The rapid growth of artificial intelligence (AI) is driving unprecedented demand for computational power, energy efficiency, and scalable hardware architectures. However, conventional silicon-based electronics are approaching fundamental physical and economic limits, constraining further improvements in performance and efficiency. High‑purity semiconducting single‑walled carbon nanotubes (s‑SWCNTs) have emerged as a promising materials platform capable of overcoming these barriers and enabling a new generation of AI hardware.
This talk explores how advances in the synthesis and purification of s‑SWCNTs unlock their potential as a replacement or complement to silicon in transistor technologies. Unlike traditional bulk semiconductors, s‑SWCNTs exhibit exceptional carrier mobility, one‑dimensional transport, and nanoscale dimensions, making them ideal for high‑speed, low‑power field‑effect transistors (FETs). Crucially, achieving high semiconducting purity eliminates metallic nanotube pathways that otherwise degrade switching performance, enabling high on/off ratios and reliable large‑scale integration. These properties position s‑SWCNT devices as strong candidates for extending Moore’s Law in the “more‑than‑Moore” era.
Beyond conventional logic, the unique electrical and structural characteristics of s‑SWCNTs open pathways toward novel computing paradigms tailored for AI workloads. The talk will highlight their application in reconfigurable electronics, non‑volatile memory, and neuromorphic systems, where computation and memory can be co‑located to minimize data movement, now a key bottleneck in modern AI accelerators. Recent demonstrations of s‑SWCNT‑based ferroelectric transistors and hybrid device architectures suggest the feasibility of energy‑efficient in‑memory computing and brain‑inspired processing systems.
Additionally, the compatibility of nanotube electronics with flexible, printable, and low‑temperature fabrication methods introduces opportunities for distributed and edge AI applications. This includes embedding intelligence into wearable devices, sensors, and Internet‑of‑Things (IoT) platforms, expanding AI beyond traditional data centers into ubiquitous environments.
The presentation will conclude by addressing key challenges, including scalable purification techniques, device uniformity, and integration with existing semiconductor infrastructure. By bridging materials science, nanoelectronics, and AI system design, high‑purity s‑SWCNTs offer a compelling pathway toward sustainable, high‑performance AI technologies. This work underscores the critical role of advanced nanomaterials in shaping the future of intelligent computing systems.
