Woo Kyung Seok, Zhang Alan, Arabelo Allison, Brown Timothy D, Park Minseong, Talin A Alec, Fuller Elliot J, Bisht Ravindra Singh, Qian Xiaofeng, Arroyave Raymundo, Ramanathan Shriram, Thomas Luke, Williams R Stanley, Kumar Suhas
Sandia National Laboratories, Livermore, CA, USA.
Department of Electrical and Computer Engineering, Texas A&M University, College Station, TX, USA.
Nat Commun. 2024 May 31;15(1):4656. doi: 10.1038/s41467-024-49149-5.
While digital computers rely on software-generated pseudo-random number generators, hardware-based true random number generators (TRNGs), which employ the natural physics of the underlying hardware, provide true stochasticity, and power and area efficiency. Research into TRNGs has extensively relied on the unpredictability in phase transitions, but such phase transitions are difficult to control given their often abrupt and narrow parameter ranges (e.g., occurring in a small temperature window). Here we demonstrate a TRNG based on self-oscillations in LaCoO that is electrically biased within its spin crossover regime. The LaCoO TRNG passes all standard tests of true stochasticity and uses only half the number of components compared to prior TRNGs. Assisted by phase field modeling, we show how spin crossovers are fundamentally better in producing true stochasticity compared to traditional phase transitions. As a validation, by probabilistically solving the NP-hard max-cut problem in a memristor crossbar array using our TRNG as a source of the required stochasticity, we demonstrate solution quality exceeding that using software-generated randomness.
虽然数字计算机依赖软件生成的伪随机数生成器，但基于硬件的真随机数生成器（TRNG）利用底层硬件的自然物理特性，提供了真正的随机性以及功率和面积效率。对TRNG的研究广泛依赖于相变中的不可预测性，但鉴于其参数范围通常很突然且狭窄（例如，发生在小温度窗口内），这种相变很难控制。在此，我们展示了一种基于LaCoO自振荡的TRNG，它在其自旋交叉区域内受到电偏置。LaCoO TRNG通过了所有真随机性的标准测试，并且与之前的TRNG相比，只使用了一半的组件数量。在相场建模的辅助下，我们展示了与传统相变相比，自旋交叉在产生真正随机性方面从根本上更具优势。作为验证，通过使用我们的TRNG作为所需随机性的来源，概率性地解决忆阻器交叉阵列中的NP难最大割问题，我们证明了解决方案质量超过使用软件生成的随机性。
