True random number generator using current difference based on a fractional stochastic model in 40-nm embedded ReRAM

Wei, Zhiqiang; Katoh, Y.; Ogasahara, Shin; Yoshimoto, Yuma; Kawai, K.; Ikeda, Y.; Eriguchi, Koji; Ohmori, Kenji; Yoneda, Shigeo

doi:10.1109/iedm.2016.7838349

Cited by 71 publications

(47 citation statements)

References 6 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…It has been demonstrated that the high unpredictability of hardware-based TRNG makes them more reliable with respect to software-based PRNG systems [26,27]. In recent years, various physical entropy sources were proposed for TRNG, like the random telegraph noise (RTN) in dielectrics [28,29] , stochastic quantum processes [25], stochastic spintronic phenomena [30,31] and memristive transport and switching [32,33,34]. Several stochastic entropy sources were identified in both CMOS technologies and emerging memristive devices.…”

Section: Prng and Trngmentioning

confidence: 99%

“…Most recently, to compensate for the area occupation and other issues of the TRNG circuit of Fig. 5, the current difference in the 1/f β noise of the RRAM device was used as the entropy source [32]. The RRAM noise is associated to multi-trap capture and emission events in defects (e.g.…”

Section: Trng Based On Stochastic Noisementioning

confidence: 99%

“…The 1/f noise can be harvested for TRNG by the circuit shown in Fig. 7 [32]. Here, the noisy current is sampled at subsequent times t and t+∆t, then the two sampled currents are subtracted leading and the difference ∆I is compared to 0.…”

Section: Trng Based On Stochastic Noisementioning

confidence: 99%

See 2 more Smart Citations

Applications of Resistive Switching Memory as Hardware Security Primitive

Carboni

Ielmini

2019

Applications of Emerging Memory Technology

View full text Add to dashboard Cite

With the widespread diffusion of ubiquitous mobile computing and internet of things (IoT), secured communication and chip authentication become key requirements. Hardware-based security concepts generally provide the best performance in terms of good security standard, low power consumption, and large area density. In these concepts, the stochastic properties of the device, such as the physical and geometrical variations of the process, are harnessed to generate random bits and functions. This is the basis for the true-random number generator (TRNG), where true random numbers are generated by exploiting the physics and randomness of nanoscale devices. The same random variations can also be used to implement physical unclonable function (PUF) for the authentication of individual hardware chips. Emerging memory devices rely on unique physical mechanisms for transport and switching, thus appear as the ideal source of entropy for hardware TRNG and PUF. These novel memory concepts include resistive switching memory (RRAM), phase change memory (PCM) and spin-transfer torque magnetic memory (STT-MRAM) devices. As these devices are increasingly adopted as memory and computing elements in several applications, exploiting their intrinsic stochastic variations for TRNG and PUF becomes an attractive solution for low cost, high performance security primitives. This chapter provides an overview of TRNG and PUF adopting emerging memory devices as the fundamental entropy source. TRNG concepts are classified by the microscopic stochastic variation that is adopted as entropy source, namely, current noise, switching delay time, or switching voltage. While most TRNG concepts rely on RRAM devices, we also show novel concepts using STT-MRAM devices which take advantage of the excellent endurance and speed of switching. The TRNG schemes are discussed in terms of the simplicity of the design, e.g., the ability to generate random bits without a probability tracking by adopting a

show abstract

Section: Prng and Trngmentioning

confidence: 99%

Section: Trng Based On Stochastic Noisementioning

confidence: 99%

See 1 more Smart Citation

Applications of Resistive Switching Memory as Hardware Security Primitive

Carboni

Ielmini

2019

Applications of Emerging Memory Technology

View full text Add to dashboard Cite

show abstract

“…In memory applications, these fluctuations can cause severe operation or read errors. On the other hand, this RTN fluctuations in resistive RAMs can be used as an entropy source of random number generators [33][34][35] . [36][37][38][39] .…”

Section: Introductionmentioning

confidence: 99%

New method to analyze random telegraph signals in resistive random access memories

González-Cordero

González

Jiménez-Molinos

et al. 2019

Journal of Vacuum Science &Amp; Technology B, Nanotechnology and Microelectronics: Materials, Processing, Measurement, and Phen

View full text Add to dashboard Cite

A new technique to analyze Random Telegraph Noise (RTN) is proposed. It has been used for the analysis of current versus time measurements performed on Ni/HfO2/Si-n+-based 2 resistive random access memories (RRAMs). The method allows to study current-time traces with a massive number of data without losing the capability of dealing with background noise and discriminating the active defects responsible for current fluctuations. A comparison of this algorithm with previous ones is given in terms of computing time and RTN description accuracy. The computing efficiency and the validity of the model have been proved and, therefore, it is feasible to propose applications for real time analysis making use of this new algorithm.

show abstract

“…Literatürde entropi kaynağı olarak elektronik devrelerde termal ve shot gürültüsü [1], jitter ve metastability [2][3][4], Brownian Motion [5], atmosferik gürültü ve nükleer bozulma [6] kullanılmıştır. Bunların yanı sıra ses, video, EEG, ECG, Mouse hareketleri gibi insan kaynaklı gürültü kaynaklarından SRSÜ ve GRSÜ tabanlı üreteçler gerçekleştirilmiştir.…”

Section: Introductionunclassified

İnsan Hareketleri Tabanlı Gerçek Rasgele Sayı Üretimi

Tuncer

Genc²

2019

Bitlis Eren Üniversitesi Fen Bilimleri Dergisi

View full text Add to dashboard Cite

Gerçek rasgele sayı üreteci (GRSÜ) ile rasgele sayı üretmek için deterministik olmayan bir gürültü kaynağından yararlanılır. Rasgelelik derecesinin daha yüksek olması nedeniyle GRSÜ, Sözde rasgele sayı üreticisinden daha güvenli sayı üretir. Bu makalede, insan hareketleri ile rasgele sayı üreten bir GRSÜ öneriyoruz. Önerilen GRSÜ, hemen hemen tüm insanların kullandığı mobil telefonlardaki ivme ve GPS sensörlerini kullanmaktadır. İlk olarak, mobil telefonu taşıyan kişinin 3-D ortamdaki hareketleri sonucunda ivme ve konum değişimleri android tabanlı bir mobil cihazdan örneklenerek elde edilmiştir. Daha sonra, elde edilen bu işaretler, normalizasyon işlemi uygulanarak ham sayı dizilerine dönüştürülmüştür. Son olarak, sayı dizilerinin istatistiksel özelliklerini iyileştirmek için XOR son işlemi uygulanmış ve rasgele sayı üretimi gerçekleştirilmiştir. Kişi yürürken, koşarken ve stabil konumda iken sensörlerden elde edilen toplamda 15 veri seti oluşturulmuştur. Sayıların istatistiksel özellikleri NIST test süiti, Skala İndeks ve otokorelasyon ile incelenmiştir. Önerilen GRSÜ, cep telefonu platformu için uygun, evrensel ve düşük maliyetli olup kişiye özgü rasgele sayı üretmektedir.

show abstract

True random number generator using current difference based on a fractional stochastic model in 40-nm embedded ReRAM

Cited by 71 publications

References 6 publications

Applications of Resistive Switching Memory as Hardware Security Primitive

Applications of Resistive Switching Memory as Hardware Security Primitive

New method to analyze random telegraph signals in resistive random access memories

İnsan Hareketleri Tabanlı Gerçek Rasgele Sayı Üretimi

Contact Info

Product

Resources

About