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((short description|Cryptographic device))
[[File:NeuG HRNG (22539520445).png|thumb|right|A USB-pluggable hardware true random number generator]]

In [[computing]], a '''hardware random number generator''' ('''HRNG'''), '''true random number generator''' ('''TRNG'''), '''non-deterministic random bit generator''' ('''NRBG'''),((sfn | Turan | Barker | Kelsey | McKay | 2018 | p=64)) or '''physical random number generator'''((sfn | Schindler | 2009 | p=7))((sfn | Stipčević | Koç | 2014 | p=279)) is a device that [[random number generation|generates random numbers]] from a [[physical process]] capable of producing [[Entropy (computing)|entropy]] (in other words, the device always has access to a physical '''entropy source'''((sfn | Turan | Barker | Kelsey | McKay | 2018 | p=64))), unlike the [[pseudorandom number generator]] (PRNG, a.k.a. "deterministic random bit generator", DRBG) that utilizes a [[deterministic algorithm]]((sfn | Schindler | 2009 | p=7)) and '''non-physical nondeterministic random bit generators''' that do not include hardware dedicated to generation of entropy.((sfn | Turan | Barker | Kelsey | McKay | 2018 | p=64))  

Nature provides ample phenomena that generate low-level, [[statistically random]] "[[noise (signal processing)|noise]]" signals, including [[thermal noise|thermal]] and [[shot noise|shot]] noise, [[jitter]] and [[metastability]] of electronic circuits, [[Brownian motion]], [[atmospheric noise]].((sfn | Sunar | 2009 | p=56)) Researchers also used the [[photoelectric effect]], involving a [[beam splitter]], other [[quantum]] phenomena,((sfn | Herrero-Collantes | Garcia-Escartin | 2017 | p=8))<ref>((cite journal |last1=Jacak |first1=Marcin M. |last2=Jóźwiak |first2=Piotr |last3=Niemczuk |first3=Jakub |last4=Jacak |first4=Janusz E. |title=Quantum generators of random numbers |journal=[[Scientific Reports]] |date=2021 |volume=11 |issue=1 |pages=16108 |doi=10.1038/s41598-021-95388-7 |pmid=34373502 |pmc=8352985 |doi-access=free))</ref><ref>((cite journal |last1=Ma |first1=Xiongfeng |last2=Yuan |first2=Xiao |last3=Cao |first3=Zhu |last4=Qi |first4=Bing |last5=Zhang |first5=Zhen |title=Quantum random number generation |journal=[[npj Quantum Information]] |date=2016 |volume=2 |issue=1 |pages=1–9 |doi=10.1038/npjqi.2016.21 |doi-access=free|arxiv=1510.08957 ))</ref> and even the [[nuclear decay]] (due to practical considerations the latter, as well as the atmospheric noise, is not viable).((sfn | Sunar | 2009 | p=56)) While "classical" (non-quantum) phenomena are not truly random, an unpredictable physical system is usually acceptable as a source of randomness, so the qualifiers "true" and "physical" are used interchangeably.((sfn | Herrero-Collantes | Garcia-Escartin | 2017 | p=4))  

A hardware random number generator is expected to output near-perfect random numbers ("[[full entropy]]").((sfn | Turan | Barker | Kelsey | McKay | 2018 | p=64)) A physical process usually does not have this property, and a practical TRNG typically includes a few blocks:((sfn | Turan | Barker | Kelsey | McKay | 2018 | p=6)) 
* a ''noise source'' that implements the physical process producing the entropy. Usually this process is [[Analog device|analog]], so a ''digitizer'' is used to convert the output of the analog source into a binary representation;
* a ''conditioner'' ([[randomness extractor]]) that improves the quality of the random bits;
* ''health tests''. TRNGs are mostly used in cryptographical algorithms that get completely broken if the random numbers have low entropy, so the testing functionality is usually included.

