Core Viewpoint - The article discusses the advancements in semiconductor technology, particularly in AI applications, focusing on key trends such as advanced packaging, CPO technology, and cooling solutions to address performance and efficiency challenges in AI accelerators [2][3]. Advanced Packaging Technology - Advanced packaging is evolving through Chiplet technology and hybrid bonding to enhance AI processor performance. The shift from silicon interposers to silicon bridges and organic RDL is aimed at cost reduction, with a future transition to panel-level packaging expected by 2028-2029 [4][5]. - Hybrid bonding is crucial for improving performance by reducing the bonding area through enhanced alignment precision [5]. CPO Technology - CPO (Co-Packaged Optics) is identified as the next-generation connection technology for AI data center servers, effectively reducing power consumption in high-bandwidth scenarios. However, high costs and the complexity of precise assembly remain significant challenges [6]. - The introduction of next-generation 448Gb SerDes technology may increase CPO adoption, as it addresses signal degradation issues by minimizing transmission distances [6]. Client Device Packaging - In client devices, semiconductor manufacturers are carefully selecting between Chiplet and monolithic architectures based on cost and performance considerations. For instance, AMD's latest Radeon series GPU has integrated previously Chiplet-based SRAM into a monolithic design [7]. - Apple's Vision Pro features a Chiplet package with two high-bandwidth custom DRAM chips, showcasing the trend towards specialized high-performance processors [7]. Cooling Solutions - Traditional cooling methods like air and water cooling are becoming less effective due to increasing power density in AI accelerators. Two-phase liquid cooling is emerging as a key solution due to its high energy efficiency and broad applicability [3][9]. - Different cooling technologies are suited for varying thermal densities: air cooling for below 10W/cm², two-phase liquid cooling for 10-100W/cm², and water cooling for above 100W/cm². The next-generation 3nm AI data center GPUs are expected to have thermal densities around 100W/cm², making two-phase liquid cooling particularly relevant [10][11][12].