A Magnetic Tunnel Junction based True Random Number Generator with conditional perturb and real-time output probability tracking
Abstract
This work experimentally demonstrates for the first time a True Random Number Generator (TRNG) based on the random switching probability of Magnetic Tunnel Junctions (MTJs). A conditional perturb and real-time output probability tracking scheme is proposed to enhance the reliability, speed, and power consumption while maintaining a 100% bit efficiency.
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tRNGs such as RRAM or MRAM are widely studied given their inherent switching stochasticity as an entropy source, which relies on material-induced variations.
“For example, tRNGs such as RRAM or MRAM, which are not Si-based complementary metal-oxide semiconductor (CMOS) devices, are widely studied given their inherent switching stochasticity as an entropy source, which relies on material-induced variations (11–18).”
From Cryptographic transistor for true random number generator with low power consumption (2024) · cites this paper as [11]
Their lack of compatibility with CMOS fabrication standards makes it difficult to apply them directly to the current CMOS logic platform used for mass production.
“Moreover, their lack of compatibility with CMOS fabrication standards makes it difficult to apply them directly to the current CMOS logic platform used for mass production.”
From Cryptographic transistor for true random number generator with low power consumption (2024) · cites this paper as [11]
These tRNGs require a few entropy extraction circuits, each of which consumes power at the milliwatt level.
“However, these tRNGs require a few entropy extraction circuits, each of which consumes power at the milliwatt level.”
From Cryptographic transistor for true random number generator with low power consumption (2024) · cites this paper as [11]
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Memory devices and applications for in-memory computing
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In-memory computing with resistive switching devices
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“Due to its higher stability and better endurance, STT-MRAM devices appear to be the best device option to implement TRNGs.”
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Chen, A. (2016) · Solid-State Electronics · doi:10.1016/j.sse.2016.07.006 · Cited by 668 · see citing works
“This behavior can be utilized to create a TRNG, which has been demonstrated experimentally with the proof of true randomness for security applications [126–128].”
BackgroundMethodStochastic p -Bits for Invertible Logic
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p-bits for probabilistic spin logic
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Spin-Transfer Torque Devices for Logic and Memory: Prospects and Perspectives
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“While the stochastic switching behavior is unfavorable in memory applications, it can be leveraged to design a promising TRNG, or spin dice (SD), as recently proposed and demonstrated [134], [135].”
BackgroundSpin-Transfer Torque Memories: Devices, Circuits, and Systems
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Implementing p-bits With Embedded MTJ
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Stochastic Memory Devices for Security and Computing
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“an improved MTJ endurance and power consumption per generated bit [143].”
BackgroundMethodInfluentialPhysical Unbiased Generation of Random Numbers With Coupled Resistive Switching Devices
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“For this purpose, real-time voltage tracking techniques must be used for compensating 0 and 1 probabilities [7].”
BackgroundMethodInfluentialTrends in Hardware Security: From Basics to ASICs
Alioto, M. (2019) · IEEE Solid-State Circuits Magazine · doi:10.1109/mssc.2019.2923503 · Cited by 114 · see citing works
“Among the others, the randomness of the write time in spin-transfer torque magnetic RAM devices [97] and the stochastic nature of the state being written at moderate write current level [21] have been explored.”
BackgroundEnergy-Efficient Stochastic Computing with Superparamagnetic Tunnel Junctions
Daniels, M. W., Madhavan, A., Talatchian, P., Mizrahi, F. A., & Stiles, M. D. (2020) · Physical Review Applied · doi:10.1103/physrevapplied.13.034016 · Cited by 89 · see citing works
“[43] are based on MTJs with average resistance R = 1000 Ω, and use an average write voltage of 217 .”
BackgroundMethodInfluentialLow-Barrier Nanomagnets as p-Bits for Spin Logic
Faria, R., Camsari, K. Y., & Datta, S. (2017) · IEEE Magnetics Letters · doi:10.1109/lmag.2017.2685358 · Cited by 85 · see citing works
Random Number Generation Based on Ferroelectric Switching
Mulaosmanovic, H., Mikolajick, T., & Slesazeck, S. (2017) · IEEE Electron Device Letters · doi:10.1109/led.2017.2771818 · Cited by 79 · see citing works
“Similarly, the stochastic nature of spin-torque switching in a magnetic tunnel junction (MTJ) [8], [9] has been proposed for RNGs as well.”
BackgroundSpintronics and Security: Prospects, Vulnerabilities, Attack Models, and Preventions
Ghosh, S. (2016) · Proceedings of the IEEE · doi:10.1109/jproc.2016.2583419 · Cited by 75 · see citing works
Weighted $p$ -Bits for FPGA Implementation of Probabilistic Circuits
Pervaiz, A. Z., Sutton, B., Ghantasala, L. A., & Camsari, K. Y. (2018) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2018.2874565 · Cited by 75 · see citing works
“Such stochastic MTJs have been experimentally demonstrated [8]–[12].”