Hardware random number generators generally produce only a limited number of random bits per second. In order to increase the available output data rate, they are often used to generate the "[[Random seed|seed]]" for a faster PRNG. DRBG also helps with the noise source "anonymization" (whitening out the noise source identifying characteristics) and [[Randomness extractor|entropy extraction]]. With a proper DRBG algorithm selected ([[cryptographically secure pseudorandom number generator]], CSPRNG), the combination can satisfy the requirements of [[Federal Information Processing Standards]] and [[Common Criteria]] standards.((sfn | Saarinen | Newell | Marshall | 2020 | p=))

== Uses ==
((See also|Applications of randomness))
Hardware random number generators can be used in any application that needs randomness. However, in many scientific applications additional cost and complexity of a TRNG (when compared with pseudo random number generators) provide no meaningful benefits. TRNGs have additional drawbacks for data science and statistical applications: impossibility to re-run a series of numbers unless they are stored, reliance on an analog physical entity can obscure the failure of the source. The TRNGs therefore are primarily used in the applications where their unpredictability and the impossibility to re-run the sequence of numbers are crucial to the success of the implementation: in cryptography and gambling machines.((sfn | Templ | 2016 | p=90))    

=== Cryptography ===
The major use for hardware random number generators is in the field of [[data encryption]], for example to create random [[cryptographic key]]s and [[Cryptographic nonce|nonces]] needed to encrypt and sign data. In addition to randomness, there are at least two additional requirements imposed by the cryptographic applications:((sfn | Herrero-Collantes | Garcia-Escartin | 2017 | p=6))
# [[forward secrecy]] guarantees that the knowledge of the past output and internal state of the device should not enable the attacker to predict future data; 
# [[backward secrecy]] protects the "opposite direction": knowledge of the output and internal state in the future should not divulge the preceding data.
A typical way to fulfill these requirements is to use a TRNG to seed a [[cryptographically secure pseudorandom number generator]].((sfn | Herrero-Collantes | Garcia-Escartin | 2017 | p=7))

== History ==
Physical devices were used to generate random numbers for thousands of years, primarily for [[gambling]]. [[Dice]] in particular have been known for more than 5000 years (found on locations in modern Iraq and Iran), and flipping 
a coin (thus producing a random bit) dates at least to the times of [[ancient Rome]].((sfn | L'Ecuyer | 2017 | p=))

The first documented use of a physical random number generator for scientific purposes was by [[Francis Galton]] (1890).<ref>((cite journal |last = Galton |first = Francis |title = Dice for statistical experiments |journal = Nature |date = 1890 |volume = 42 |issue = 1070 |pages = 13–14 |url = http://galton.org/essays/1890-1899/galton-1890-dice.pdf |access-date = 14 May 2014 |doi = 10.1038/042013a0 |bibcode = 1890Natur..42...13G |s2cid = 4038609 |doi-access = free |archive-date = 4 March 2016 |archive-url = https://web.archive.org/web/20160304000756/http://galton.org/essays/1890-1899/galton-1890-dice.pdf |url-status = live ))</ref> He devised a way to sample a [[probability distribution]] using a common gambling dice. In addition to the top digit, Galton also looked at the face of a dice closest to him, thus creating 6*4 = 24 outcomes (about 4.6 bits of randomness).((sfn | L'Ecuyer | 2017 | p=)) 

Kendall and Babington-Smith (1938)<ref>Kendall, M. G., and B. Babington-Smith. 1938. “Randomness and other random sampling numbers”. Journal of the Royal Statistical Society 101:147–166.</ref> used a fast-rotating 10-sector disk that was illuminated by periodic bursts of light. The sampling was done by a human who wrote the number under the light beam onto a pad. The device was utilized to produce a 100,000-digit random number table (at the time such tables were used for statistical experiments, like PRNG nowadays).((sfn | L'Ecuyer | 2017 | p=))

