{"ID":2856389,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2510.11461","arxiv_id":"2510.11461","title":"Thermal Analysis of 3D GPU-Memory Architectures with Boron Nitride Interposer","abstract":"As artificial intelligence (AI) chips become more powerful, the thermal management capabilities of conventional silicon (Si) substrates become insufficient for 3D-stacked designs. This work integrates electrically insulative and thermally conductive hexagonal boron nitride (h-BN) interposers into AI chips for effective thermal management. Using COMSOL Multiphysics, the effects of High-Bandwidth Memory (HBM) distributions and thermal interface material configurations on heat dissipation and hotspot mitigation were studied. A 20 °C reduction in hot spots was achieved using h-BN interposers compared to Si interposers. Such an improvement could reduce AI chips' power leakage by 22% and significantly enhance their thermal performance.","short_abstract":"As artificial intelligence (AI) chips become more powerful, the thermal management capabilities of conventional silicon (Si) substrates become insufficient for 3D-stacked designs. This work integrates electrically insulative and thermally conductive hexagonal boron nitride (h-BN) interposers into AI chips for effective...","url_abs":"https://arxiv.org/abs/2510.11461","url_pdf":"https://arxiv.org/pdf/2510.11461v1","authors":"[\"Eric Han Wang\",\"Weijia Yan\",\"Ruihong Huang\"]","published":"2025-10-13T14:34:52Z","proceeding":"eess.SP","tasks":"[\"eess.SP\"]","methods":"[]","has_code":false}