As a promising candidate for high-density data storage and neuromorphic computing, cross-point memory arrays provide a platform to overcome the von Neumann bottleneck and accelerate neural network computation. In order to suppress the sneak-path current problem that limits their scalability and read accuracy, a two-terminal selector can be integrated at each cross-point to form the one-selector-one-memristor (1S1R) stack. In this work, we demonstrate a CuAg alloy-based, thermally stable and electroforming-free selector device with tunable threshold voltage and over 7 orders of magnitude ON/OFF ratio. A vertically stacked 64 × 64 1S1R cross-point array is further implemented by integrating the selector with SiO₂-based memristors. The 1S1R devices exhibit extremely low leakage currents and proper switching characteristics, which are suitable for both storage class memory and synaptic weight storage. Finally, a selector-based leaky integrate-and-fire neuron is designed and experimentally implemented, which expands the application prospect of CuAg alloy selectors from synapses to neurons.

作为高密度数据存储和神经形态计算的有前途的候选者，交叉点存储器阵列提供了一个平台来克服冯诺依曼瓶颈并加速神经网络计算。为了抑制限制其可扩展性和读取准确性的潜行路径电流问题，可以在每个交叉点集成一个双端选择器以形成单选择器单忆阻器 (1S1R) 堆栈。在这项工作中，我们展示了一种基于 CuAg 合金、热稳定且无电铸的选择器设备，具有可调阈值电压和超过 7 个数量级的开/关比。_{通过将选择器与 SiO 2}集成，进一步实现了垂直堆叠的 64 × 64 1S1R 交叉点阵列基于忆阻器。1S1R 器件具有极低的漏电流和适当的开关特性，适用于存储级内存和突触权重存储。最后，设计并实验实现了基于选择器的leaky integrate-and-fire神经元，拓展了CuAg合金选择器从突触到神经元的应用前景。