Advancing Flexible Optoelectronic Synapses and Neurons with MXene-Integrated Polymeric Platforms

TL;DR

This paper reviews how MXene-based materials are being used to develop flexible, energy-efficient optoelectronic devices for brain-inspired computing.

Contribution

The paper provides a comprehensive review of MXene-integrated polymeric platforms for optoelectronic synapses and neurons, highlighting their potential for neuromorphic computing.

Findings

MXene-based devices show synergistic electrical-optical modulation for improved energy efficiency and multilevel plasticity.

MXene-enabled neurons demonstrate integrate-and-fire dynamics and spatiotemporal information integration.

Challenges remain in device stability and large-scale integration for practical neuromorphic systems.

Abstract

Neuromorphic computing, inspired by the human brain’s architecture, offers a transformative approach to overcoming the limitations of traditional von Neumann systems by enabling highly parallel, energy-efficient information processing. Among emerging materials, MXenes—a class of two-dimensional transition metal carbides and nitrides—have garnered significant attention due to their exceptional electrical conductivity, tunable surface chemistry, and mechanical flexibility. This review comprehensively examines recent advancements in MXene-based optoelectronic synapses and neurons, focusing on their structural properties, device architectures, and operational mechanisms. We emphasize synergistic electrical–optical modulation in memristive and transistor-based synaptic devices, enabling improved energy efficiency, multilevel plasticity, and fast response times. In parallel, MXene-enabled optoelectronic neurons demonstrate integrate-and-fire dynamics and spatiotemporal information integration crucial for biologically inspired neural computations. Furthermore, this review explores innovative neuromorphic hardware platforms that leverage multifunctional MXene devices to achieve programmable synaptic–neuronal switching, enhancing computational flexibility and scalability. Despite these promising developments, challenges remain in device stability, reproducibility, and large-scale integration. Addressing these gaps through advanced synthesis, defect engineering, and architectural innovation will be pivotal for realizing practical, low-power optoelectronic neuromorphic systems. This review thus provides a critical roadmap for advancing MXene-based materials and devices toward next-generation intelligent computing and adaptive sensory applications.

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