The limitations of conventional semiconductor technology have become increasingly apparent as AI applications require exponentially larger computational resources. Once the engines of rapid technological advances, silicon-based transistors are now encountering fundamental physical constraints at the nanoscale that inhibit further scaling and performance enhancement. Moore’s law, which predicted the doubling of transistors on a chip every two years, is running out of space.

On top of that, the breakdown of Dennard scaling, which once enabled simultaneous improvements in speed, power efficiency, and density, has further intensified the need for alternative materials and device architectures capable of sustaining AI-driven workloads.

This is where nanotechnology comes in. Working on a nanoscale offers a pathway to overcome the constraints of conventional tech, enabling the precise manipulation of materials at the atomic and molecular levels, typically within the one to 100 nanometer range.

At this minute scale, materials exhibit unique physical, chemical, and electrical characteristics. These small-scale properties can enable faster operation, lower energy consumption, and can be used to deliver complex functionalities within a single nanoscale architecture.

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Discover how nanotechnology is advancing AI with energy-efficient chips, in-memory computing, neuromorphic hardware, and nanoscale data storage solutions.