BackgroundImplementing Bayesian networks with embedded stochastic MRAM
Faria, R., Camsari, K. Y., & Datta, S. (2018) · AIP Advances · doi:10.1063/1.5021332 · Cited by 71 · see citing works
Time-Encoded Values for Highly Efficient Stochastic Circuits
Najafi, M. H., Jamali-Zavareh, S., Lilja, D. J., et al. (2017) · IEEE Transactions on Very Large Scale Integration (VLSI) Systems · doi:10.1109/tvlsi.2016.2645902 · Cited by 69 · see citing works
“Obtain an unbiased random value 0 ≤ r ≤ 1 from a random [15], [16] or pseudo-random source [19], [20]; compare it with the target value x ; output a one if r ≤ x and a zero otherwise.”
BackgroundIn-memory computing to break the memory wall*
Huang, X., Liu, C., Jiang, Y., & Zhou, P. (2020) · Chinese Physics B · doi:10.1088/1674-1056/ab90e7 · Cited by 70 · see citing works
True Random Number Generation Using Read Noise of Flash Memory Cells
Ray, B., & Milenković, A. (2018) · IEEE Transactions on Electron Devices · doi:10.1109/ted.2018.2792436 · Cited by 64 · see citing works
“The other interesting TRNG proposals include an oscillator-based TRNG [15], a technology-independent TRNG [16], a portable TRNG using smartphone camera [17], a TRNG based on hot-carrier injection-balanced metastable sense amplifiers [18], and TRNGs based on switching instability in emerging memory technologies, such a…”
BackgroundHigh-speed true random number generation based on paired memristors for security electronics
Zhang, T., Yin, M., Xu, C., Lu, X., Sun, X., Yang, Y., & Huang, R. (2017) · Nanotechnology · doi:10.1088/1361-6528/aa8b3a · Cited by 62 · see citing works
Spin-Orbit-Torque-Based Spin-Dice: A True Random-Number Generator
Kim, Y., Fong, X., & Roy, K. (2015) · IEEE Magnetics Letters · doi:10.1109/lmag.2015.2496548 · Cited by 61 · see citing works
A building block for hardware belief networks
Behin-Aein, B., Diep, V., & Datta, S. (2016) · Scientific Reports · doi:10.1038/srep29893 · Cited by 70 · see citing works
Bayesian Inference With Muller C-Elements
Friedman, J. S., Calvet, L. E., Bessìère, P., Jacques Droulez, A., & Querlioz, D. (2016) · IEEE Transactions on Circuits and Systems I Regular Papers · doi:10.1109/tcsi.2016.2546064 · Cited by 52 · see citing works
“This estimate ignores the overhead associated with random number generation, which may be efficiently achieved with emerging nanodevices [16], [20], [21] and will therefore become essential”
BackgroundAziza, H., Postel-Pellerin, J., Bazzi, H., Canet, P., Moreau, M., della Marca, V., & Harb, A. (2020) · IEEE Transactions on Nanotechnology · doi:10.1109/tnano.2020.2976735 · Cited by 55 · see citing works
“2976735 on resistive-switching devices [4]–[6].”
MethodReview of Magnetic Tunnel Junctions for Stochastic Computing
Zink, B. R., Lv, Y., & Wang, J. (2022) · IEEE Journal on Exploratory Solid-State Computational Devices and Circuits · doi:10.1109/jxcdc.2022.3227062 · Cited by 50 · see citing works
“3(a) and (b) [63].”
Self-citationCSRO-Based Reconfigurable True Random Number Generator Using RRAM
Govindaraj, R., Ghosh, S., & Katkoori, S. (2018) · IEEE Transactions on Very Large Scale Integration (VLSI) Systems · doi:10.1109/tvlsi.2018.2823274 · Cited by 49 · see citing works
“Emerging technologies such as spintronics [6], [7], memristor [8], and resis-”
A Variation-Aware Timing Modeling Approach for Write Operation in Hybrid CMOS/STT-MTJ Circuits
Rose, R. D., Lanuzza, M., Crupi, F., et al. (2017) · IEEE Transactions on Circuits and Systems I Regular Papers · doi:10.1109/tcsi.2017.2762431 · Cited by 48 · see citing works
“The resulting state of the MTJ is random as it depends only on the stochastic variations of the tS [46].”
BackgroundStochastic Insulator-to-Metal Phase Transition-Based True Random Number Generator
Jerry, M., Ni, K., Parihar, A., Raychowdhury, A., & Datta, S. (2017) · IEEE Electron Device Letters · doi:10.1109/led.2017.2771812 · Cited by 47 · see citing works
“Emerging devices such as magnetic tunnel junctions [6] and resistive random access memory devices [7] exhibit intrinsic stochastic behavior and have shown potential as TRNGs.”
