Neuromorphic devices have garnered significant attention for their potential to revolutionize conventional computing architecture and drive advancements in artificial neural systems. Aluminum nitride-based (AlN-based) memristors have emerged as particularly noteworthy due to their exceptional properties, including ultrafast switching speed, small switching current, substantial on/off ratio, controllable material growth, and compatibility with complementary metal-oxide-semiconductor (CMOS) processes. These remarkable characteristics hold immense significance in the fabrication of novel neuromorphic devices, specifically for artificial synapses. However, the commonly observed abrupt resistive switching behavior in AlN-based memristors poses a challenge to the recognition accuracy of artificial neural networks (ANNs) at the system level. Thus, achieving a gradual switching behavior with multi-level conductance becomes highly desirable for artificial synapses. Here, an interfacial engineering approach is introduced to optimize the Ag/AlN/Pt memristor by incorporating an aluminum scandium nitride (AlScN) layer within the AlN layer. The tri-layer AlN/AlScN/AlN stacked memristor (ASAM) demonstrates a notable achievement of gradual switching behavior at the RESET operation, attributed to the alleviation of abrupt conductive filament formation resulting from the ferroelectric polarization effect of the AlScN layer. Additionally, the ASAM shows excellent resistive switching performance with ultrafast switching speed (<5 ns), low operation voltage (<0.5 V), and ultralow power consumption as small as 0.2 pJ. By appropriately adjusting the current compliance and resetting stop voltage, the ASAM exhibits remarkable characteristics of controlled gradual switching and multi-level conductance. Moreover, the ASAM successfully mimics biological synaptic functions, such as long-term potentiation (LTP), long-term depression (LTD), paired-pulse facilitation (PPF), and Spike-Timing-Dependent Plasticity (STDP). Leveraging the near linearity of conductance modulation provided by the ASAM, the MNIST handwritten digits recognition task, which is simulated using experimental data by constructing a convolutional neural network (CNN), can achieve a high accuracy of 93% after 150 epochs. Importantly, the design scheme of the stacked device structure with polarization effect holds significant promise for the fabrication of nitride-based memristors with highly linear and symmetrical conductance regulation. Such characteristics are crucial for the development of analog computing architecture, enabling more efficient and accurate neuromorphic systems.

中文翻译：

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基于三层 AlN/AlScN/AlN 堆叠忆阻器的人工突触，用于神经形态计算

神经形态设备因其彻底改变传统计算架构和推动人工神经系统进步的潜力而受到广泛关注。氮化铝基（AlN 基）忆阻器因其卓越的性能而特别引人注目，包括超快的开关速度、小开关电流、大的开/关比、可控的材料生长以及与互补金属氧化物半导体的兼容性。 CMOS）工艺。这些显着的特征对于新型神经形态装置的制造具有巨大意义，特别是对于人工突触。然而，AlN 忆阻器中常见的突然阻变行为对系统级人工神经网络 (ANN) 的识别精度提出了挑战。因此，实现具有多级电导的渐进开关行为对于人工突触来说变得非常理想。在这里，引入了一种界面工程方法，通过在 AlN 层内加入氮化铝钪 (AlScN) 层来优化 Ag/AlN/Pt 忆阻器。三层 AlN/AlScN/AlN 堆叠忆阻器 (ASAM) 在复位操作中表现出渐进开关行为的显着成就，这归因于 AlScN 层的铁电极化效应导致的突然导电丝形成的缓解。此外，ASAM 还具有出色的电阻开关性能，具有超快开关速度 (<5 ns)、低工作电压 (<0.5 V) 和低至 0.2 pJ 的超低功耗。通过适当调整电流顺应性和复位停止电压，ASAM表现出受控渐变开关和多级电导的显着特性。此外，ASAM 成功地模拟了生物突触功能，例如长时程增强 (LTP)、长时程抑制 (LTD)、配对脉冲促进 (PPF) 和尖峰时序依赖性可塑性 (STDP)。利用 ASAM 提供的近线性电导调制，通过构建卷积神经网络 (CNN) 使用实验数据模拟 MNIST 手写数字识别任务，在 150 个 epoch 后可以达到 93% 的高精度。重要的是，具有极化效应的堆叠器件结构的设计方案对于制造具有高度线性和对称电导调节的氮化物基忆阻器具有重要的前景。这些特性对于模拟计算架构的发展至关重要，可以实现更高效、更准确的神经形态系统。