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

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 (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.

Memristor-Based Artificial Chips

“…For the nanodevices and nanotechnology, the memristor-based nanodevice primitives should be optimized to meet the AI application requirements of computing chips, and developing integration technology is beneficial to the design of future large-scale computing chips. Through integration with a high-density memristor-based crossbar synapse array, future computing chips should be more efficient in terms of area and should reach a much larger integration scale. − In the future development direction, it is expected that there will be general brain-like chips based on memristors, which will have a unified architecture design based on the most advanced technology. − At the same time, these universal chips should achieve high energy efficiency while achieving computational accuracy which surpasses that of traditional chips.…”