On 29 April 1947, the [[RAND Corporation]] began generating random digits with an "electronic roulette wheel", consisting of a random frequency pulse source of about 100,000 pulses per second gated once per second with a constant frequency pulse and fed into a five-bit binary counter. [[Douglas Aircraft]] built the equipment, implementing Cecil Hasting's suggestion (RAND P-113)<ref>((citation |url = http://www.rand.org/pubs/papers/P113 |publisher = Rand Corporation |title = P-113 |series = Papers |date = January 1949 |last1 = Brown |first1 = George W. |access-date = 2009-05-10 |archive-date = 2007-06-05 |archive-url = https://web.archive.org/web/20070605075201/http://www.rand.org/pubs/papers/P113/ |url-status = live )).</ref> for a noise source (most likely the well known behavior of the 6D4 miniature gas [[thyratron]] tube, when placed in a magnetic field<ref>((citation |title = Electrical Noise Generators |last = Cobine, Curry |journal = Proceedings of the I.R.E. |year = 1947 |issue = September 1947 |pages = 875–9 ))</ref>). Twenty of the 32 possible counter values were mapped onto the 10 decimal digits and the other 12 counter values were discarded.<ref>((citation |url = http://www.rand.org/pubs/monograph_reports/MR1418/index2.html |publisher = Rand Corporation |title = Monograph report |date = January 2001 |access-date = 2009-01-29 |archive-date = 2018-04-15 |archive-url = https://web.archive.org/web/20180415035650/https://www.rand.org/pubs/monograph_reports/MR1418/index2.html |url-status = live )).</ref> The results of a long run from the RAND machine, filtered and tested, were converted into a table, which originally existed only as a deck of [[punched cards]], but was later published in 1955 as a book, 50 rows of 50 digits on each page((sfn | L'Ecuyer | 2017 | p=)) (''[[A Million Random Digits with 100,000 Normal Deviates]]''). The RAND table was a significant breakthrough in delivering random numbers because such a large and carefully prepared table had never before been available. It has been a useful source for simulations, modeling, and for deriving the arbitrary constants in cryptographic algorithms to demonstrate that the constants had not been selected maliciously ("[[nothing up my sleeve numbers]]"). <ref>((cite book |last1 = Schneier |first1 = Bruce |title = Applied Cryptography |publisher = John Wiley & Sons, Inc. |isbn = 978-0-471-11709-4 |page = 423 |edition = Second |chapter = Other Stream Ciphers and Real Random-Sequence Generators |date = 1995-11-01 ))</ref>

Since the early 1950s, research into TRNGs has been highly active, with thousands of research works published and about 2000 patents granted by 2017.((sfn | L'Ecuyer | 2017 | p=))

== Physical phenomena with random properties ==
A lot of different TRNG designs were proposed over time with a large variety of noise sources and digitization techniques ("harvesting"). However, practical considerations (size, power, cost, performance, robustness) dictate the following desirable traits:((sfn | Sunar | 2009 | p=57))
* use of a commonly available inexpensive silicon process;
* exclusive use of digital design techniques. This allows an easier [[system-on-chip]] integration and enables the use of [[FPGA]]s;
* compact and low-power design. This discourages use of analog components (e.g., [[amplifier]]s);
* mathematical justification of the entropy collection mechanisms.

Stipčević & Koç in 2014 classified the physical phenomena used to implement TRNG into four groups:((sfn | Stipčević | Koç | 2014 | p=279))
* electrical noise;
* free-running oscillators;
* chaos;
* quantum effects.

=== Electrical noise-based RNG ===
Noise-based RNGs generally follow the same outline: the source of a [[noise generator]] is fed into a [[comparator]]. If the voltage is above threshold, the comparator output is 1, otherwise 0. The random bit value is latched using a flip-flop. Sources of noise vary and include:((sfn | Stipčević | Koç | 2014 | pp=279-280))
* [[Johnson–Nyquist noise]] ("thermal noise");
* [[Zener diode|Zener noise]];
* [[avalanche breakdown]].
The drawbacks of using noise sources for an RNG design are:((sfn | Stipčević | Koç | 2014 | p=280))
* noise levels are hard to control, they vary with environmental changes and device-to-device;
* calibration processes needed to ensure a guaranteed amount of entropy are time-consuming;
* noise levels are typically low, thus the design requires power-hungry amplifiers. The sensitivity of amplifier inputs enables manipulation by an attacker;
* circuitry located nearby generates a lot of non-random noise thus lowering the entropy;
* a proof of randomness is near-impossible as multiple interacting physical processes are involved.((sfn | Stipčević | Koç | 2014 | p=286))

