Super‐Linear‐Threshold‐Switching Selector with Multiple Jar‐Shaped Cu‐Filaments in the Amorphous Ge<sub>3</sub>Se<sub>7</sub> Resistive Switching Layer in a Cross‐Point Synaptic Memristor Array
Hea‐Jee Kim, Dae‐Seong Woo, Soo‐Min Jin, Hyo‐Jun Kwon, Kihyun Kwon, Dong‐Won Kim, Dong‐Hyun Park, Dong‐Eon Kim, Hong‐Uk Jin, Hyun‐Do Choi, Tae‐Hun Shim, Jea‐Gun Park
Abstract
Abstract 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 ( I on / I off ) 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 Ge 3 Se 7 switching layer. The selector performs super‐linear‐threshold‐switching with a selectivity of > 10 7 , death region of −0.70–0.65 V, holding time of 300 ns, switching speed of 25 ns, and endurance cycle of > 10 6 . In addition, the mechanism of switching is proven by the formation of conductive Cu filaments between the CuGeSe and Ge 3 Se 7 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.