Thermal management is the discipline of moving waste heat out of AI compute hardware and the nanoscale materials it is built from, and it is now shifting from a downstream packaging concern to an architectural constraint that decides whether 3D chiplet stacks and co-packaged optics can scale at all.
Summary
Every joule delivered into a processor leaves as heat. As AI accelerators move from single dies to 2.5D and 3D chiplet assemblies, and as optics migrate from the board edge into the package, the heat has to escape through thinner, more thermally resistive, more crowded structures. “AI thermal management” is the cluster of techniques addressing this: package- and system-level heat extraction, thermal-aware physical design, and the materials science of heat conduction at nanometre scales where classical Fourier-law intuitions break down.
The work splits into three layers. At the system layer, the argument is architectural: co-packaged optics should be treated not as a component swap but as a commitment that reshapes the boundary between photonics, electronics and packaging, with thermal-aware co-design, standardisation and serviceability deciding whether it reaches volume deployment. At the design-methodology layer, high-resolution simulation is exposing errors in current practice: modelling chiplet stacks with uniform power maps underestimates peak temperature and hides the differences between frontside and backside power delivery, and backside power delivery in 3D configurations carries a pronounced thermal penalty under realistic localised workloads because lateral heat spreading is limited. Machine learning is entering the same layer, with multi-agent reinforcement learning used to trade wirelength against thermal design power in chiplet placement, two objectives that conflict directly.
The parameters that decide the field are: power density per unit die area and its spatial non-uniformity; through-stack and interfacial thermal resistance rather than bulk conductivity; the temperature ceiling of the most heat-sensitive component in the package, which for co-packaged optics is the laser and modulator rather than the logic; and whether design tools can price thermal cost early enough in the flow to change floorplans instead of only sizing heatsinks afterwards.
Evidence base
Open questions
- By how much do non-uniform power maps at 5 micrometre resolution change predicted peak temperature relative to uniform maps, and does that gap reverse the backside power delivery decision for any specific shipping 3D product?
- What is the actual Pareto improvement, in degrees and in wirelength, from multi-agent reinforcement learning placement against conventional weighted-sum optimisation on an industrial chiplet floorplan?
- Does the cooling benefit measured for hBN spreaders around gold nanoparticles in water survive translation to a dry, multilayer semiconductor stack where interfacial thermal conductance dominates?
- Which component sets the temperature ceiling in a co-packaged optics assembly, and can standardisation and serviceability requirements be met without giving back the energy-per-bit advantage that motivates moving optics into the package?
Assessment drafted 2026-08-31 from up to 18 KB sources using the technology-scorecard framework; scores are a draft read pending review.