=== Chaos-based RNG ===
The idea of chaos-based noise stems from the use of a complex system that is hard to characterize by observing its behavior over time. For example, lasers can be put into (undesirable in other applications) chaos mode with chaotically fluctuating power, with power detected using a [[photodiode]] and sampled by a comparator. The design can be quite small, as all [[photonics]] elements can be integrated on-chip. Stipčević & Koç characterize this technique as "most objectionable", mostly due to the fact that chaotic behavior is usually controlled by a differential equation and no new randomness is introduced, thus there is a possibility of the chaos-based TRNG producing a limited subset of possible output strings.((sfn | Stipčević | Koç | 2014 | pp=288-289))

=== Free-running oscillators-based RNG ===
The TRNGs based on a free-running oscillator (FRO) typically utilize one or more [[ring oscillator]]s (ROs), outputs of which are sampled using yet another oscillator. Since [[Inverter (logic gate)|inverters]] forming the RO can be thought of as amplifiers with a very large gain, an FRO output exhibits very fast oscillations in phase in frequency domains. The FRO-based TRNGs are very popular due to their use of the standard digital logic despite issues with randomness proofs and chip-to-chip variability.((sfn | Stipčević | Koç | 2014 | pp=288-289))

=== Quantum-based RNG ===
Quantum random number generation technology is well established with 8 commercial '''quantum random number generator''' ('''QRNG''') products offered before 2017.((sfn | Herrero-Collantes | Garcia-Escartin | 2017 | p=2))

Herrero-Collantes & Garcia-Escartin list the following stochastic processes as "quantum":
* [[nuclear decay]] historically was the earliest quantum method used since the 1960s owing its popularity to the availability of [[Geiger counter]]s and calibrated [[radiation source]]s. The entropy harvesting was done using an event counter that was periodically sampled or a time counter that was sampled at the time of the event. Similar designs were utilized in the 1950s to generate random noise in [[analog computer]]s. The major drawbacks were [[radiation safety]] concerns, low bit rates, and [[Discrete uniform distribution|non-uniform distribution]];((sfn | Herrero-Collantes | Garcia-Escartin | 2017 | pp=10-13))
* [[shot noise]], a quantum mechanical noise source found in electronic circuits, while technically a quantum effect, is hard to isolate from the [[thermal noise]], so, with few exceptions, noise sources utilizing it are only partially quantum and are usually classified as "classical";((sfn | Herrero-Collantes | Garcia-Escartin | 2017 | pp=13-14))
* [[quantum optics]]:
** ''branching path generator'' using a [[beamsplitter]] so that a photon from a [[single-photon source]] randomly takes one of the two paths and sensed by one of the two [[single-photon detector]]s thus generating a random bit;((sfn | Herrero-Collantes | Garcia-Escartin | 2017 | p=15))
** ''time of arrival generator'' and ''photon counting generator'' use a weak photon source, with the entropy harvested similarly to the case of radioactive decay;((sfn | Herrero-Collantes | Garcia-Escartin | 2017 | p=17))
** ''attenuated pulse generators'' is a generalization (simplifying the equipment) of the above methods that allows more than one photon in the system at a time;((sfn | Herrero-Collantes | Garcia-Escartin | 2017 | p=20))  
** ''[[vacuum fluctuations]] generators'' use a laser [[homodyne detection]] to probe the changes in the [[vacuum state]];((sfn | Herrero-Collantes | Garcia-Escartin | 2017 | pp=20-21))
** ''laser phase noise generators'' use the [[phase noise]] on the output of a [[Laser diode|single spatial mode laser]] that is converted to amplitude using an unbalanced [[Mach-Zehnder interferometer]]. The noise is sampled by a photodetector;((sfn | Herrero-Collantes | Garcia-Escartin | 2017 | pp=21-22))  
** [[amplified spontaneous emission]] generators use spontaneous light emission present in the [[optical amplifier]]s as a source of noise;((sfn | Herrero-Collantes | Garcia-Escartin | 2017 | pp=23-24))
** ''[[Raman scattering]] generators'' extract entropy from the interaction of photons with the solid-state materials;((sfn | Herrero-Collantes | Garcia-Escartin | 2017 | pp=24-25)) 
** ''[[optical parametric oscillator]] generator'' uses the [[spontaneous parametric down-conversion]] leading to binary phase state selection in a degenerate optical parametric oscillator;((sfn | Herrero-Collantes | Garcia-Escartin | 2017 | pp=27-28))
To reduce costs and increase robustness of quantum random number generators,<ref>((cite journal | last=Huang | first=Leilei | last2=Zhou | first2=Hongyi | last3=Feng | first3=Kai | last4=Xie | first4=Chongjin | title=Quantum random number cloud platform | journal=npj Quantum Information | publisher=Springer Science and Business Media LLC | volume=7 | issue=1 | date=2021-07-07 | issn=2056-6387 | doi=10.1038/s41534-021-00442-x | page=| doi-access=free ))</ref> online services have been implemented.((sfn | Herrero-Collantes | Garcia-Escartin | 2017 | p=2))

