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Memristors for neuromorphic computing systems: basic parameters and methods of their optimizationa Lomonosov Moscow State University, Faculty of Physics, Leninskie Gory 1 build. 2, Moscow, 119991, Russian Federation b National Research Centre ‘Kurchatov Institute’, pl. akad. Kurchatova 1, Moscow, 123182, Russian Federation c Moscow Institute of Physics and Technology (National Research University), Institutskii per. 9, Dolgoprudny, Moscow Region, 141701, Russian Federation In recent years, the scientific community has maintained substantial interest in neuromorphic computing systems based on memristive devices owing to their unique advantages. Although there is a wide variety of memristors, a device with optimal parameters for neuromorphic computing has not been developed yet. This article presents comprehensive overview of the principal types and parameters of memristive devices. Memristors operating through valence change, electrochemical metallization, phase change, ferroelectric tunneling, and spin-tunneling effects are considered. The key characteristics, benefits and limitations of each memristor type are analyzed. The main approaches to enhancing parameters of memristors are presented, including optimization of composition and structure of memristive devices, circuit architecture and algorithmic solutions. Typically, an English full text is available in about 1 month from the date of publication of the original article.
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