Unified Analog PUF and TRNG Based on Current-Steering DAC and VCO

Danesh, Mohammadhadi; Venkatasubramaniyan, Aishwarya Bahudhanam; Kapoor, Gaurav; Ramesh, Naveen; Sadasivuni, Sudarsan; Chandrasekaran, Sanjeev Tannirkulam; Sanyal, Arindam

doi:10.1109/tvlsi.2020.3011648

Cited by 26 publications

(10 citation statements)

References 29 publications

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“…Also, the difficulty of external attacks is reduced. The analog-TRNG uses an entropy source based on the noise presented in some analog circuits [13]- [16]. However, an analog/time to digital converter is necessary to digitize the entropy, increasing the area of the TRNG.…”

Section: Introductionmentioning

confidence: 99%

A Robust and Healthy Against PVT Variations TRNG Based on Frequency Collapse

et al. 2022

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True Random Number Generator (TRNG) is used in many applications, generally for generating random cryptography keys. In this way, the trust of the cryptography system depends on the quality of the random numbers generated. However, the entropy fluctuations produced by external perturbations generate some false positives in the random sequence. These false positives can generate a disastrous scenario, depending on the application. This work presents the results of different tests to demonstrate the robustness and health of the TRNG based on frequency collapse. The TRNG passed all entropy tests provided for NIST SP800-90B and AIS31. The entropy test denotes a 0.9789 minimum entropy normalized and 7.998 Shannon entropy. In addition, the TRNG passes the health tests provided for NIST SP800-90B. The health test shows a number of identical values I v = 0%, I v−1 < 0.004% and a maximum cutoff value M C v = 10 with LM C v = 13 in the repetition count and adaptive proportion tests, respectively. The implementation passed all the statistical tests provided for NIST SP800-22 and AIS20. Besides, the implementation passes the different tests with Process, Voltage, and Temperature (PVT) variations. The TRNG is implemented in a 0.18µm General Purpose (GP) CMOS technology, occupying 25600µm 2 with four entropy sources. Finally, the implementation presents a 7.3 until 9.2-Mb/s of bit rate, 0.56 until 1.88-mW of power consumption, and 77.2 until 204.3-pJ/bit of energy per bit using an entropy source with 16 and 2 delay stages, respectively.

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Section: Introductionmentioning

confidence: 99%

A Robust and Healthy Against PVT Variations TRNG Based on Frequency Collapse

et al. 2022

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“…Furthermore, the constrained resources in IoT devices have led to the unified designs of secure cores, such as ADC-based TRNG/PUF and SRAM-based TRNG/PUF. There are a few PUF and TRNG unified designs reported in the literature [ 5 , 113 , 114 , 115 ] which is a new trend in entropy harvest designs.…”

Section: Trng-puf United Designmentioning

confidence: 99%

“…In [ 5 ], the PUF and TRNG are implemented by analog current-steering DAC and voltage-controlled-oscillator (VCO) devices, shown in Figure 25 .…”

Section: Trng-puf United Designmentioning

confidence: 99%

“…A review of silicon-based TRNG/PUF designs is shown in Table 4 . Among all of these designs [ 5 , 113 , 114 , 115 ], the unified implementations of TRNG and PUF demonstrated to reduce the integration effort, cost, and area.…”

Section: Trng-puf United Designmentioning

confidence: 99%

“…There are two types of entropy source, dynamic and static entropy source, which can be generated from silicon chips. Dynamic entropy sources that provide true randomness are usually extracted from the indeterminate physical processes, such as the jitter of ring oscillators (RO) [ 3 , 4 ] and thermal noise from the digital-to-analog converter (DAC) [ 5 ], or unpredictable events, such as the human-driven timing of mouse movements and keyboard strokes [ 6 ]. Because the indeterminate physical processes are completely determined by the dynamic parameters of the system, this type of entropy is categorized as dynamic entropy.…”

Section: Introductionmentioning

confidence: 99%

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Entropy Sources Based on Silicon Chips: True Random Number Generator and Physical Unclonable Function

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Liu

Lan

et al. 2022

Entropy

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Entropy is a measure of uncertainty or randomness. It is the foundation for almost all cryptographic systems. True random number generators (TRNGs) and physical unclonable functions (PUFs) are the silicon primitives to respectively harvest dynamic and static entropy to generate random bit streams. In this survey paper, we present a systematic and comprehensive review of different state-of-the-art methods to harvest entropy from silicon-based devices, including the implementations, applications, and the security of the designs. Furthermore, we conclude the trends of the entropy source design to point out the current spots of entropy harvesting.

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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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Unified Analog PUF and TRNG Based on Current-Steering DAC and VCO

Cited by 26 publications

References 29 publications

A Robust and Healthy Against PVT Variations TRNG Based on Frequency Collapse

A Robust and Healthy Against PVT Variations TRNG Based on Frequency Collapse

Entropy Sources Based on Silicon Chips: True Random Number Generator and Physical Unclonable Function

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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Unified Analog PUF and TRNG Based on Current-Steering DAC and VCO

Cited by 26 publications

References 29 publications

A Robust and Healthy Against PVT Variations TRNG Based on Frequency Collapse

A Robust and Healthy Against PVT Variations TRNG Based on Frequency Collapse

Entropy Sources Based on Silicon Chips: True Random Number Generator and Physical Unclonable Function

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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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