== Performance test ==
The failure of a TRNG can be quite complex and subtle, necessitating validation of not just the results (the output bit stream), but of the unpredictability of the entropy source.((sfn | Herrero-Collantes | Garcia-Escartin | 2017 | p=4)) Hardware random number generators should be constantly monitored for proper operation to protect against the entropy source degradation due to natural causes and deliberate attacks. [[Federal Information Processing Standard|FIPS]] [[FIPS 140|Pub 140-2]] and [[NIST]] Special Publication 800-90B((sfn | Turan | Barker | Kelsey | McKay | 2018 | p=)) define tests which can be used for this. 

The minimal set of real-time tests mandated by the certification bodies is not large; for example, NIST in SP 800-90B requires just two ''continuous health tests'':((sfn | Turan | Barker | Kelsey | McKay | 2018 | pp=25-27))
# ''repetition count test'' checks that the sequences of identical digits are not too long, for a (typical) case of a TRNG that digitizes one bit at a time, this means not having long strings of either 0s or 1s;
# ''adaptive proportion test'' verifies that any random digit does not occur too frequently in the data stream (low [[Bias (statistics)|bias]]). For bit-oriented entropy sources that means that the count of 1s and 0s in the bit stream is approximately the same.

== Problems ==
((sources|section|date=September 2023))
It is very easy to misconstruct hardware or software devices which attempt to generate random numbers. Also, most 'break' silently, often producing decreasingly random numbers as they degrade. Failure modes in such devices are plentiful and are complicated, slow, and hard to detect. Methods that combine multiple sources of entropy are more robust.

Because many entropy sources are often quite fragile, and fail silently, statistical tests on their output should be performed continuously. Many, but not all, such devices incorporate some such tests into the software that reads the device.

=== Attacks ===
((main|Random number generator attack))
Just as with other components of a cryptography system, a cryptographic random number generator should be designed to resist [[random number generator attack|certain attacks]]. Defending against these attacks is difficult without a hardware entropy source.((cn|date=September 2023))

The physical processes in HRNG introduce new attack surfaces. For example, a free-running oscillator-based TRNG can be attacked using a [[frequency injection]].<ref>((cite book | last=Markettos | first=A. Theodore | last2=Moore | first2=Simon W. | title=Lecture Notes in Computer Science | chapter=The Frequency Injection Attack on Ring-Oscillator-Based True Random Number Generators | publisher=Springer Berlin Heidelberg | publication-place=Berlin, Heidelberg | year=2009 | isbn=978-3-642-04137-2 | issn=0302-9743 | doi=10.1007/978-3-642-04138-9_23 | page=317–331 | url=https://www.academia.edu/download/69622562/978-3-642-04138-9_23.pdf))</ref>

