An Analysis of Components and Enhancement Strategies for Advancing Memristive Neural Networks.

Advancements in artificial intelligence (AI) and big data have highlighted the limitations of traditional von Neumann architectures, such as excessive power consumption and limited performance improvement with increasing parameter numbers. These challenges are significant for edge devices requiring higher energy and area efficiency. Recently, many reports on memristor-based neural networks (Mem-NN) using resistive switching memory have shown efficient computing performance with a low power requirement.

Multiphase Reset Induced Reliable Dual-Mode Resistance Switching of the Ta/HfO/RuO Memristor.

Higher functionality should be achieved within the device-level switching characteristics to secure the operational possibility of mixed-signal data processing within a memristive crossbar array. This work investigated electroforming-free Ta/HfO/RuO resistive switching devices for digital- and analog-type applications through various structural and electrical analyses. The multiphase reset behavior, induced by the conducting filament modulation and oxygen vacancy generation (annihilation) in the HfO layer by interacting with the Ta (RuO) electrode, was utilized for the switching mode change.