面向先进技术节点BEOL互连堆栈的预测性结构-热导率建模框架：基于广泛层分辨热测量
Predictive Structure to Thermal Conductivity Modeling Framework for BEOL Interconnect Stacks in Advanced Technology Nodes Enabled by Extensive Layer Resolved Thermal Measurements
- School of Integrated Circuits, Peking University（北京大学集成电路学院）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
该研究通过时间域热反射测量和统计建模，建立了结构感知的热导率预测框架，实现了对先进BEOL互连堆栈的通用热分析。
AI中文摘要：
先进集成电路中BEOL互连堆栈的结构复杂性日益增加，这要求一种结构感知的热导率（\k{appa}）建模框架。然而，目前仍缺乏从层分辨热测量中推导出的、能够定量捕捉\k{appa}对互连结构依赖性的通用模型，这限制了预测性热分析。在此，我们建立了一个实验推导的、结构感知的\k{appa}建模框架，该框架依托于具有约100纳米深度分辨率的时间域热反射测量。对包含超过40个实验测量的、跨越不同BEOL层的层分辨\k{appa}值的编译数据集进行统计分析，揭示了一个通用的经验性结构-\k{appa}关系，从而能够基于互连结构进行预测性建模。基于有效介质理论和实际布局的层内三维\k{appa}分布模型进一步解析了实际互连层内的空间\k{appa}变化。这些模型共同建立了一个实验推导的、结构感知的\k{appa}建模框架，用于先进互连堆栈和3D IC的预测性和通用热分析。
英文摘要：
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.