=== Estimating entropy ===
((See also|Entropy estimation))

There are mathematical techniques for estimating the [[information entropy|entropy]] of a sequence of symbols. None are so reliable that their estimates can be fully relied upon; there are always assumptions which may be very difficult to confirm. These are useful for determining if there is enough entropy in a seed pool, for example, but they cannot, in general, distinguish between a true random source and a pseudorandom generator. This problem is avoided by the conservative use of hardware entropy sources.

== See also ==
* [[AN/CYZ-9]]
* [[Bell test experiments]]
* [[/dev/random]]
* [[Premium Bond#ERNIE|ERNIE]]
* [[Lavarand]] (a hardware random number generator based on movement of the floating material in lava lamps)
* [[List of random number generators]]
* [[Lottery machine]]
* [[RDRAND]]
* [[Trusted Platform Module]]

== References ==
((Reflist|30em))

== Sources ==
* ((cite report | last1=Turan | first1=Meltem Sönmez | last2=Barker | first2=Elaine | last3=Kelsey | first3=John | last4=McKay | first4=Kerry A | last5=Baish | first5=Mary L | last6=Boyle | first6=Mike | title=NIST SP800-90B: Recommendation for the entropy sources used for random bit generation | publisher=National Institute of Standards and Technology | publication-place=Gaithersburg, MD | year=2018 | doi=10.6028/nist.sp.800-90b| doi-access=free ))
* ((cite book | last=Templ | first=M. | title=Simulation for Data Science with R | publisher=Packt Publishing | year=2016 | isbn=978-1-78588-587-7 | url=https://books.google.com/books?id=EAJwDQAAQBAJ&pg=PA90 | access-date=2023-08-07))
* ((cite conference | last1=Saarinen | first1=Markku-Juhani O. | last2=Newell | first2=G. Richard | last3=Marshall | first3=Ben | title=Building a Modern TRNG: An Entropy Source Interface for RISC-V | publisher=ACM | publication-place=New York, NY, USA | date=2020-11-09 | doi=10.1145/3411504.3421212 | url=https://web.archive.org/web/20210316160110id_/https://eprint.iacr.org/2020/866.pdf))
* ((cite book | last=Schindler | first=Werner | pages=5–23 | title=Cryptographic Engineering | chapter=Random Number Generators for Cryptographic Applications | publisher=Springer US | publication-place=Boston, MA | year=2009 | isbn=978-0-387-71816-3 | doi=10.1007/978-0-387-71817-0_2))
* ((cite book | last=Sunar | first=Berk | pages=55–73 | title=Cryptographic Engineering | chapter=True Random Number Generators for Cryptography | publisher=Springer US | publication-place=Boston, MA | year=2009 | isbn=978-0-387-71816-3 | doi=10.1007/978-0-387-71817-0_4))
* ((cite conference | conference = 2017 Winter Simulation Conference (WSC) | last=L'Ecuyer | first=Pierre | title=History of uniform random number generation | publisher=IEEE | year=2017 | doi=10.1109/wsc.2017.8247790 | location =  Las Vegas, NV, USA | isbn=978-1-5386-3428-8 | issn = 1558-4305 | url = https://inria.hal.science/hal-01561551/file/wsc17rng-history-report.pdf))
* ((cite book | last1=Stipčević | first1=Mario | last2=Koç | first2=Çetin Kaya | pages=275–315 | title=Open Problems in Mathematics and Computational Science | chapter=True Random Number Generators | publisher=Springer International Publishing | publication-place=Cham | year=2014 | isbn=978-3-319-10682-3 | doi=10.1007/978-3-319-10683-0_12 | url=https://cetinkayakoc.net/docs/b08.pdf))
* ((cite journal | last=Herrero-Collantes | first=Miguel | last2=Garcia-Escartin | first2=Juan Carlos | title=Quantum random number generators | journal=[[Reviews of Modern Physics]] | publisher=[[American Physical Society]] (APS) | volume=89 | issue=1 | date=2017-02-22 | issn=0034-6861 | doi=10.1103/revmodphys.89.015004 | arxiv = 1604.03304 | url = https://arxiv.org/pdf/1604.03304.pdf | pages = arXiv page numbers used | no-pp = yes))
* ((cite book |title=Quantum Random Number Generation: Theory and Practice |date=2020 |publisher=[[Springer, Cham|Springer Cham]] |isbn=978-3-319-72596-3 |url=https://link.springer.com/book/10.1007/978-3-319-72596-3))