BackgroundOosawa, S., Konishi, T., Onizawa, N., & Hanyu, T. (2015) · IEEE International New Circuits and Systems Conference (NEWCAS) · doi:10.1109/newcas.2015.7182089 · Cited by 47 · see citing works
“A CMOS-based TRNG [1] is suitable for integration but both generation speed and randomness are not good, while a Spin Transfer Torque Magnetic Tunnel Junction (STT-MTJ) [2] based TRNG [3]-[5] has a superior advantage of generation speed and randomness quality, but it is difficult to be integrated in a scaled CMOS tech…”
BackgroundVariation-Resilient True Random Number Generators Based on Multiple STT-MTJs
Qu, Y., Cockburn, B. F., Huang, Z. Y., Cai, H., Zhang, Y., Zhao, W. S., & Han, J. (2018) · IEEE Transactions on Nanotechnology · doi:10.1109/tnano.2018.2873970 · Cited by 47 · see citing works
“One of the possible solutions in the literature includes a feedback calibration circuit [10]–[12],”
BackgroundOstwal, V., & Appenzeller, J. (2019) · IEEE Magnetics Letters · doi:10.1109/lmag.2019.2912971 · Cited by 46 · see citing works
High-Entropy STT-MTJ-Based TRNG
Vatajelu, E. I., & Natale, G. D. (2018) · IEEE Transactions on Very Large Scale Integration (VLSI) Systems · doi:10.1109/tvlsi.2018.2879439 · Cited by 45 · see citing works
“This result validated by the work in [8], where the authors show measurement data of an MTJ device.”
ResultFrom Charge to Spin and Spin to Charge: Stochastic Magnets for Probabilistic Switching
Camsari, K. Y., Debashis, P., Ostwal, V., Pervaiz, A. Z., Shen, T., Chen, Z., Datta, S., & Appenzeller, J. (2020) · Proceedings of the IEEE · doi:10.1109/jproc.2020.2966925 · Cited by 44 · see citing works
“The stochastic switching behavior of a collection of high-barrier nanomagnets can be exploited to generate randomness in different ways [30], [31], [71]–[76].”
BackgroundRecent trends in hardware security exploiting hybrid CMOS-resistive memory circuits
Sahay, S., & Suri, M. (2017) · Semiconductor Science and Technology · doi:10.1088/1361-6641/aa8f07 · Cited by 44 · see citing works
GeSe-Based Ovonic Threshold Switching Volatile True Random Number Generator
Chai, Z., Freitas, P., Marsland, J., Fantini, A., Garbin, D., Goux, L., Kar, G. S., Shao, W., Zhang, W., Brown, J., et al. (2019) · IEEE Electron Device Letters · doi:10.1109/led.2019.2960947 · Cited by 41 · see citing works
“The difficulty in finding the accurate balance point, and the sensitivity this point to bias and process variations, is a common issue for many emerging TRNG solutions based on novel logic and memory devices such as FeFET, RRAM and magnetic tunnel junction (MTJ)[8],[13],[14],[31],[32].”
MethodEmerging nonvolatile memory (NVM) technologies
Chen, A. (2015) · European Solid-State Device Research Conference · doi:10.1109/essderc.2015.7324725 · Cited by 39 · see citing works
“This behavior can be utilized to create a RNG, which has been demonstrated experimentally and shown to have sufficient randomness for security applications [31].”
BackgroundPerspective: Stochastic magnetic devices for cognitive computing
Roy, K., Sengupta, A., & Shim, Y. S. (2018) · Journal of Applied Physics · doi:10.1063/1.5020168 · Cited by 37 · see citing works
Spintronics intelligent devices
Cai, W., Huang, Y., Zhang, X., Wang, S., Pan, Y., Yin, J., Shi, K., & Zhao, W. S. (2023) · Science China Physics Mechanics and Astronomy · doi:10.1007/s11433-022-2012-2 · Cited by 37 · see citing works
Cryptographic transistor for true random number generator with low power consumption
Kim, S.-I., You, H.-J., Kim, M.-S., An, U.-S., Kim, M.-S., Lee, D. H., Ryu, S.-T., & Choi, Y.-K. (2024) · Science Advances · doi:10.1126/sciadv.adk6042 · Cited by 33 · see citing works
“For example, tRNGs such as RRAM or MRAM, which are not Si-based complementary metal-oxide semiconductor (CMOS) devices, are widely studied given their inherent switching stochasticity as an entropy source, which relies on material-induced variations ( 11 – 18 ).”
