Random Bitstream Generation Using Voltage-Controlled Magnetic Anisotropy and Spin Orbit Torque Magnetic Tunnel Junctions
Random Bitstream Generation Using Voltage-Controlled Magnetic Anisotropy and Spin Orbit Torque Magnetic Tunnel Junctions
复制标题
DOI：
10.1109/jxcdc.2022.3231550
复制
发表时间：
2022-11
影响因子：
2.4
通讯作者：
Samuel Liu;J. Kwon;Paul W. Bessler;S. Cardwell;Catherine D. Schuman;J. D. Smith;J. Aimone;S. Misra;J. Incorvia
中科院分区：
文献类型：
--
作者：
Samuel Liu;J. Kwon;Paul W. Bessler;S. Cardwell;Catherine D. Schuman;J. D. Smith;J. Aimone;S. Misra;J. Incorvia
使用随机数生成器（RNG）的概率计算可以利用纳米器件的固有随机性来获得系统级的好处。该应用的候选设备需要产生高度随机的“硬币翻转”，同时还具有可调的硬币偏置。磁性隧道结（MTJ）由于其热驱动磁化动力学而被研究为RNG，通常使用自旋转移矩（STT）电流幅度来控制MTJ自由层（FL）磁化的随机切换，这里称为随机写入方法。控制MTJ-RNG还有另外的旋钮，包括压控磁各向异性（VCMA）和自旋轨道力矩（SOT），需要系统地研究和比较这些方法。我们建立了一个分析模型的MTJ来表征使用VCMA和SOT产生随机比特流。结果表明，这两种方法产生高质量，均匀分布的比特流。使用STT电流或所施加的磁场偏置比特流示出了针对VCMA和SOT两者的S形分布与偏置幅度的关系，相比之下，针对随机写入的S形较小。每个样本的能量消耗计算为0.1 pJ（SOT），1 pJ（随机写入），和20 pJ（VCMA），揭示了使用SOT的潜在能源效益，并显示使用VCMA可能需要更高的阻尼材料。然后将生成的比特流应用于两个任务：生成任意概率分布和使用MTJ-RNG作为随机神经元来执行模拟退火，其中VCMA和SOT方法都显示出以小延迟和低能量有效地最小化系统能量的能力。这些结果显示了MTJ作为真正的RNG的灵活性，并阐明了用于优化应用的器件操作的设计参数。
Probabilistic computing using random number generators (RNGs) can leverage the inherent stochasticity of nanodevices for system-level benefits. Device candidates for this application need to produce highly random “coinflips” while also having tunable biasing of the coin. The magnetic tunnel junction (MTJ) has been studied as an RNG due to its thermally-driven magnetization dynamics, often using spin transfer torque (STT) current amplitude to control the random switching of the MTJ free layer (FL) magnetization, here called the stochastic write method. There are additional knobs to control the MTJ-RNG, including voltage-controlled magnetic anisotropy (VCMA) and spin orbit torque (SOT), and there is a need to systematically study and compare these methods. We build an analytical model of the MTJ to characterize using VCMA and SOT to generate random bit streams. The results show that both methods produce high-quality, uniformly distributed bitstreams. Biasing the bitstreams using either STT current or an applied magnetic field shows a sigmoidal distribution versus bias amplitude for both VCMA and SOT, compared to less sigmoidal for stochastic write. The energy consumption per sample is calculated to be 0.1 pJ (SOT), 1 pJ (stochastic write), and 20 pJ (VCMA), revealing the potential energy benefit of using SOT and showing using VCMA may require higher damping materials. The generated bitstreams are then applied to two tasks: generating an arbitrary probability distribution and using the MTJ-RNGs as stochastic neurons to perform simulated annealing, where both VCMA and SOT methods show the ability to effectively minimize the system energy with a small delay and low energy. These results show the flexibility of the MTJ as a true RNG and elucidate design parameters for optimizing the device operation for applications.