Analysis of Memristor-Based Neural Networks and Logic Circuits for Artificial Intelligence Using Standard and Improved Memristor Models

Memristors are state-of-the-art electronic elements with nano sizes, about 3 nm dimensions, with very good nano-second switching and memory properties, low power usage of about 100 µW, and good compatibility with the current technology of CMOS-integrated chips and circuits. These components are potentially applicable in T-byte memory arrays, artificial neural networks, logic gates and many other digital and analog electronic schemes and devices for artificial intelligence. This paper presents the application of some simple and fast-operating modified memristor models with activation thresholds in neural networks and logic circuits. MATLAB ver. 2016a and LTSPICE ver. XVII products are used for the analysis of memristor neural nets and logical circuits for artificial intelligence. Several simple, accurate and fast-operating existing modified memristor models, together with several frequently used standard memristor models, are utilized for the associated analyses and simulations. A comparison between the used memristor models is conducted. The considered memristor models are tuned, using experimentally recorded i-v relations of tungsten-sulfide Knowm memristors. An accurate functioning of the analyzed neural nets and logic functions is confirmed by the derived results. The considered modified memristor models, neural networks and logic schemes are important in modeling and analysis of memristor-based circuits for ultra-high-density artificial intelligence-integrated chips.