Reservoir computing (RC) has garnered considerable interest owing to its uncomplicated network structure and minimal training costs. Nevertheless, the computing capacity of RC systems is limited by the material‐dependent physical dynamics of reservoir devices. In this study, an efficient neuromorphic reservoir device with adjustable reservoir states, achieved through the development of an electrically tunable three‐terminal charge trap memory, is introduced. This device utilizes molybdenum disulfide (MoS2) as the channel material and a perhydropolysilazane‐based charge trap layer. Notably, the absence of a tunneling layer in the device structure enables dynamic resistive switching, characterized by outstanding endurance and an excellent memory window. Furthermore, by implementing a simple input decay and refresh scheme, a reconfigurable neuromorphic device capable of multiple feature extraction and functioning as an artificial synapse is developed. The device's efficacy is validated through device‐to‐system‐level simulations within a hardware‐based wide RC (WRC) system, resulting in an improved recognition rate in the MNIST hand‐written digit recognition task from 87.6% to 91.0%, a testament to the enhanced computing capacity. This strategic approach advances the development of hardware‐based WRC systems, marking a significant step toward energy‐efficient reservoir computing.

中文翻译：

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通过基于可重构 MoS2 的人工突触增强计算能力，具有双特征策略，用于宽储层计算


油藏计算 （RC） 由于其简单的网络结构和最低的训练成本而引起了相当大的兴趣。然而，RC 系统的计算能力受到储层装置与材料相关的物理动力学的限制。在这项研究中，介绍了一种通过开发电可调的三端电荷陷阱记忆实现的具有可调节储层状态的高效神经形态储库装置。该器件利用二硫化钼 （MoS2） 作为通道材料和基于全氢聚硅氮烷的电荷捕获层。值得注意的是，器件结构中没有隧穿层，可实现动态电阻开关，其特点是出色的耐用性和出色的存储窗口。此外，通过实现简单的输入衰减和刷新方案，开发了一种能够提取多个特征并用作人工突触的可重构神经形态器件。该设备的有效性通过在基于硬件的宽 RC （WRC） 系统内进行设备到系统级仿真来验证，从而将 MNIST 手写数字识别任务的识别率从 87.6% 提高到 91.0%，这证明了增强的计算能力。这种战略方法推动了基于硬件的 WRC 系统的发展，标志着向节能油藏计算迈出了重要一步。