An Improved Memristor Model and Applications

Memristors are promising and useful electronic elements with good memory and switching properties. Due to their nano-sizes, low energy consumption and good compatibility to CMOS high-density integrated chips, memristors could be utilized in neural networks, memory crossbars, and many other electronic schemes. In this work, an improved and simple, fast-functioning model of metal-oxide memristors is offered. Its corresponding LTSPICE library model is generated and applied for analysis of memory crossbars. The suggested model successfully operates at high-frequency signals, expressing the main fingerprints of memristors. Its appropriate operation in complex memristor-based electronic devices and circuits is established.