Neuromorphic Memristive Chips: Design and Technology
Authors:   Saint Petersburg Electrotechnical University ETU LETI, St. Petersburg, 197022, Russian Federation, Andreeva N.V. , Luchinin V.V. ,   Saint Petersburg Electrotechnical University ETU LETI, St. Petersburg, 197022, Russian Federation, Ryndin E.A. ,   Saint Petersburg Electrotechnical University ETU LETI, St. Petersburg, 197022, Russian Federation, Anchkov M.G. ,   Saint Petersburg Electrotechnical University ETU LETI, St. Petersburg, 197022, Russian Federation, Romanov A.A. ,   Saint Petersburg Electrotechnical University ETU LETI, St. Petersburg, 197022, Russian Federation, Chigirev D.A. ,   Saint Petersburg Electrotechnical University ETU LETI, St. Petersburg, 197022, Russian Federation, Mazing D.C. ,   Saint Petersburg Electrotechnical University ETU LETI, St. Petersburg, 197022, Russian Federation, Gerasimova M.I. ,   Saint Petersburg Electrotechnical University ETU LETI, St. Petersburg, 197022, Russian Federation, Sevostyanov E.N. ,   Saint Petersburg Electrotechnical University ETU LETI, St. Petersburg, 197022, Russian Federation, Trushlyakova V.V. ,   Saint Petersburg Electrotechnical University ETU LETI, St. Petersburg, 197022, Russian Federation, Demin Y.A. ,   Saint Petersburg Electrotechnical University ETU LETI, St. Petersburg, 197022, Russian Federation
Published: December 2021

Memristive neuromorphic chips exploit a prospective class of novel functional materials (memristors) to deploy a new architecture of spiking neural networks for developing basic blocks of brain-like systems. Memristor-based neuromorphic hardware solutions for multi-agent systems are considered as challenges in frontier areas of chip design for fast and energy-efficient computing. As functional materials, metal oxide thin films with resistive switching and memory effects (memristive structures) are recognized as a potential elemental base for new components of neuromorphic engineering, enabling a combination of both data storage and processing in a single unit. A key design issue in this case is a hardware defined functionality of neural networks. The gradient change of resistive properties of memristive elements and its non-volatile memory behavior ensure the possibility of spiking neural network organization with unsupervised learning through hardware implementation of basic synaptic mechanisms, such as Hebb's learning rules including spike — timing dependent plasticity, long-term potentiation and depression. This paper provides an overview of scientific researches carrying out at Saint Petersburg Electrotechnical University "LETI" since 2014 in the field of novel electronic components for neuromorphic hardware solutions of brain-like chip design. Among the most promising concepts developed by ETU "LETI" are: the design of metal-insulator-metal structures exhibiting multilevel resistive switching (gradient tuning of resistive properties and bipolar resistive switching are combined together in a sin¬gle memristive element) for further use as artificial synaptic devices in neuromorphic chips; computing schemes for spatio-temporal pattern recognition based on spiking neural network architecture implementation; breadboard models of analogue circuits of hardware implementation of neuromorphic blocks for brain-like system developing.