随机性
随机数生成
稳健性(进化)
扭矩
电压
隧道磁电阻
堆栈(抽象数据类型)
计算机科学
物理
拓扑(电路)
数学
算法
电气工程
凝聚态物理
工程类
量子力学
生物化学
统计
化学
铁磁性
基因
程序设计语言
作者
X. H. Li,Mingkun Zhao,R. Zhang,Caihua Wan,Yizhan Wang,Xiaochun Luo,Shuxi Liu,Jingkang Xia,Guoqiang Yu,Xiufeng Han
摘要
True random number generators (TRNGs) play a pivotal role in solving NP-hard problems, neural network computing, and hardware accelerators for algorithms, such as the simulated annealing. In this work, we focus on TRNG based on high-barrier magnetic tunnel junctions (HB-MTJs) with identical stack structure and cell geometry, but employing different spin–orbit torque (SOT) switching schemes. We conducted a comparative study of their switching probability as a function of pulse amplitude and width of the applied voltage. Through experimental and theoretical investigations, we have observed that the Y-type SOT-MTJs exhibit the gentlest dependence of the switching probability on the external voltage. This characteristic indicates superior tunability in randomness and enhanced robustness against external disturbances when Y-type SOT-MTJs are employed as TRNGs. Furthermore, the random numbers generated by these Y-type SOT-MTJs, following XOR pretreatment, have passed the National Institute of Standards and Technology SP800-22 test. This comprehensive study demonstrates the high performance and immense potential of Y-type SOT-MTJs for the TRNG implementations.
科研通智能强力驱动
Strongly Powered by AbleSci AI