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Mini Review Volume 10 Issue 2
Industry trends, simulation-guided optimization, and hotspot-aware zoned cooling for high-power Artificial Intelligence (AI) chips
Ved Dwivedi, Balraj S. Mani, Nuggehalli M. Ravindra
Received: May 26, 2026 | Published: June 8, 2026
Citation: Dwivedi V, Mani BS, Ravindra NM. Industry trends, simulation-guided optimization, and hotspot-aware zoned cooling for high-power Artificial Intelligence (AI) chips. Material Sci & Eng Int J. 2026;10(2):69-79. DOI: 10.15406/mseij.2026.10.00270
Abstract
High-power artificial-intelligence accelerators require package and cooling designs that address both total heat load and spatial hotspots. A 700 W, 812.25 mm² (86.18 W/cm²) fivelayer die–TIM1–IHS–TIM2–cold-plate stack was evaluated in ANSYS Mechanical using idealized fixed-temperature and prescribed-convection boundaries. An initial analytical discrepancy was traced to a 0.21384 mm automatic contact tolerance that exceeded the 0.05 mm TIM layers and created nonphysical Die–IHS and IHS–cold-plate bypass contacts. Suppressing Contact Regions 5 and 3 restored the intended serial path, with four throughthickness divisions in each TIM. The corrected fine-mesh fixed-sink baseline produced Tmin = 32.221 °C, Tavg = 54.664 °C, Tmax = 78.998 °C, and a −700 W reaction. Increasing both TIM conductivities from 2 to 20 W/(m·K) reduced Tmax from 113.050 to 60.161 °C, while increasing TIM1 thickness from 0.02 to 0.10 mm raised Tmax from 70.287 to 95.944 °C. A combined tested configuration using k = 20 W/(m·K), a 3 mm IHS, and a 4 mm cold plate produced Tmax = 55.059 °C, 30.3% below the corrected baseline. At the same prescribed mean h = 5,000 W/(m²·K), hotspot-aware zoning reduced Tmax by 3.610 °C for the baseline package and 2.440 °C for the combined package while reducing temperature spread by 7.97% and 8.44%. The fine-to-refined-fine mesh-sensitivity difference in Tmax was 0.006 °C, although the full grid sequence was non-monotonic. The results constitute numerical screening and analytical benchmarking, not experimental validation; coolant channels, pressure drop, coolant heating, transient behavior, structural stress, and controller implementation were not modeled, and the GNN–GRU framework remains proposed.
Keywords: thermal management, AI accelerators, direct to chip liquid cooling, integrated heat spreader, ANSYS® mechanical, parameter sweep, hotspot-aware zoned cooling, graph neural network, gated recurrent unit, cyber-physical systems.
©2026 Dwivedi, et al. This is an open access article distributed under the terms of the, which permits unrestricted use, distribution, and build upon your work non-commercially.