16.3 A 23Mb/s 23pJ/b fully synthesized true-random-number generator in 28nm and 65nm CMOS

Yang, Kaiyuan; Fick, David; Henry, M.; Lee, Yoonmyung; Blaauw, David; Sylvester, Dennis

doi:10.1109/isscc.2014.6757434

Cited by 94 publications

(89 citation statements)

References 5 publications

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“…The lowest energy solution today is to use jitter as a way to efficiently amplify the noise present in CMOS ring oscillators. The most energy efficient implementation 28 requires 23pJ/bit and 375µm 2 .…”

Section: Introductionmentioning

confidence: 99%

Low-Energy Truly Random Number Generation with Superparamagnetic Tunnel Junctions for Unconventional Computing

et al. 2017

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Low-energy random number generation is critical for many emerging computing schemes proposed to complement or replace von Neumann architectures. However, current random number generators are always associated with an energy cost that is prohibitive for these computing schemes. In this paper, we introduce random number bit generation based on specific nanodevices: superparamagnetic tunnel junctions. We experimentally demonstrate high quality random bit generation that represents orders-of-magnitude improvements in energy efficiency compared to current solutions. We show that the random generation speed improves with nanodevice scaling, and investigate the impact of temperature, magnetic field and crosstalk. Finally, we show how alternative computing schemes can be implemented using superparamagentic tunnel junctions as random number generators. These results open the way for fabricating efficient hardware computing devices leveraging stochasticity, and highlight a novel use for emerging nanodevices.

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Section: Introductionmentioning

confidence: 99%

Low-Energy Truly Random Number Generation with Superparamagnetic Tunnel Junctions for Unconventional Computing

et al. 2017

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“…As indicated by (8) and (9), the distribution of collapse time depends on the relative magnitude of systematic mismatch and random jitter. If systematic mismatch is small, noise will have a more significant impact, resulting in a longer collapse time with wider distribution.…”

Section: B Systematic Mismatch Versus Random Jittermentioning

confidence: 94%

“…Following the analysis in [15], supply noise adds correlated delay variations to all inverters in RO and can be viewed as an additional correlated variation to the ΔDelay i,A and ΔDelay i,B terms in (1) and (2). However, since the supply variation is common for all stages, variation in the delay difference between the two edges is not significant, which results in small fluctuations in the mean value of collapse time in (8). Despite the modulating of average cycles to collapse by supply noise, the cycles to collapse of a given run still follows inverse Gaussian distribution caused by thermal noise.…”

Section: A Analytical Model Of Frequency Collapse In An Even-stage Romentioning

confidence: 99%

“…Hence, RO-based TRNGs have previously employed an odd-stage number RO where mismatch naturally cancels out [8]. However, we will show in Section III-A that, in fact, the process variation in an even-stage RO can be used as a natural source of tunability to enable a highly adaptive TRNG design that is robust to a wide range of environmental and other factors using an automatic tuning loop.…”

Section: B Systematic Mismatch Versus Random Jittermentioning

confidence: 99%

“…Efforts to increase entropy of RO-based TRNG include combining outputs of several parallel ROs [7], chaotic ROs with multiple feedback paths (FIRO and GARO) [13], and including a dynamic duty cycle tuning loop to remove bias in outputs [10]. Recent RO-based TRNGs employ new random bit extraction schemes like measuring time for a thirdharmonic RO to collapse to fundamental frequency [8] and beat frequency between two ROs running at close frequencies [9]. These new schemes provide better randomness and performance thanks to the new jitter amplification approaches, but robustness was not verified across PVT conditions and could pose difficulties to their applications.…”

Section: Introductionmentioning

confidence: 99%

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An All-Digital Edge Racing True Random Number Generator Robust Against PVT Variations

Yang

Blaauw

Sylvester

2016

IEEE J. Solid-State Circuits

113

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This paper presents an all-digital true random number generator (TRNG) harvesting entropy from the collapse of two edges injected into one even-stage ring, fabricated in 40 and 180 nm CMOS technologies. A configurable ring and tuning loop provides robustness across a wide range of temperature (−40 • C to 120 • C), voltage (0.6 to 0.9 V), process variation, and external attack. The dynamic tuning loop automatically configures the ring to meet a sufficient collapse time, thereby maximizing entropy. Measured random bits pass all NIST randomness tests across all measured operating conditions and power supply attacks. In 40 nm, the TRNG occupies only 836 µm 2 and consumes 23 pJ/bit at nominal 0.9 V and 11 pJ/bit at 0.6 V.

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Threshold Switching of Ag or Cu in Dielectrics: Materials, Mechanism, and Applications

Wang

Rao

Midya

et al. 2017

Adv Funct Materials

293

233

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Threshold switches with Ag or Cu active metal species are volatile memristors (also termed diffusive memristors) featuring spontaneous rupture of conduction channels. The temporal dynamics of the conductance evolution is closely related to the electrochemical and diffusive dynamics of the active metals which could be modulated by electric field strength, biasing duration, temperature, and so on. Microscopic pictures by electron microscopy and quantitative thermodynamics modeling are examined to give insights into the underlying physics of the switching. Depending on the time scale of the relaxation process, such devices find a variety of novel applications in electronics, ranging from selector devices for memories to synaptic devices for neuromorphic computing.is applied due to the formation of a conduction channel(s) with Ag or Cu atoms. Unlike the ECM cells, the resistance recovers back spontaneously upon cessation of the external bias, yielding a superior I-V nonlinearity [4][5][6][7][8][9][10][11] and unique temporal conductance evolution dynamics. [7,12,13] Such a relaxation process is due to the physical dissolution of the metallic conduction channel under driving forces such as minimization of interfacial energy. In case active metals are used as electrodes, these metals may be doped into the dielectrics eventually under the combined effect of electric fields, thermal diffusion, which may lead to a reduced threshold voltage for the subsequent switching, similar to the process called "electroforming." The unique delay and relaxation dynamics of Ag and Cu-based threshold switches make them suitable for innovative applications in circuits and systems.Threshold switches with Ag or Cu active metals are also termed as "diffusive memristors" [7] to emphasize the underlying nature of the diffusive dynamics of the metal species. Factors including bias amplitude, biasing duration, as well as ambient temperature have been observed to have an impact on such a process, showing a wide range of dynamical properties, which could be exploited as access devices for memories with fast transition (e.g., <100 ns) or synaptic emulators with a relatively slower evolution (e.g., >1 µs). We survey the recently developed material systems which have exhibited this kind of threshold switching. New evidences by electron microscopy and quantitatively thermodynamic modeling are examined to give insights into the underlying physics of the mechanisms. We also discuss applications enabled by the advent of such threshold switches. Temporal Response of the SwitchingThe dynamical response of threshold switching is a critical property for many applications but has been characterized to a lesser extent. The temporal responses could be probed by applying voltage pulses and measuring the resulting currents in the time domain. It is a general observation in both ECM and threshold switches that the conductance experienced a transition from insulating state to conducting state after a finite time duration (delay time) under the external bias, as ill...

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16.3 A 23Mb/s 23pJ/b fully synthesized true-random-number generator in 28nm and 65nm CMOS

Cited by 94 publications

References 5 publications

Low-Energy Truly Random Number Generation with Superparamagnetic Tunnel Junctions for Unconventional Computing

Low-Energy Truly Random Number Generation with Superparamagnetic Tunnel Junctions for Unconventional Computing

An All-Digital Edge Racing True Random Number Generator Robust Against PVT Variations

Threshold Switching of Ag or Cu in Dielectrics: Materials, Mechanism, and Applications

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