The learning and inference efficiencies of an artificial neural network represented by a cross-point synaptic memristor array can be achieved using a selector, with high selectivity (Ion /Ioff ) and sufficient death region, stacked vertically on a synaptic memristor. This can prevent a sneak current in the memristor array. A selector with multiple jar-shaped conductive Cu filaments in the resistive switching layer is precisely fabricated by designing the Cu ion concentration depth profile of the CuGeSe layer as a filament source, TiN diffusion barrier layer, and Ge3 Se7 switching layer. The selector performs super-linear-threshold-switching with a selectivity of > 107 , death region of -0.70-0.65 V, holding time of 300 ns, switching speed of 25 ns, and endurance cycle of > 106 . In addition, the mechanism of switching is proven by the formation of conductive Cu filaments between the CuGeSe and Ge3 Se7 layers under a positive bias on the top Pt electrode and an automatic rupture of the filaments after the holding time. Particularly, a spiking deep neural network using the designed one-selector-one-memory cross-point array improves the Modified National Institute of Standards and Technology classification accuracy by ≈3.8% by eliminating the sneak current in the cross-point array during the inference process.
Keywords: deep neural networks; jar-shaped conductive Cu filaments; memristor arrays; super-linear threshold switching.
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