Advanced Thermal Management for High Power HPC, AI

Increasing power consumption and density of highperformance computing (HPC), artificial intelligence (AI), and automotive packages are driving the demand for innovative thermal management solutions. As devices become more powerful and compact, managing heat becomes critical to ensuring reliability and performance. There are several key challenges in large HPC and AI packages. Larger die sizes and higher I/O functionality demand improved thermal performance. Increased thermo-mechanical mismatch leads to warpage, complicating assembly processes and affecting overall thermal performance. Choosing the right bill of materials (BOM) is essential for effective thermal management. Metal-based thermal interface materials (TIMs) offer better thermal conductivity but introduce challenges in assembly and reliability. Thicker TIM layers increase bond line thickness (BLT), potentially degrading thermal performance. The design and material of the lid are crucial for thermal performance, impacting heat dissipation and overall package efficiency. Comprehensive experiments and simulations, including the design of experiments (DOE), are necessary to optimize package designs. Various test vehicles (TV) utilizing flip-chip packages with different TIMs, lid designs, and assembly processes are benchmarked against standard configurations. Advanced metal-based TIMs are being evaluated for their potential in high-power applications, focusing on the trade-offs between thermal performance, assembly, and reliability. This work demonstrates various high-thermal-interfacematerials' thermal and mechanical characterization data against current process-of-record data for the next-generation hypergrowth HPC and AI market. It also benchmarks cost vs. performance data.