Spintronic memristors for computing

TL;DR

This review explores spintronic memristors, highlighting their diverse state spaces and potential for advanced computing applications like neuromorphic and stochastic computing, while discussing current challenges and future trends.

Contribution

It provides a comprehensive survey of spintronic memristors, detailing various device types and their unique state dynamics for computing, which is a novel perspective in the field.

Findings

Spintronic memristors exhibit diverse state trajectories including steady, oscillatory, stochastic, and chaotic.

They are suitable for in-memory logic, neuromorphic, and stochastic computing applications.

Challenges remain in scaling and practical implementation of large systems.

Abstract

The ever-increasing amount of data from ubiquitous smart devices fosters data-centric and cognitive algorithms. Traditional digital computer systems have separate logic and memory units, resulting in a huge delay and energy cost for implementing these algorithms. Memristors are programmable resistors with a memory, providing a paradigm-shifting approach towards creating intelligent hardware systems to handle data-centric tasks. Spintronic nanodevices are promising choices as they are high-speed, low-power, highly scalable, robust, and capable of constructing dynamic complex systems. In this Review, we survey spintronic devices from a memristor point of view. We introduce spintronic memristors based on magnetic tunnel junctions, nanomagnet ensemble, domain walls, topological spin textures, and spin waves, which represent dramatically different state spaces. They can exhibit steady, oscillatory, stochastic, and chaotic trajectories in their state spaces, which have been exploited for in-memory logic, neuromorphic computing, stochastic and chaos computing. Finally, we discuss challenges and trends in realizing large-scale spintronic memristive systems for practical applications.