Predictive Structure to Thermal Conductivity Modeling Framework for BEOL Interconnect Stacks in Advanced Technology Nodes Enabled by Extensive Layer Resolved Thermal Measurements

Abstract

The increasing structural complexity of BEOL interconnect stacks in advanced integrated circuits demands a structure-aware thermal conductivity (\k{appa}) modeling framework. However, generalizable models derived from layer-resolved thermal measurements that quantitatively capture the dependence of \k{appa} on interconnect structures remain lacking, limiting predictive thermal analysis. Here, we establish an experimentally derived, structure-aware \k{appa} modeling framework enabled by time-domain thermoreflectance measurements with ~100 nm depth resolution. Statistical analysis of a compiled dataset comprising over 40 experimentally measured layer-resolved \k{appa} values across diverse BEOL layers reveals a generalizable empirical structure-to-\k{appa} relationship, enabling predictive modeling based on interconnect structure. An intra-layer three-dimensional \k{appa} distribution model based on effective medium theory and realistic layouts further resolves spatial \k{appa} variations within practical interconnect layers. Together, these models establish an experimentally derived, structure-aware \k{appa} modeling framework for predictive and generalizable thermal analysis of advanced interconnect stacks and 3D ICs.