Random Bit Generator Using Delayed Self-Difference of Filtered Amplified Spontaneous Emission

Li, Lei; Wang, Anbang; Li, Pu; Wang, Longsheng; Wang, Yuncai

doi:10.1109/jphot.2014.2304555

Cited by 19 publications

(18 citation statements)

References 27 publications

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“…1. A true-random signal was realized by detecting the output of an amplified-spontaneous-emission (ASE) light source [27] and [28]. It may be noted that we are using analog signal without converting to the digital signal.…”

Section: Experimental Investigation On Mutual Interferences a Ementioning

confidence: 99%

Mutual Interferences of a True-Random LiDAR With Other LiDAR Signals

Hwang

Lee

2020

IEEE Access

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We investigate characteristics and impacts of the mutual interference to a LiDAR using an analog true-random signal, for autonomous vehicle applications. Various LiDAR signals with a continuouswave, a pulse light from a directly modulated laser, and a frequency-modulated continuous-wave light, were generated as the mutual interference signals. The signal patterns of the interference signal formats at the receiver of the victim LiDAR were characterized by measurement results. There exist two major impacts of the mutual interference, causing a false alarm that induces ghost targets and/or images in 3-D scanning LiDARs, and reducing the SNR that degrades detection probability of an echo signal. For quantitative analysis, we estimate the cross-correlation related to the two influences. From the results, it is confirmed that the proposed LiDAR using the random signal has the mutual interference immunity regardless of the signal formats of the interference.

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Section: Experimental Investigation On Mutual Interferences a Ementioning

confidence: 99%

Mutual Interferences of a True-Random LiDAR With Other LiDAR Signals

Hwang

Lee

2020

IEEE Access

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“…described deterministically; thus, entropy can be harvested by detecting and sampling the ASE signal. There have been several different but closely related schemes to generate random numbers from ASE sources [3,[14][15][16][17][18][19][20]. Here, we discuss the system depicted in Fig.…”

Section: Rng From Amplified Spontaneous Emissionmentioning

confidence: 99%

“…There have been several different but closely related schemes to generate random numbers from ASE sources [3,[14][15][16][17][18][19][20]. Here, we discuss the system depicted in Fig.…”

Section: Bwmentioning

confidence: 99%

“…In some optical entropy sources such as single photon measurements [2,[8][9][10][11], optical parametric oscillators [12], and spontaneous Raman scattering [13], the measurements themselves are quantized. In others, such as those based on amplified spontaneous emission [3,[14][15][16][17][18][19][20], laser phase noise [21][22][23][24][25][26], quantum vacuum fluctuations [27][28][29][30][31], and stimulated Raman scattering [32,33], an unpredictable analog waveform with quantum mechanical origins is sampled and digitized. In this Perspective, we will provide an in-depth analysis of one of each type: single photon time-of-arrival measurements and amplified spontaneous emission.…”

mentioning

confidence: 99%

“…Due to the increasing importance of the security of digital information and the wide variety of physical methods used to generate random numbers, a standard set of evaluation metrics for random number generation is essential. Previous works have used a variety of methods to estimate the entropy of physical sources of randomness [8][9][10][11]19,23,[25][26][27][40][41][42]44,45,50]; however, many of these techniques assume that there are no inter-sample correlations. As of this writing, there is no widely accepted technique to estimate the entropy of physical entropy sources.…”

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confidence: 99%

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Recommendations and illustrations for the evaluation of photonic random number generators

et al. 2017

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The never-ending quest to improve the security of digital information combined with recent improvements in hardware technology has caused the field of random number generation to undergo a fundamental shift from relying solely on pseudo-random algorithms to employing optical entropy sources. Despite these significant advances on the hardware side, commonly used statistical measures and evaluation practices remain ill-suited to understand or quantify the optical entropy that underlies physical random number generation. We review the state of the art in the evaluation of optical random number generation and recommend a new paradigm: quantifying entropy generation and understanding the physical limits of the optical sources of randomness. In order to do this, we advocate for the separation of the physical entropy source from deterministic post-processing in the evaluation of random number generators and for the explicit consideration of the impact of the measurement and digitization process on the rate of entropy production. We present the Cohen-Procaccia estimate of the entropy rate h( , τ ) as one way to do this. In order to provide an illustration of our recommendations, we apply the Cohen-Procaccia estimate as well as the entropy estimates from the new NIST draft standards for physical random number generators to evaluate and compare three common optical entropy sources: single photon time-of-arrival detection, chaotic lasers, and amplified spontaneous emission. arXiv:1612.04415v3 [physics.optics]

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Semiconductor Emitters in Entropy Sources for Quantum Random Number Generation

Alkhazragi,

Lu,

Yan

et al. 2023

Annalen der Physik

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Random number generation (RNG) is needed for a myriad of applications ranging from secure communication encryption to numerical simulations to sports and games. However, generating truly random numbers can be elusive. Pseudorandom bit generation using computer algorithms provides a high random bit generation rate. Nevertheless, the reliance on predefined algorithms makes it deterministic and predictable once initial conditions are known. Relying on physical phenomena (such as measuring electrical noise or even rolling dice) can achieve a less predictable sequence of bits. Furthermore, if the physical phenomena originate from quantum effects, they can be truly random and completely unpredictable due to quantum indeterminacy. Traditionally, physical RNG is significantly slower than pseudorandom techniques. To meet the demand for high‐speed RNG with perfect unpredictability, semiconductor light sources are adopted as parts of the sources of randomness, i.e., entropy sources, in quantum RNG (QRNG) systems. The high speed of their noise, the high efficiency, and the small scale of these devices make them ideal for chip‐scale QRNG. Here, the applications and recent advances of QRNG are reviewed using semiconductor emitters. Finally, the performance of these emitters is compared and discuss their potential in future technologies.

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Random Bit Generator Using Delayed Self-Difference of Filtered Amplified Spontaneous Emission

Cited by 19 publications

References 27 publications

Mutual Interferences of a True-Random LiDAR With Other LiDAR Signals

Mutual Interferences of a True-Random LiDAR With Other LiDAR Signals

Recommendations and illustrations for the evaluation of photonic random number generators

Semiconductor Emitters in Entropy Sources for Quantum Random Number Generation

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