Small But Mighty: How is Nanotechnology Powering AI?

By Owais AliReviewed by Frances BriggsJul 30 2025

AI demands are pushing current hardware to its limits. Nanotechnology presents a faster, smaller, and smarter solution at the atomic scale.

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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.^1,2

Impact of Nanotechnology on AI

In-Memory Computing Architectures

One of the most promising avenues is in-memory computing (IMC), where data is processed directly where it's stored, rather than being shuffled between memory and processor. This eliminates the energy-intensive data transfers that characterize traditional architectures.

However, implementing IMC requires precise nanoscale engineering of memory devices and peripheral circuits.

Recent advances in halide perovskite nanomaterials highlight their suitability for IMC applications. A study published in Nature Nanotechnology demonstrated that two-dimensional halide perovskites exhibit mixed electronic-ionic conductivity at the nanoscale. These structures behave much like synapses, with conductance that can be fine-tuned in both directions.

They are superior to their three-dimensional analogs, with better air and moisture stability. Additionally, they provide linear and symmetric conductance modulation, which is essential for effective neural network training.

Because of their nanoscale fabrication, these devices have minimal grain boundary effects. This enables the uniform migration of halide vacancies, which results in their highly controllable synaptic responses.

Crossbar arrays constructed from these perovskite synapses attained computational precision within 0.08 % of theoretical performance limits, which is attributed to nanoscale optimization that promotes homogeneous ion distribution throughout the device structure.^1,3

Brain-Like Hardware

Nanotechnology is also helping engineers build chips that mimic the brain's responses. This field is known as neuromorphic computing. These nano-based, synaptic optoelectronic devices mitigate latency limitations inherent in conventional image processing architectures.

By integrating sensing and processing functions within a unified nanoscale framework, they establish neural-mimetic pathways that replicate the visual information processing efficiency of biological systems. In doing so, they enhance their performance in pattern recognition and autonomous navigation.

Zinc oxide nanodots, for example, can replicate how neurons 'forget' unused information, enabling adaptive responses essential for continuous learning and real-time decision-making. Meanwhile, carbon nanotube transistors paired with molybdenum disulfide replicate the spike-based signalling found in biological neurons for a different result. These transistors demonstrate low-power, brain-inspired circuits that can carry out complex pattern recognition with minimal energy input.² 

AI's Energy Problem

Training state-of-the-art AI models today can consume more electricity than dozens of households use in a year.

This is unsustainable, both in terms of expenses and environmental impact. Training requirements are doubling every 3.4 months, and computational infrastructure is struggling to keep up. By integrating nanotechnology, we may be able to mitigate these challenges. Energy-efficient nanoscale components could maintain performance while reducing power consumption.

The spiking neural networks mentioned above, for example, use nanomaterials to process information with tiny energy budgets. Some setups have achieved energy reductions of over 100,000 times those of conventional methods, without sacrificing accuracy. 

In speech recognition, systems using memristors made from tungsten, silicon dioxide, magnesium oxide, and molybdenum have hit 94 % recognition accuracy while using a fraction of the power of typical digital hardware.⁴

Advanced Memory and Storage Technologies

Nanotechnology enables revolutionary improvements in data storage density through quantum confinement effects and the unique properties of nanoscale materials.

Quantum dot storage systems, for example, use tiny semiconducting particles to store and retrieve data based on their energy states. This enables information encoding at previously unattainable densities while maintaining the rapid access capabilities essential for AI applications.

Emerging nanomaterials, such as titanium dioxide, graphene, and transition metal dichalcogenides (e.g., MoS₂, WS₂), are enabling the fabrication of memristors, nanoscale devices whose resistance changes depending on past activity, for information storage modulated by these electrical impulses.

Memristors made from materials like graphene or titanium dioxide can be packed into arrays dense enough to store up to 10 trillion bits per square inch. 

This degree of miniaturization enhances AI computational efficiency by enabling compact hardware that can manage extensive training datasets without relying on large-scale infrastructure.^4,5

Ultrafast Magnetic Switching Systems

Another advancement is in spintronics. Ultrafast magnetic switching represents a transformative advancement in memory technology, where devices can switch states far faster and with less energy than traditional components. 

