A Nature Nanotechnology review says nanophotonics is pushing optical computing closer to practical AI use by identifying programmable, stable and densely integrated photonic platforms.
The paper argues AI’s rising speed and energy demands make light-based computing attractive because it offers high bandwidth, massive parallelism and lower power consumption than conventional approaches.
It highlights the main bottlenecks to scaling—materials properties, architecture design, nanofabrication and large-scale integration—and outlines nanotechnology fixes to improve speed, scalability and computational complexity.
Commercial and lab systems already point to that trajectory, the review says, citing examples from edge computing and scientific computing to large AI models, including a previously reported 160-TOPS/W photonic chiplet.
Could breakthroughs in nanophotonic computing finally solve AI's massive energy crisis, or will manufacturing costs keep these optical chips trapped in the lab?
Can advanced calibration algorithms truly overcome the complex synchronization and buffering bottlenecks that have historically hindered commercial optical computing?
How will the integration of exotic nanomaterials like lithium niobate reshape the environmental footprint and manufacturing pipelines of future data centers?