Ismail Muhammad, Rasheed Maria, Mahata Chandreswar, Kang Myounggon, Kim Sungjun
Division of Electronics and Electrical Engineering, Dongguk University, Seoul, 04620, Republic of Korea.
Department of Electronics Engineering, Korea National University of Transportation, Chungju- si, 27469, Republic of Korea.
Nano Converg. 2023 Jul 10;10(1):33. doi: 10.1186/s40580-023-00380-8.
Memristors, owing to their uncomplicated structure and resemblance to biological synapses, are predicted to see increased usage in the domain of artificial intelligence. Additionally, to augment the capacity for multilayer data storage in high-density memory applications, meticulous regulation of quantized conduction with an extremely low transition energy is required. In this work, an a-HfSiO-based memristor was grown through atomic layer deposition (ALD) and investigated for its electrical and biological properties for use in multilevel switching memory and neuromorphic computing systems. The crystal structure and chemical distribution of the HfSiOx/TaN layers were analyzed using X-ray diffraction (XRD) and X-ray photoelectron spectroscopy (XPS), respectively. The Pt/a-HfSiO/TaN memristor was confirmed by transmission electron microscopy (TEM) and showed analog bipolar switching behavior with high endurance stability (1000 cycles), long data retention performance (10 s), and uniform voltage distribution. Its multilevel capability was demonstrated by restricting current compliance (CC) and stopping the reset voltage. The memristor exhibited synaptic properties, such as short-term plasticity, excitatory postsynaptic current (EPSC), spiking-rate-dependent plasticity (SRDP), post-tetanic potentiation (PTP), and paired-pulse facilitation (PPF). Furthermore, it demonstrated 94.6% pattern accuracy in neural network simulations. Thus, a-HfSiO-based memristors have great potential for use in multilevel memory and neuromorphic computing systems.
忆阻器因其结构简单且与生物突触相似，预计在人工智能领域的应用将会增加。此外，为了增强高密度存储器应用中的多层数据存储能力，需要对具有极低转变能量的量化传导进行精确调控。在这项工作中，通过原子层沉积（ALD）生长了一种基于非晶态HfSiO的忆阻器，并对其用于多级开关存储器和神经形态计算系统的电学和生物学特性进行了研究。分别使用X射线衍射（XRD）和X射线光电子能谱（XPS）分析了HfSiOx/TaN层的晶体结构和化学分布。通过透射电子显微镜（TEM）确认了Pt/非晶态HfSiO/TaN忆阻器，其显示出具有高耐久性稳定性（1000个循环）、长数据保持性能（10秒）和均匀电压分布的模拟双极开关行为。通过限制电流依从性（CC）和停止复位电压，证明了其多级能力。该忆阻器表现出突触特性，如短期可塑性、兴奋性突触后电流（EPSC）、脉冲发放率依赖性可塑性（SRDP）、强直后增强（PTP）和双脉冲易化（PPF）。此外，在神经网络模拟中它展示了94.6%的模式准确率。因此，基于非晶态HfSiO的忆阻器在多级存储器和神经形态计算系统中具有巨大的应用潜力。