A Low-Cost Highly Reliable Spintronic True Random Number Generator Circuit for Secure Cryptography
Alibeigi, I., Amirany, A., Rajaei, R., et al. (2019) · SPIN · doi:10.1142/s2010324720500034 · Cited by 33 · see citing works
Hardware implementation of Bayesian network building blocks with stochastic spintronic devices
Debashis, P., Ostwal, V., Faria, R., et al. (2020) · Scientific Reports · doi:10.1038/s41598-020-72842-6 · Cited by 33 · see citing works
A survey of in-spin transfer torque MRAM computing
Cai, H., Liu, B., Chen, J., Naviner, L., Zhou, Y., Wang, Z., & Yang, J. (2021) · Science China Information Sciences · doi:10.1007/s11432-021-3220-0 · Cited by 33 · see citing works
“Data security based on the stochastic behavior of MRAM is also important, such as the physical unclonable function and true random number generator [43–45].”
BackgroundAn Overview of Spintronic True Random Number Generator
Fu, Z., Tang, Y., Zhao, X., Lü, K., Dong, Y., Shukla, A., Zhu, Z., & Yang, Y. (2021) · Frontiers in Physics · doi:10.3389/fphy.2021.638207 · Cited by 33 · see citing works
“The correction logic works similarly to the tracking-feedback circuit in another example [33].”
MethodRHS-TRNG: A Resilient High-Speed True Random Number Generator Based on STT-MTJ Device
Fu, S., Li, T., Zhang, C., et al. (2023) · IEEE Transactions on Very Large Scale Integration (VLSI) Systems · doi:10.1109/tvlsi.2023.3298327 · Cited by 30 · see citing works
Magnetoresistive Circuits and Systems: Embedded Non-Volatile Memory to Crossbar Arrays
Agrawal, A., Wang, C., Sharma, T., & Roy, K. (2021) · IEEE Transactions on Circuits and Systems I Regular Papers · doi:10.1109/tcsi.2021.3069682 · Cited by 29 · see citing works
Spintronic memristors for computing
Shao, Q., Wang, Z., Zhou, Y., Fukami, S., Querlioz, D., & Chua, L. O. (2025) · npj Spintronics · doi:10.1038/s44306-025-00078-z · Cited by 40 · see citing works
“Thestochasticnatureofswitchingcanbeusedfor truerandomnumbergeneration 129,130 and stochastic computing 131 .”
Random Number Generation by Differential Read of Stochastic Switching in Spin-Transfer Torque Memory
Carboni, R., Chen, W., Siddik, M., Harms, J., Lyle, A., Kula, W., Sandhu, G. S., & Ielmini, D. (2018) · IEEE Electron Device Letters · doi:10.1109/led.2018.2833543 · Cited by 28 · see citing works
“Both spin-transfer torque magnetic memory (STT-MRAM) [16], [23] and RRAM [18]–[20] have been used to demonstrate working TRNG.”
MethodSpinDrop: Dropout-Based Bayesian Binary Neural Networks With Spintronic Implementation
Ahmed, S. T., Danouchi, K., Münch, C., Prenat, G., Anghel, L., & Tahoori, M. B. (2023) · IEEE Journal on Emerging and Selected Topics in Circuits and Systems · doi:10.1109/jetcas.2023.3242146 · Cited by 25 · see citing works
“To compensate for probability deviation, another solution consists in adding a feedback loop to increase the accuracy of the generated probability [31], [32].”
BackgroundSpintastic: spin-based stochastic logic for energy-efficient computing
Venkatesan, R., Venkataramani, S., Fong, X., et al. (2015) · National University of Singapore · doi:10.5555/2755753.2757176 · Cited by 24 · see citing works
Microcontroller TRNGs Using Perturbed States of NOR Flash Memory Cells
Poudel, P., Ray, B., & Milenković, A. (2018) · IEEE Transactions on Computers · doi:10.1109/tc.2018.2866459 · Cited by 21 · see citing works
“and spin transfer-torquemagnetic memory (MRAM) [14].”
Hybrid CMOS-RRAM True Random Number Generator Exploiting Coupled Entropy Sources
Equbal, M. S., Ketkar, T., & Sahay, S. (2023) · IEEE Transactions on Electron Devices · doi:10.1109/ted.2023.3241122 · Cited by 21 · see citing works
“Therefore, TRNGs based on stochastic switching variability of MRAMs [12], [13] and self-heating phase change memory (PCM) arrays [14] were demonstrated.”
