用AI和设计启发法实现3D光子芯片实时热管理
AI-Driven Thermal Mapping and Management in 3D Integrated Photonic Circuits
- 融合稀疏传感器数据与芯片设计信息，预测多层温度变化
- 通过局部材料与结构参数修正，热估准确率显著提升
- 无需大量训练数据，适合下一代3D光子系统设计
光子集成电路（PICs）正推动高性能计算、数据中心和传感技术发展，但三维（3D）PICs因高密度键合与异质材料带来严峻的热管理挑战。传统方法如热显微镜和封装内传感器仅提供稀疏数据，难以实现完整热分布可视化。本文提出一种双方法解决方案：基于AI的热建模框架结合设计启发式方法。AI方法融合稀疏传感器数据、设计层信息与密度特征，预测多层温度变化；启发式方法利用局部材料属性、布局、器件几何与传感器坐标，优化特定区域的热估计。通过插值传感器数据并基于设计区域对比分析调整局部热阻，生成2D热图。该方法在不依赖大量训练数据的前提下，显著提升热估准确性。二者协同，为下一代3D光子系统提供可扩展、高精度的实时热映射与设计期仿真方案。
原文摘要 · Abstract (English)
Photonic Integrated Circuits (PICs) are advancing high-performance computing, data centers, and sensing, yet three-dimensional (3D) PICs introduce critical thermal management challenges due to high-density bonding and heterogeneous materials. Traditional methods like thermal microscopes and in-package sensors yield sparse data, limiting full thermal profile visibility. This paper presents a dual-method solution combining an AI-driven thermal modeling framework with a design-based heuristic approach. The AI method integrates sparse sensor data with design layer and density information to predict multilayer temperature variations, while the heuristic approach uses localized material properties, design layout, component geometries, and sensor coordinates to refine thermal estimations in specific regions. A 2D thermal map of a 3D PIC is generated by interpolating sensor data and adjusting for local thermal resistivity using comparative analysis between design regions. The heuristic method complements the AI model, improving estimation accuracy without extensive training data. Together, these methods offer a scalable, accurate solution for real-time thermal mapping and design-time simulation, enabling reliable thermal management in next-generation 3D photonic systems.
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