Memristors for neuromorphic computing systems: basic parameters and methods of their optimization

A.S. Ilin^†^a, A.N. Matsukatova^‡^a, b, M.N. Martyshov^§^a, A.V. Emelyanov^*^a, b, c, V.V. Rylkov^#^b, V.A. Demin^°^a, b, P.A. Forsh^&^a, P.K. Kashkarov^a, b, c, M.V. Kovalchuk^a, b, c
^aLomonosov Moscow State University, Faculty of Physics, Leninskie Gory 1 build. 2, Moscow, 119991, Russian Federation
^bNational Research Centre ‘Kurchatov Institute’, pl. Akademika Kurchatova 1, Moscow, 123182, Russian Federation
^cMoscow Institute of Physics and Technology (National Research University), Institutskii per. 9, Dolgoprudny, Moscow Region, 141700, 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.

Correspondence should be addressed to
Keywords: memristors, mechanisms of operation, type of memristors, characteristic optimization
PACS: 07.05.Mh, 87.18.Sn, 87.19.L− (all)
DOI: 10.3367/UFNe.2025.09.040037
URL: https://ufn.ru/en/articles/2026/4/b/
Web of Science

001764602900003
Scopus

2-s2.0-105047323440
ADS NASA

2026PhyU...69..344I
Citation: Ilin A S, Matsukatova A N, Martyshov M N, Emelyanov A V, Rylkov V V, Demin V A, Forsh P A, Kashkarov P K, Kovalchuk M V "Memristors for neuromorphic computing systems: basic parameters and methods of their optimization" Phys. Usp. 69 344–356 (2026)

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Received: 14th, July 2025, revised: 19th, September 2025, accepted: 21st, September 2025

Оригинал: Ильин А С, Мацукатова А Н, Мартышов М Н, Емельянов А В, Рыльков В В, Демин В А, Форш П А, Кашкаров П К, Ковальчук М В «Мемристоры для нейроморфных вычислительных систем: основные параметры и способы их оптимизации» УФН 196 369–382 (2026); DOI: 10.3367/UFNr.2025.09.040037

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