A memristor¹ has been proposed as an artificial synapse for emerging neuromorphic computing applications^2,3. To train a neural network in memristor arrays, changes in weight values in the form of device conductance should be distinct and uniform³. An electrochemical metallization (ECM) memory^4,5, typically based on silicon (Si), has demonstrated a good analogue switching capability^6,7 owing to the high mobility of metal ions in the Si switching medium⁸. However, the large stochasticity of the ion movement results in switching variability. Here we demonstrate a Si memristor with alloyed conduction channels that shows a stable and controllable device operation, which enables the large-scale implementation of crossbar arrays. The conduction channel is formed by conventional silver (Ag) as a primary mobile metal alloyed with silicidable copper (Cu) that stabilizes switching. In an optimal alloying ratio, Cu effectively regulates the Ag movement, which contributes to a substantial improvement in the spatial/temporal switching uniformity, a stable data retention over a large conductance range and a substantially enhanced programmed symmetry in analogue conductance states. This alloyed memristor allows the fabrication of large-scale crossbar arrays that feature a high device yield and accurate analogue programming capability. Thus, our discovery of an alloyed memristor is a key step paving the way beyond von Neumann computing.

忆阻器¹已被提出作为人工突触新兴的神经形态计算应用程序^2,3。要训​​练忆阻器阵列中的神经网络，以设备电导形式出现的重量值变化应清晰而统一³。通常基于硅（Si）的电化学金属化（ECM）存储器^4,5已显示出良好的模拟转换能力^6,7，这是因为金属离子在Si转换介质⁸中的迁移率很高。然而，离子运动的大随机性导致开关可变性。在这里，我们演示了具有合金化导电通道的Si忆阻器，该器件显示了稳定且可控的器件操作，从而可大规模实施交叉开关阵列。传导通道由传统的银（Ag）形成，它是与可稳定开关的可硅化铜（Cu）合金化的主要移动金属。在最佳合金化比例下，Cu有效地调节了Ag的运动，从而大大改善了空间/时间切换的均匀性，在较大的电导率范围内保持了稳定的数据，并且在模拟电导率状态下大大提高了编程的对称性。这种合金忆阻器允许制造具有高器件良率和精确模拟编程能力的大型纵横开关阵列。因此，我们发现合金忆阻器是超越冯·诺依曼计算的关键步骤。