One team, using extended-duration picosecond pulses with nano-photoconductive switches, achieved switching with just nine femtojoules of energy. These durations approach commercial instrument levels but far more efficiently. 

The researchers demonstrated that the energy required for spin-orbit torque switching decreases by more than an order of magnitude as pulse durations enter the picosecond regime, with projected switching energies as low as nine femtojoules for 100 × 100 nanometer ferrimagnetic devices.

Micromagnetic and macrospin simulations revealed a transition from non-coherent to coherent magnetization reversal, accompanied by substantial changes in magnetization dynamics at reduced pulse durations.

These findings emphasize the potential of ultrafast spin-orbit torque magnetic memories and reveal alternative magnetization reversal pathways at extremely fast timescales.⁶

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Thermal Management Through Nanoscale Engineering

Another environmental challenge with AI is its heat generation. Cooling powerful AI systems efficiently is particularly challenging. Conventional thermal interface materials (TIMs) often perform several orders of magnitude below theoretical predictions due to the interfacial resistance effects that limit heat transfer efficiency.

Nanotechnology can address these limitations by enabling precise control over material interfaces at the atomic and molecular scales. This can reduce interfacial thermal resistance and enhance heat conduction pathways.

By carefully layering materials to create “gradient heterointerfaces”, scientists have boosted heat transfer far beyond current standards. In one study, liquid-metal colloids made from Galinstan and aluminium nitride handled up to 2,760 watts of heat from a 16 cm² area. They can also cut electricity use by 65 % when paired with microchannel cooling systems.⁷

Experimentally, these colloidal TIMs exhibited thermal resistances ranging from 0.42 to 0.86 mm² K W^-1 within operational interfaces, surpassing leading thermal conductors by more than an order of magnitude. This enhanced performance results from the gradient heterointerface facilitating efficient thermal transport across liquid–solid boundaries and the pronounced colloidal thixotropy.

System-Level Integration and Future Prospects

Building AI systems with nanoscale components isn’t as simple as swapping out old parts for new ones. It requires fresh thinking about system design, new manufacturing techniques, and clever ways of integrating novel materials with today’s silicon-based infrastructure.

The continued advancement of these integrated systems relies on advancing the understanding of nanoscale device behavior, optimizing their interaction with conventional electronics, and developing scalable fabrication techniques to ensure reliable and efficient system performance.

References and Further Reading

1. Springer Nature. (2025). Nanotech powers on-chip intelligence. Nature Nanotechnology, 20(1), 1. https://doi.org/10.1038/s41565-025-01856-w
2. Olawade, D. B., et al. (2024). The synergy of artificial intelligence and nanotechnology towards advancing innovation and sustainability - A mini-review. Nano Trends, 8, 100052. https://doi.org/10.1016/j.nwnano.2024.100052
3. Kim, S. J., et al. (2024). Linearly programmable two-dimensional halide perovskite memristor arrays for neuromorphic computing. Nature Nanotechnology, 20(1), 83-92. https://doi.org/10.1038/s41565-024-01790-3
4. Tripathy, A., et al. (2023). Convergence of Nanotechnology and Machine Learning: The State of the Art, Challenges, and Perspectives. International Journal of Molecular Sciences, 25(22), 12368. https://doi.org/10.3390/ijms252212368
5. Lee, M., et al. (2023). Nanomaterial-Based Synaptic Optoelectronic Devices for In-Sensor Preprocessing of Image Data. ACS Omega, 8(6), 5209–5224. https://doi.org/10.1021/acsomega.3c00440
6. Díaz, E., et al. (2024). Energy-efficient picosecond spin–orbit torque magnetization switching in ferro- and ferrimagnetic films. Nature Nanotechnology, 20(1), 36-42. https://doi.org/10.1038/s41565-024-01788-x
7. Wu, K., et al. (2024). Mechanochemistry-mediated colloidal liquid metals for electronic device cooling at kilowatt levels. Nature Nanotechnology, 20(1), 104-111. https://doi.org/10.1038/s41565-024-01793-0

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