BackgroundTrue random number generator based on spin–orbit torque magnetic tunnel junctions
Li, X., Zhao, M., Zhang, R., et al. (2023) · Applied Physics Letters · doi:10.1063/5.0171768 · Cited by 21 · see citing works
True random number generation exploiting SET voltage variability in resistive RAM memory arrays
Postel-Pellerin, J., Bazzi, H., Aziza, H., Canet, P., Moreau, M., della Marca, V., & Harb, A. (2019) · Non-Volatile Memory Technology Symposium · doi:10.1109/nvmts47818.2019.9043369 · Cited by 21 · see citing works
“Over the last few years there has been a lot of activity across research groups proposing efficient TRNG based on resistive-switching devices [4-6].”
BackgroundMethodSRAM Circuits for True Random Number Generation Using Intrinsic Bit Instability
Clark, L. T., Medapuram, S. B., & Kadiyala, D. K. (2018) · IEEE Transactions on Very Large Scale Integration (VLSI) Systems · doi:10.1109/tvlsi.2018.2840049 · Cited by 18 · see citing works
“Variation with time allows better TRNGs, since their capacity is not limited [6]–[8].”
BackgroundA spin orbit torque based true random number generator with real-time optimization
Liu, Y., Wang, Z., Li, Z., et al. (2018) · IEEE International Conference on Nanotechnology (IEEE-NANO) · doi:10.1109/nano.2018.8626347 · Cited by 18 · see citing works
“A true random number generator (TRNG) circuit was designed by leveraging a STT-MTJ with a correction block in [1], while in [2] a STT-MTJ based TRNG (STT-TRNG) with real-time”
MethodBipolar Random Spike and Bipolar Random Number Generation by Two Magnetic Tunnel Junctions
Lv, Y., Zink, B. R., & Wang, J. (2022) · IEEE Transactions on Electron Devices · doi:10.1109/ted.2022.3144117 · Cited by 17 · see citing works
Self-citationRen, R., Cao, Y. H., Wang, C., et al. (2024) · Nano Letters · doi:10.1021/acs.nanolett.4c01989 · Cited by 16 · see citing works
True Random Number Generation using Latency Variations of Commercial MRAM Chips
Ferdaus, F., Talukder, B. M. S. B., Sadi, M., & Rahman, M. T. (2021) · International Symposium on Quality Electronic Design (ISQED) · doi:10.1109/isqed51717.2021.9424346 · Cited by 18 · see citing works
“Therefore, several emerging memory-based TRNGs, capable of providing high density and throughput, have been proposed to overcome existing challenges [8], [12], [15], [22], [23].”
BackgroundShukla, A., Heller, L., Morshed, M. G., et al. (2023) · International Symposium on Quality Electronic Design (ISQED) · doi:10.1109/isqed57927.2023.10129319 · Cited by 16 · see citing works
“For example, in [5], [13], a 10-bit counter feedback circuit, which counts the number of ‘1’s’ in a random sequence and adjusts the input current pulse duration in real time to achieve 50% switching probability,”
BackgroundMethodTseng, P.-H., Lee, M.-H., Lin, Y., Lung, H.-L., Wang, K.-C., & Lu, C.-Y. (2021) · IEEE Transactions on Electron Devices · doi:10.1109/ted.2021.3057028 · Cited by 18 · see citing works
A High-Speed True Random Number Generator Based on Unified Selector-RRAM
Qin, Y., Wang, Z., Yang, Y., Shan, L., Wang, Q., Bao, L., Robertson, J., Cai, Y., & Huang, R. (2023) · IEEE Electron Device Letters · doi:10.1109/led.2023.3322992 · Cited by 16 · see citing works
“…the proposed TRNG based on VO x /HfO x device exhibits improved stability and reliability against process variation compared with other emerging device-based TRNGs, such as RRAM [19], [20], [21], ovonic threshold switching (OTS) selector [23] and magnetic tunnel junction (MTJ) [30], etc. Fig.”
MethodSpin-Hall-Effect-Based Stochastic Number Generator for Parallel Stochastic Computing
Hu, J., Li, B., Ma, C., et al. (2019) · IEEE Transactions on Electron Devices · doi:10.1109/ted.2019.2920401 · Cited by 15 · see citing works
“ous researchers have achieved true randomness by biasing an MTJ to its switching thresholds [6], [14].”
BackgroundIntrinsic MRAM Properties Enable Security Circuits
Du, H., Wang, Y., Yang, J., & Cai, H. (2023) · IEEE Transactions on Circuits & Systems II Express Briefs · doi:10.1109/tcsii.2023.3331842 · Cited by 15 · see citing works
“In comparison, emerging non-volatile memory devices like resistive random access memory (RRAM) [5], [6], [7] and magnetoresistive random access memory (MRAM) [8], [9], [10], [11], [12] exhibit promising potential in hardware security.”
