Hybrid Memory Device (Memory/Selector) with Scalable and Simple Structure for XNOR‐Based Neural Network Applications

Abstract

This study investigates the electrical behavior of a hybrid memory device with both memory and selector characteristics. Electrical measurements and simulations indicate that the electrical behaviors (nonvolatile characteristics in Al2O3 layers and volatile characteristics in TiO2 layers) are linked to the stability of the conductive filament (CF) used. The binding energy between the Ag atoms in the CF is crucial for achieving nonvolatile or volatile characteristics. Thus, the hybrid memory device exhibits tunable threshold‐voltage characteristics and a consistent off‐state without requiring an additional selector device. Furthermore, the buffer metal layer between the active electrode and oxide layer affects the filament‐formation process in terms of the switching time. Experimental results show that the buffer layer significantly affects ion motion, such as redox reactions and ion migration. Thus, hybrid memory devices with a Zr buffer layer can solve the voltage–time dilemma, enabling fast and low‐voltage switching. Robust XNOR‐based neural network applications are developed using hybrid memory devices in a cross‐point array with characteristics such as scalability, simple structure, and excellent switching characteristics. By carefully considering the on–off ratio and device variability, hybrid memory devices can ensure reliable operation with a high pattern recognition accuracy in XNOR‐based neural neuromorphic hardware systems.