True Random Number Generator with a Metastability-Based Quality Control

Tokunaga,; Blaauw,; Mudge,

doi:10.1109/isscc.2007.373465

Cited by 80 publications

(58 citation statements)

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“…In [12], a controller circuit calibrates the phase of the clock signal generated from a signal generator based on the quality of randomness in the bits generated. C. Tokunaga et al [7] inject charge into the meta-stable circuit to counter any mismatch in the devices. The charge injection is done through an array of capacitors that are conditionally charged based on the amount of bias that needs to be corrected.…”

Section: Bias Removal Techniquesmentioning

confidence: 99%

“…C. Tokunaga et al . [7] have proposed a meta-stability based TRNG which does not directly extract the random bits, but estimates the random noise present in the circuit using the resolution time for the meta-stable element to reach stability. Memory cells also provide a means to generate randomness.…”

Section: Introductionmentioning

confidence: 99%

“…The counter measures include digital post-processing using the XOR function [2,3,12,13], Von Neumann entropy extraction [1,2,6] and hash extractors like universal hash function [8] or Secure Hash Algorithm (SHA-1) [6]. Circuit design techniques like charge injection [7] and configurable devices [10] are also used to tune the TRNG against bias. Each of these techniques provides varied degree of improvement in the randomness of the generated bits at different energy overhead.…”

Section: Introductionmentioning

confidence: 99%

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Entropy extraction in metastability-based TRNG

Suresh¹,

Burleson²

2010

2010 IEEE International Symposium on Hardware-Oriented Security and Trust (HOST)

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Abstract-True Random Number Generators (TRNG) implemented in deep sub micron (DSM) technologies become biased in bit generation due to process variations and fluctuations in operating conditions. A variety of mechanisms ranging from analog and digital circuit techniques to algorithmic postprocessing can be employed to remove bias. In this work we compare the effectiveness of digital post-processing using the XOR function and Von Neumann Corrector with circuit calibration technique for a meta-stability based reference TRNG design. The energy consumption per random bit is used as the metric for comparison of the different techniques. The results indicate that the calibration technique is effective for 12% larger process variation than the XOR function and extracts entropy comparable to the Von Neumann Corrector at 56% lesser energy/bit. The analysis thereby demonstrates that circuit calibration provides an efficient tradeoff between entropy and energy/bit for removing bias in lightweight TRNG.

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Section: Bias Removal Techniquesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Entropy extraction in metastability-based TRNG

Suresh¹,

Burleson²

2010

2010 IEEE International Symposium on Hardware-Oriented Security and Trust (HOST)

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“…For example, a clocked comparator can generate '0', '1' digital bits. The time-to-digit converter (TDC) method [3] and the oscillator sampling method [2] can also deal with a chaotic signal. However, the above methods can't make full use of the statistics of a chaotic signal which are more stable and controllable than those of noises.…”

Section: Random Number Generationmentioning

confidence: 99%

“…One intuitive method is to amplify and sample devices' noises such as thermal noise [1], but its disadvantage is that the circuits are usually unable to shield noises from supply and substrate. Some papers employ the oscillator sampling method [2] or the meta-stability method [3], but the randomness in these generators is also brought by devices' noises.…”

Section: Introductionmentioning

confidence: 99%

Random numbers from an integrated CMOS double-scroll

Cao

2010

IEICE Electron. Express

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A double-scroll chaotic oscillator based true random number generator (TRNG) is designed and fabricated in CMOS 0.18 μm process. In order to realize a TRNG suitable for CMOS process, an op-amp based chaotic oscillator is chosen and transistor-level op-amps are designed. Then, a symmetrically dividing method is proposed to exploit the statistics of chaotic signals generated by the oscillator and improve the throughput of random bits. The TRNG core occupies 0.026 mm 2 and dissipates 1 mW. Measurements show that the generated binary bits can pass the SP800-22 test suites and FIPS140-1 standard with a throughput of 20 Mbps.

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A 0.012‐mm² 0.244‐pJ/bit successive approximation register analog‐to‐digital converter‐based true random number generator for Internet of Things applications in a 65‐nm complementary metal–oxide–semiconductor

Cheng,

Chen,

Stefano

et al. 2024

Circuit Theory & Apps

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SummaryThis paper proposes a true random number generator (TRNG) based on a successive approximation register (SAR) analog‐to‐digital converter (ADC). A top‐plate sampling SAR ADC structure and a new MSB‐isolation switch scheme (MISS) are combined to achieve low‐power operation and small area occupancy at the same time. When the SAR ADC has completed its conversion, a secondary firing of the comparator measures the remaining signal. This 1‐bit quantized residue serves as the true random sequence, thus eliminating the need for extra circuitry in the TRNG. A 65‐nm prototype of the proposed TRNG was found to occupy an area of 0.012 mm2 and successfully passed all the National Institute of Standards and Technology (NIST) tests without post‐processing. It achieved a figure‐of‐merit of 0.244 pJ/bit for a 1 Mb/s random sequence with a 0.8‐V power supply. Its resilience to power noise injection attacks was also verified.

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True Random Number Generator with a Metastability-Based Quality Control

Cited by 80 publications

References 1 publication

Entropy extraction in metastability-based TRNG

Entropy extraction in metastability-based TRNG

Random numbers from an integrated CMOS double-scroll

A 0.012‐mm² 0.244‐pJ/bit successive approximation register analog‐to‐digital converter‐based true random number generator for Internet of Things applications in a 65‐nm complementary metal–oxide–semiconductor

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True Random Number Generator with a Metastability-Based Quality Control

Cited by 80 publications

References 1 publication

Entropy extraction in metastability-based TRNG

Entropy extraction in metastability-based TRNG

Random numbers from an integrated CMOS double-scroll

A 0.012‐mm2 0.244‐pJ/bit successive approximation register analog‐to‐digital converter‐based true random number generator for Internet of Things applications in a 65‐nm complementary metal–oxide–semiconductor

Contact Info

Product

Resources

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A 0.012‐mm² 0.244‐pJ/bit successive approximation register analog‐to‐digital converter‐based true random number generator for Internet of Things applications in a 65‐nm complementary metal–oxide–semiconductor