=== General references ===
((Refbegin))
* ((citation |last = Brown |first = George W |title = History of Rand's Million Digits |date = June 1949 |url = http://www.rand.org/pubs/papers/P113 |series = papers |issue = P-113 |publisher = [[RAND Corporation]] |access-date = 2009-05-10 |archive-date = 2007-06-05 |archive-url = https://web.archive.org/web/20070605075201/http://www.rand.org/pubs/papers/P113/ |url-status = live ))
* ((citation |last = Brown |first = Bernice |title = Some Tests of the Randomness of a Million Digits |date = October 1948 |url = http://www.rand.org/pubs/papers/P44 |series = Papers |issue = P-44 |publisher = [[RAND Corporation]] |access-date = 2009-05-10 |archive-date = 2007-06-05 |archive-url = https://web.archive.org/web/20070605075822/http://www.rand.org/pubs/papers/P44/ |url-status = live ))
* ((citation |title = Electron Tube Data handbook |year = 1957 |chapter = Tube type 6D4 |publisher = Sylvania ))
* ((citation |title = A Million Random Digits with 100,000 Normal Deviates |date = January 2001 |url = http://www.rand.org/publications/classics/randomdigits/ |publisher = [[RAND Corporation]] |access-date = 2002-12-22 |archive-date = 2002-12-16 |archive-url = https://web.archive.org/web/20021216191633/http://www.rand.org/publications/classics/randomdigits/ |url-status = live )).
* ((citation |last = Galton |first = Francis |title = Dice for statistical experiments |url = http://www.mugu.com/galton/statistician.html |year = 1890 |author-link = Francis Galton |journal = Nature |volume = 42 |issue = 1070 |pages = 13–4 |doi = 10.1038/042013a0 |bibcode = 1890Natur..42...13G |doi-access = free |access-date = 2004-03-28 |archive-date = 2004-04-04 |archive-url = https://web.archive.org/web/20040404063102/http://www.mugu.com/galton/statistician.html |url-status = live ))
* ((citation |title = Randomness and Genuine Random Number Generator With Self-testing Functions |url = https://geant4.web.cern.ch/geant4/results/papers/QMD-MC2010.pdf |location = Japan |publisher = LE Tech RNG |access-date = 2015-04-20 |archive-date = 2018-03-01 |archive-url = https://web.archive.org/web/20180301104756/https://geant4.web.cern.ch/geant4/results/papers/QMD-MC2010.pdf |url-status = dead ))
((Refend))

==External links==
* ((citation |url = http://www.ietf.org/rfc/rfc4086.txt |title = RFC 4086 on Randomness Recommendations for Security (replaces earlier RFC 1750) |publisher = IETF )).
* ((citation |url = http://www.cryptography.com/public/pdf/IntelRNG.pdf |publisher = [[Intel]] |title = The Intel Random Number Generator |date = 22 April 1999 )).
* [https://www.protegost.com/ ProtegoST SG100], ProtegoST, "Hardware Random Number Generator "Based on quantum physics random number source from a zener diode".

((DEFAULTSORT:Hardware Random Number Generator))
[[Category:Cryptography]]
[[Category:Random number generation]]
[[Category:Computer peripherals]]

[[de:Zufallszahlengenerator#Physikalischer Zufallszahlengenerator]]

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