BackgroundSpin Orbit Torque-Assisted Magnetic Tunnel Junction-Based Hardware Trojan
Kumar, R., Divyanshu, D., Khan, D., et al. (2022) · Electronics · doi:10.3390/electronics11111753 · Cited by 14 · see citing works
Bai, Y. D., Fan, D., & Lin, M. (2017) · IEEE Transactions on Multi-Scale Computing Systems · doi:10.1109/tmscs.2017.2787109 · Cited by 14 · see citing works
“The proposed random number generator time diagram [35], [42].”
BackgroundMethodResultInfluentialPolysynchronous stochastic circuits
Najafi, M. H., Lilja, D. J., Riedel, M. D., & Bazargan, K. (2016) · Asia and South Pacific Design Automation Conference (ASP-DAC) · doi:10.1109/aspdac.2016.7428060 · Cited by 13 · see citing works
“Such streams can either be generated from physical random sources [19, 20] or with pseudo-random constructs such as linear feedback shift registers (LFSRs).”
BackgroundApproximation enhancement for stochastic Bayesian inference
Friedman, J. S., Jacques Droulez, A., Bessìère, P., Lobo, J., & Querlioz, D. (2017) · International Journal of Approximate Reasoning · doi:10.1016/j.ijar.2017.03.007 · Cited by 12 · see citing works
“Particularly exciting technologies include memristors [29], [30], magnetic tunnel junctions [26]–[28], and single-electron transistors [25].”
On the Hardware Implementation of MRAM Physically Unclonable Function
Chen, Y.-S., Wang, D.-Y., Hsin, Y.-C., et al. (2017) · IEEE Transactions on Electron Devices · doi:10.1109/ted.2017.2755867 · Cited by 13 · see citing works
“is adopted in [11], only single MTJ cell was investigated.”
Toward Energy-Efficient STT-MRAM Design With Multi-Modes Reconfiguration
Cai, H., Chen, J., Zhou, Y., & Zhao, W. S. (2021) · IEEE Transactions on Circuits & Systems II Express Briefs · doi:10.1109/tcsii.2021.3059031 · Cited by 13 · see citing works
Becle, E., Prenat, G., Talatchian, P., Anghel, L., & Prejbeanu, L. (2022) · IEEE Transactions on Circuits and Systems I Regular Papers · doi:10.1109/tcsi.2022.3173030 · Cited by 13 · see citing works
Neuromorphic computing with hybrid memristive/CMOS synapses for real-time learning
Ielmini, D., Ambrogio, S., Milo, V., Balatti, S., & Wang, Z. (2016) · IEEE International Symposium on Circuits and Systems proceedings · doi:10.1109/iscas.2016.7527508 · Cited by 13 · see citing works
“The stochastic variation of switching characteristics allows to develop compact circuit blocks for true random number generation (RNG) using RRAM [7] and STT-RAM [8].”
MethodCompact Programmable True Random Number Generator Based on Spin Torque Nano-Oscillator
Zhang, B., Lin, Z., Liu, Y., et al. (2022) · IEEE Transactions on Nanotechnology · doi:10.1109/tnano.2022.3216844 · Cited by 11 · see citing works
“Magnetic Tunneling Junction (MTJ) also holds intrinsic stochasticity in switching process and can be used as TRNG devices [13], [14], [15], [16], [17].”
MethodPUFFIN: An Efficient DNN Training Accelerator for Direct Feedback Alignment in FeFET
Chen, F. (2021) · International Symposium on Low Power Electronics and Design (ISLPED) · doi:10.1109/islped52811.2021.9502499 · Cited by 10 · see citing works
Hybrid spin‐CMOS stochastic spiking neuron for high‐speed emulation of In vivo neuron dynamics
Pyle, S. D., Camsari, K. Y., & DeMara, R. F. (2018) · IET Computers & Digital Techniques · doi:10.1049/iet-cdt.2017.0145 · Cited by 8 · see citing works
A Calibration-Free In-Memory True Random Number Generator Using Voltage-Controlled MRAM
Yang, J., Wu, D., Lee, A., et al. (2021) · IEEE European Solid-State Device Research Conference (ESSDERC) · doi:10.1109/essderc53440.2021.9631784 · Cited by 9 · see citing works
“STT-MRAM rely on a critical current that has a pre-determined amplitude and pulse width to achieve metastability.”
MethodInfluentialZink, B. R., Lv, Y., Zabihi, M., et al. (2023) · IEEE Journal on Exploratory Solid-State Computational Devices and Circuits · doi:10.1109/jxcdc.2023.3266136 · Cited by 9 · see citing works
“This is because a single magnetic tunnel junction (MTJ) can be used as a true RNG (TRNG) with a tunable probability [23], [24], [25], [26].”
Self-citationPolysynchronous Clocking: Exploiting the Skew Tolerance of Stochastic Circuits
Najafi, M. H., Lilja, D. J., Riedel, M. D., et al. (2017) · IEEE Transactions on Computers · doi:10.1109/tc.2017.2697881 · Cited by 9 · see citing works
“value 0 r 1 from a random [9] [43] or pseudorandom source [13], [18]; compare it to the target value x; output a one if r x and a zero otherwise.”
