Super‐Linear‐Threshold‐Switching Selector with Multiple Jar‐Shaped Cu‐Filaments in the Amorphous Ge3Se7 Resistive Switching Layer in a Cross‐Point Synaptic Memristor Array

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 i...

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Published in:Advanced materials (Weinheim) 2022-10, Vol.34 (40), p.n/a
Main Authors: Kim, Hea‐Jee, Woo, Dae‐Seong, Jin, Soo‐Min, Kwon, Hyo‐Jun, Kwon, Ki‐Hyun, Kim, Dong‐Won, Park, Dong‐Hyun, Kim, Dong‐Eon, Jin, Hong‐Uk, Choi, Hyun‐Do, Shim, Tae‐Hun, Park, Jea‐Gun
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Language:English
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Summary: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 Ge3Se7 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 Ge3Se7 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. An artificial neural network consisting of a hardware‐based cross‐point synaptic memristor array should employ selectors with two‐terminal electrodes to prevent an undesired sneak current and to improve learning and inference efficiencies. Here, a highly reliable super‐linear‐threshold‐switching selector with multiple jar‐shaped Cu‐filaments in the amorphous Ge3Se7 resistive switching layer by controlling the Cu ion concentration depth profile is developed.
ISSN:0935-9648
1521-4095
DOI:10.1002/adma.202203643