Core Viewpoint - The semiconductor industry faces unprecedented challenges in power delivery and thermal management due to the increasing complexity and power demands of AI workloads, necessitating innovative design and manufacturing approaches [1][2][20]. Power Delivery Challenges - AI-specific chips are pushing transistor density to new limits, leading to significant power demands, with NVIDIA's Blackwell consuming between 700W to 1400W [1]. - Dynamic power consumption, primarily influenced by data movement between memory and computation units, dominates power usage, creating design constraints from memory hierarchy decisions to power delivery networks [1][2]. Thermal Management Issues - The transition to 3D stacking and localized heat generation complicates thermal dissipation, increasing challenges like electromigration and localized hotspots [2]. - Advanced packaging techniques are essential for effective thermal management, with materials like indium alloy TIM being effective due to their high thermal conductivity [8]. Vertical Power Delivery Innovations - The semiconductor industry is exploring vertical power delivery techniques to overcome limitations of traditional horizontal power delivery, which suffers from significant power loss and overheating [4]. - By embedding power rails directly beneath chips, vertical delivery reduces voltage drop and noise while freeing up space for critical signal transmission [4][5]. Material Innovations - Molybdenum is emerging as a key alternative to tungsten and copper for interconnects, offering lower contact resistance and better performance in densely packed chip designs [11][12]. - The shift to molybdenum aligns with industry efforts to mitigate electromigration risks associated with high current densities in AI workloads [12][13]. Backside Power Delivery Networks (BSPDN) - BSPDN represents a transformative shift in chip architecture, separating power and signal routing to enhance efficiency and layout flexibility [15][16]. - This approach allows for dual-side cooling strategies, although it introduces new challenges in terms of mechanical reliability and yield optimization [16]. System-Level Design Optimization - The integration of power delivery, thermal distribution, and mechanical stress modeling is becoming crucial for next-generation AI chips, requiring collaboration across design teams [18][19]. - Enhancing power delivery efficiency directly correlates with reduced heat generation and cooling costs, which is vital for large-scale data centers [20]. Conclusion - The future of AI chip power delivery will require deep interdisciplinary collaboration, with innovations like BSPDN, molybdenum interconnects, and vertical integration paving the way for improved performance and scalability [20].