Magnetoionics for Synaptic Devices and Neuromorphic Computing: Recent Advances, Challenges, and Future Perspectives.

Area of Science:

• Neuromorphic engineering
• Materials science
• Solid-state physics

Background:

• Traditional computing struggles with Big Data tasks like classification and pattern recognition.
• Software neural networks on conventional computers are inefficient due to separate memory and processing units.
• Existing brain-inspired computing methods often use electric currents, causing significant Joule heating.

Purpose of the Study:

• To review the use of magnetoionics in neuromorphic applications.
• To highlight energy-efficient alternatives to current-based computing methods.
• To discuss the modulation of synaptic weight using voltage-driven ion motion.

Main Methods:

• Reviewing magnetoionic control of magnetization via voltage-induced ion insertion/retrieval.
• Analyzing control of magnetic stripe domains and skyrmions in gated thin films.
• Examining integration with solid-state ionic supercapacitors for synaptic emulation.

Main Results:

• Magnetoionics provides an energy-efficient method for emulating synaptic functions like potentiation, depression, and plasticity.
• Voltage-driven ion motion can effectively modulate magnetic properties for neuromorphic applications.
• Novel approaches involve controlling magnetic domains and skyrmions using ionic gating.

Conclusions:

• Magnetoionics presents a promising, energy-efficient pathway for advancing neuromorphic computing.
• Further research into magnetoionic devices can overcome limitations of current-based approaches.
• This field holds significant potential for future brain-inspired computing technologies.