Alloying conducting channels for reliable neuromorphic computing

A memristor 1 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 uniform 3 . 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 medium 8 . 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. Alloying conduction channels of a Si memristor enables stable and controllable device operation with high switching uniformity.