BackgroundVodenicarevic, D., Locatelli, N., Mizrahi, F. A., Hirtzlin, T., Friedman, J. S., Grollier, J., & Querlioz, D. (2018) · IEEE International Symposium on Circuits and Systems proceedings · doi:10.1109/iscas.2018.8351771 · Cited by 9 · see citing works
Process Variation-Resilient STT-MTJ based TRNG using Linear Correcting Codes
Ali, R., Wang, Y., Hou, Z., Ma, H., Zhang, Y., & Zhao, W. S. (2019) · IEEE/ACM International Symposium on Nanoscale Architectures (NANOARCH) · doi:10.1109/nanoarch47378.2019.181294 · Cited by 10 · see citing works
“TRNGs based on these devices with stochastic behaviors were recently proposed, e.g. resistive random-access memories (RRAMs) [11] [12] and magnetic tunnel junctions (MTJs) [13]-[17].”
BackgroundMethodSalehi, S., Zand, R., Zaeemzadeh, A., et al. (2019) · ACM Great Lakes Symposium on VLSI (GLSVLSI) · doi:10.1145/3299874.3318037 · Cited by 8 · see citing works
“In recent years the use of superparamagnetic MTJs that are not thermally stable have been experimentally and theoretically investigated in search of functional spintronic devices [5, 7–9, 12, 17, 21, 22].”
BackgroundProcess Variation Sensitivity of Spin-Orbit Torque Perpendicular Nanomagnets in DBNs
Pourmeidani, H., Debashis, P., Chen, Z., et al. (2021) · IEEE Transactions on Magnetics · doi:10.1109/tmag.2021.3075391 · Cited by 8 · see citing works
“In [4]– [13], efficient and small neuronal hardware in several learning systems is realized by utilizing these single-bit spin-based hardware units’ stochasticity behavior.”
BackgroundDebashis, P., Li, H., Nikonov, D. E., et al. (2022) · IEEE Magnetics Letters · doi:10.1109/lmag.2022.3152991 · Cited by 8 · see citing works
Zahoor, F., Nisar, A., Das, K. K., et al. (2024) · IEEE Transactions on Electron Devices · doi:10.1109/ted.2024.3399170 · Cited by 7 · see citing works
“…stochastic behavior of MTJ switching such as using optimal excitation pulse in the presence of process, voltage, and temperature (PVT) variations [21], switching of MTJ in the subcritical current regime [5], and combining the stochastic nature of MTJ and behavior of XOR gates against…”
BackgroundBhuin, S., Sweeney, J., Pagliarini, S., et al. (2017) · IEEE/ACM International Symposium on Nanoscale Architectures (NANOARCH) · doi:10.1109/nanoarch.2017.8053738 · Cited by 7 · see citing works
“This STT-MTJ has been exploited for implementing TRNGs in several schemes [8], [9], [10].”
BackgroundMethodClark, L. T., Adams, J. W., & Holbert, K. E. (2017) · Integration · doi:10.1016/j.vlsi.2017.10.001 · Cited by 7 · see citing works
Zhang, R., Li, X., Wan, C., Hoffmann, R., Hindenberg, M., Xu, Y., Liu, S., Kong, D., Xiong, S., He, S., et al. (2025) · Nature Communications · doi:10.1038/s41467-025-66864-9 · Cited by 9 · see citing works
Narasimman, G., Basu, J., Sethi, P., Krishnia, S., Yi, C., Lew, W. S., & Basu, A. (2020) · IEEE Sensors Journal · doi:10.1109/jsen.2020.2980021 · Cited by 6 · see citing works
“In such MRNGs, switching probability of a magnetic tunnel junction (MTJ) is adjusted with injected current to obtain stochastic orientation of free layer of random numbers [5], [6].”
MethodZink, B. R., Riedel, M. D., Karpuzcu, U. R., et al. (2023) · IEEE Transactions on Magnetics · doi:10.1109/tmag.2023.3326076 · Cited by 6 · see citing works
“Spintronics is a beyond-CMOS technology [6], [7] that are promising solutions for probabilistic computing schemes and have been proposed as random number generators [8], [9], [10], [11].”
BackgroundSelf-citationLeveraging Spintronic Devices for Efficient Approximate Logic and Stochastic Neural Networks
Angizi, S., He, Z., Bai, Y. D., et al. (2018) · ACM Great Lakes Symposium on VLSI (GLSVLSI) · doi:10.1145/3194554.3194618 · Cited by 6 · see citing works
“In this paper, we employ the conditional perturbation scheme [7] to generate random sample and achieve a bit rate 2.”
