Variation-Resilient True Random Number Generators Based on Multiple STT-MTJs

In the Internet of Things era, security concerns may require a cryptography system in every connected device. True random number generators (TRNGs) are preferred instead of pseudorandom number generators in the cryptography systems to achieve a higher level of security. For on-chip applications, we seek scalable and CMOS-compatible devices and designs for TRNGs. In this paper, the stochastic behavior of the spin transfer torque magnetic tunnel junction (STT-MTJ) is utilized for the source of randomness. However, variations and correlations exist in MTJs due to fabrication limitations, so TRNG designs based on a single MTJ have to be postprocessed or tracked in real time to ensure an acceptable level of randomness. Two novel designs are proposed in this paper, which can produce random sequences with high variation resilience. The first design uses a parallel structure to minimize variation effects, and the second design leverages the symmetry of an MTJ pair to take advantage of any correlations. Moreover, a universal circuit for quality improvement is proposed and it can be used with any random number generator. All of the designs are validated in a 28-nm CMOS process by Monte Carlo simulation with a compact model of the MTJ. The National Institute of Standards and Technology (NIST) statistical test suite is used to test the randomness quality of the generated sequences under the scenario of encryption keys in the transport layer security or secure sockets layer (TLS/SSL) cryptographic protocol.

[1]  A. Fert,et al.  Current-induced magnetization switching in atom-thick tungsten engineered perpendicular magnetic tunnel junctions with large tunnel magnetoresistance , 2017, Nature Communications.

[2]  John P. Hayes,et al.  Survey of Stochastic Computing , 2013, TECS.

[3]  J. Nowak,et al.  Spin torque switching of perpendicular Ta∣CoFeB∣MgO-based magnetic tunnel junctions , 2011 .

[4]  Yu Wang,et al.  Energy-efficient neuromorphic computation based on compound spin synapse with stochastic learning , 2015, 2015 IEEE International Symposium on Circuits and Systems (ISCAS).

[5]  David Blaauw,et al.  16.3 A 23Mb/s 23pJ/b fully synthesized true-random-number generator in 28nm and 65nm CMOS , 2014, 2014 IEEE International Solid-State Circuits Conference Digest of Technical Papers (ISSCC).

[6]  Zhengya Zhang,et al.  A Native Stochastic Computing Architecture Enabled by Memristors , 2014, IEEE Transactions on Nanotechnology.

[7]  Zheng Li,et al.  Variation-Tolerant and Disturbance-Free Sensing Circuit for Deep Nanometer STT-MRAM , 2014, IEEE Transactions on Nanotechnology.

[8]  Baoqin Chen,et al.  Proximity effect in electron beam lithography , 2004, Proceedings. 7th International Conference on Solid-State and Integrated Circuits Technology, 2004..

[9]  G. Kar,et al.  Solving the BEOL compatibility challenge of top-pinned magnetic tunnel junction stacks , 2017, 2017 IEEE International Electron Devices Meeting (IEDM).

[10]  Chris H. Kim,et al.  A Magnetic Tunnel Junction based True Random Number Generator with conditional perturb and real-time output probability tracking , 2014, 2014 IEEE International Electron Devices Meeting.

[11]  Hiroshi Imamura,et al.  Spin dice: A scalable truly random number generator based on spintronics , 2014 .

[12]  Himanshu Kaul,et al.  2.4 Gbps, 7 mW All-Digital PVT-Variation Tolerant True Random Number Generator for 45 nm CMOS High-Performance Microprocessors , 2012, IEEE Journal of Solid-State Circuits.

[13]  Wolfhard Möller,et al.  Lateral variation of target poisoning during reactive magnetron sputtering , 2007 .

[14]  Takayuki Konishi,et al.  Design of an STT-MTJ based true random number generator using digitally controlled probability-locked loop , 2015, 2015 IEEE 13th International New Circuits and Systems Conference (NEWCAS).

[15]  Hao Chen,et al.  A Stochastic Computational Approach for Accurate and Efficient Reliability Evaluation , 2014, IEEE Transactions on Computers.

[16]  Donald E. Eastlake,et al.  Randomness Recommendations for Security , 1994, RFC.

[17]  D. Edelstein,et al.  Key parameters affecting STT-MRAM switching efficiency and improved device performance of 400°C-compatible p-MTJs , 2017, 2017 IEEE International Electron Devices Meeting (IEDM).

[18]  Lirida A. B. Naviner,et al.  A novel circuit design of true random number generator using magnetic tunnel junction , 2016, 2016 IEEE/ACM International Symposium on Nanoscale Architectures (NANOARCH).

[19]  Karsten Rott,et al.  Tunneling magnetoresistance of perpendicular CoFeB-based junctions with exchange bias , 2017 .

[20]  John Robertson,et al.  Sub-nanometer Atomic Layer Deposition for Spintronics in Magnetic Tunnel Junctions Based on Graphene Spin-Filtering Membranes , 2014, ACS nano.

[21]  H. Meng,et al.  Materials with perpendicular magnetic anisotropy for magnetic random access memory , 2011 .

[22]  H. Ohno,et al.  A perpendicular-anisotropy CoFeB-MgO magnetic tunnel junction. , 2010, Nature materials.

[23]  Pierre L'Ecuyer,et al.  Maximally equidistributed combined Tausworthe generators , 1996, Math. Comput..

[24]  Miao Hu,et al.  A Novel True Random Number Generator Design Leveraging Emerging Memristor Technology , 2015, ACM Great Lakes Symposium on VLSI.

[25]  Eric Rescorla,et al.  The Transport Layer Security (TLS) Protocol Version 1.2 , 2008, RFC.

[26]  Witold Pedrycz,et al.  A true random number generator based on parallel STT-MTJs , 2017, Design, Automation & Test in Europe Conference & Exhibition (DATE), 2017.

[27]  Antonio Pescapè,et al.  Integration of Cloud computing and Internet of Things: A survey , 2016, Future Gener. Comput. Syst..

[28]  Bruce F. Cockburn,et al.  On the efficiency and accuracy of hybrid pseudo-random number generators for FPGA-based simulations , 2008, 2008 IEEE International Symposium on Parallel and Distributed Processing.

[29]  Yiran Chen,et al.  Compact Model of Subvolume MTJ and Its Design Application at Nanoscale Technology Nodes , 2015, IEEE Transactions on Electron Devices.