Reservoir computing (RC), a variant of recurrent neural networks (RNNs), is well-known for its reduced energy consumption through exclusive focus on training the output weight and its superior performance in handling spatiotemporal information. Implementing these networks in hardware requires devices with superior fading memory behavior. Unlike filament-based two-terminal devices, those relying on ferroelectric switching demonstrate improved voltage reliability, while three-terminal transistors provide additional active control. HfO₂-based ferroelectric materials such as Hf_0.5Zr_0.5O₂ (HZO), have garnered attention for their scalability and seamless integration with CMOS technology. This study implements a RC hardware based on MoS₂–HZO integrated device structure with enhanced spontaneous polarization field. By adjusting the oxygen vacancy concentration, the devices exhibit consistent responses to both identical and nonidentical voltages, making them suitable for diverse RC applications. The high accuracy of MNIST handwritten digits recognition highlights the rich reservoir states of the traditional RC architecture. Additionally, the impact of masks on RC implementation is assessed, showcasing the device's capability for spatiotemporal signal analysis. This development paves the way for implementing energy-efficient and high-performance computing solutions.

中文翻译：

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用于储层计算系统的基于MoS2-HZO集成铁电场效应晶体管的物理储层

储层计算（RC）是循环神经网络（RNN）的一种变体，以其通过专注于训练输出权重来降低能耗以及在处理时空信息方面的卓越性能而闻名。在硬件中实现这些网络需要具有卓越的衰落存储行为的设备。与基于灯丝的两端器件不同，那些依靠铁电开关的器件表现出更高的电压可靠性，而三端晶体管则提供额外的主动控制。HfO ₂基铁电材料，例如Hf _0.5 Zr _0.5 O ₂ (HZO)，因其可扩展性以及与CMOS技术的无缝集成而受到关注。本研究实现了一种基于MoS ₂ –HZO集成器件结构的RC硬件，具有增强的自发极化场。通过调整氧空位浓度，这些器件对相同和不同的电压表现出一致的响应，使其适合各种 RC 应用。MNIST手写数字识别的高精度凸显了传统RC架构丰富的储层状态。此外，还评估了掩模对 RC 实现的影响，展示了该设备的时空信号分析能力。这一发展为实施节能和高性能计算解决方案铺平了道路。