MethodDual-Bit Spin Dice Based on Spin–Orbit Torque in Perpendicular Magnetic Multilayers
Yi, P., Ma, H. H., Xu, N., et al. (2019) · IEEE Magnetics Letters · doi:10.1109/lmag.2019.2944123 · Cited by 6 · see citing works
Applications of Resistive Switching Memory as Hardware Security Primitive
Carboni, R., & Ielmini, D. (2019) · Springer series in advanced microelectronics · doi:10.1007/978-981-13-8379-3_4 · Cited by 7 · see citing works
“Thestochastic switching in STT-MRAM has been used for various TRNG concepts, either based on the time variation [31, 83] or the voltage variation [76, 30].”
BackgroundMethodInfluentialA Calibration-Free In-Memory True Random Number Generator Using Voltage-Controlled MRAM
Yang, J., Wu, D., Lee, A., et al. (2021) · Proceedings of ESSCIRC · doi:10.1109/esscirc53450.2021.9567766 · Cited by 5 · see citing works
Spin Dice Based on Orthogonal Spin-Transfer Devices With Planar Polarizer
Li, X., Luo, S., Qin, H., et al. (2018) · IEEE Transactions on Magnetics · doi:10.1109/tmag.2018.2832194 · Cited by 5 · see citing works
“NIST’s SP 800-22 statistical test [22] was utilized to test the randomness of the generated bit streams which have not been treated with any post-processions.”
MethodExploring Security Solutions and Vulnerabilities for Embedded Non-Volatile Memories
Tisha, Z. T., Muldavin, J., & Guin, U. (2024) · IEEE Computer Society Annual Symposium on VLSI · doi:10.1109/isvlsi61997.2024.00072 · Cited by 4 · see citing works
“…[41] – RRAM [17], [18] [42], [43], [46] – MRAM [19], [20] [33], [35], [39] [49] FLASH [23], [51] [23], [40] – FeRAM [52] [36] – devices [33]–[35], FeRAMS [36], etc. MRAM-based TRNGs have gained attention due to their ability to produce high-quality random numbers and robustness [37]–[39].”
BackgroundOuattara, F., Nejat, A., Torres, L., et al. (2019) · IEEE Access · doi:10.1109/access.2019.2907186 · Cited by 4 · see citing works
“The authors in [11] proposed the RTOPT approach to generated numbers with less bias.”
BackgroundZou, S., Zhao, X. G., Xue, Y., Gao, J., Li, Y.-L., Cui, Y., & Luo, J. (2025) · IEICE Electronics Express · doi:10.1587/elex.22.20250084 · Cited by 4 · see citing works
Electrically-Tunable Stochasticity for Spin-based Neuromorphic Circuits: Self-Adjusting to Variation
Pourmeidani, H., Debashis, P., Chen, Z., et al. (2020) · Conference proceedings · doi:10.1109/mwscas48704.2020.9184556 · Cited by 3 · see citing works
“Recently, the stochasticity behavior of these single-bit spin-based hardware units has been used to realize compact and effective neuronal hardware in various learning systems [1]-[4].”
BackgroundQuantum random number generation using an on-chip nanowire plasmonic waveguide
Strydom, C., Soleymani, S., Özdemir, Ş. K., & Tame, M. (2024) · New Journal of Physics · doi:10.1088/1367-2630/ad2d1c · Cited by 3 · see citing works
- Authors
- Choi, W. H., Lv, Y., Kim, J., Deshpande, A., Kang, G., Wang, J.-P., & Kim, C. H.
- Affiliations
- University of Minnesota (US)
- Venue
- 2014 IEEE International Electron Devices Meeting
- Publisher
- IEEE
- Published
- 2014-12
- Pages
- 12.5.1-12.5.4
- ISSN
- 0163-1918 (ISSN-L)
- OpenAlex
- W2004596214
- Cited by
- 128
- References
- 6
- Citations by year
- 2026: 3 · 2025: 7 · 2024: 7 · 2023: 12 · 2022: 8 · 2021: 13 · 2020: 11 · 2019: 20 · 2018: 16 · 2017: 16 · 2016: 10 · 2015: 5
- FWCI
- 6.75
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- Technical digest - International Electron Devices Meeting/Technical digest (publishedVersion)
- Funders
- Defense Advanced Research Projects Agency
- Topics
- Physical Sciences › Computer Science › Hardware and Architecture › Physical Unclonable Functions (PUFs) and Hardware Security
- Physical Sciences › Physics and Astronomy › Atomic and Molecular Physics, and Optics › Magnetic properties of thin films
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Chaos-based Image/Signal Encryption
- Keywords
- true random number generator, magnetic tunnel junctions, power consumption, hardware randomness
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