Flexible Neuromorphic Memristors: From Mechanisms to Applications

Area of Science:

• Materials Science
• Neuroscience
• Computer Engineering

Background:

• The von Neumann architecture's memory-processor separation limits data-intensive applications.
• Neuromorphic computing, inspired by the brain, offers low-power in-memory computing solutions.
• Flexible memristors emulate synapses and enable wearable electronics.

Purpose of the Study:

• To systematically review flexible neuromorphic memristors.
• To cover materials, switching mechanisms, device architectures, and fabrication.
• To discuss applications and future directions in AI and bio-integrated systems.

Main Methods:

• Systematic literature review of flexible neuromorphic memristors.
• Categorization of materials (natural, synthetic, inorganic) and switching mechanisms (conductive filaments, interface effects, ferroelectricity, phase change).
• Analysis of device architectures (sandwich, crossbar, fiber-based) and fabrication techniques.

Main Results:

• Flexible memristors utilize diverse switching mechanisms and materials for tunable conductance.
• Various device architectures and low-temperature fabrication methods are employed.
• Recent advances show promise in neuromorphic computing, biomimetic sensing, and wearable systems.

Conclusions:

• Flexible neuromorphic memristors are crucial for next-generation AI and edge computing.
• Bridging material innovation and system integration is key for development.
• Future work should address mechanical stability and explore self-healing materials.