Carbon nanotubes (CNTs) are revolutionizing neuromorphic computing by mimicking the synaptic plasticity of biological neurons. Recent breakthroughs have demonstrated CNT-based artificial synapses with switching speeds of <10 ns and energy consumption as low as 10 fJ per spike, outperforming traditional silicon-based devices by orders of magnitude. These properties enable the development of brain-inspired computing architectures capable of processing complex tasks like image recognition with >95% accuracy.
The integration of CNTs into memristive devices has shown remarkable progress in achieving multi-level resistance states (>16 levels), essential for analog computing paradigms. This is facilitated by the precise control of CNT chirality and diameter (0.8–2 nm), which directly influence electronic properties such as conductivity (>10⁵ S/cm). Such advancements pave the way for scalable neuromorphic systems with densities exceeding 10¹⁰ synapses/cm².
CNT-based neuromorphic devices also exhibit exceptional durability, with endurance cycles exceeding 10¹² without performance degradation. This is attributed to the mechanical robustness of CNTs (Young’s modulus ~1 TPa) and their resistance to electromigration, making them ideal for long-term operation in harsh environments like space or industrial automation systems.
Despite these advantages, challenges remain in achieving uniform CNT alignment over large areas (>1 cm²). Recent innovations in dielectrophoretic assembly techniques have improved alignment precision to <1°, enabling the fabrication of high-performance neuromorphic arrays with minimal variability (<5%). With continued progress in material synthesis and device engineering, CNTs are set to redefine the future of artificial intelligence hardware.
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