust to process variation, achieving a Shannonentropy</div><div>generating rate between 99.7Mbps and 127.8Mbps for</div><div>90% of the instances.</div>

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