Core Viewpoint - The introduction of Backside Power Delivery Network (BSPDN) by major semiconductor companies like Intel and TSMC is a significant advancement in semiconductor technology, aimed at addressing the limitations of traditional chip designs and extending Moore's Law [2][4]. Group 1: What is Backside Power Delivery? - BSPDN is considered a breakthrough that continues Moore's Law, improving heat dissipation, reducing IR drop, and increasing chip density [4]. - Traditional chip designs concentrate power and signal lines on the front of the wafer, which becomes problematic as advanced processes approach 2nm and below [5]. Group 2: Importance of Backside Power Delivery - Reduces voltage drop and power loss, ensuring stable power supply during high-speed AI computations and server applications [6]. - Addresses thermal bottlenecks and IR drop issues caused by lengthy circuits, which can lead to operational errors or performance degradation [7]. - Enhances performance by separating power and signal, thereby reducing interference [8]. Group 3: Global Strategies for Backside Power Delivery - Three main solutions are currently being developed: imec's Buried Power Rail, Intel's PowerVia, and TSMC's Super Power Rail [10]. - imec is a leader in BSPDN technology, having published its findings in collaboration with Arm in 2022, utilizing BPR and nTSV architecture [11]. - Intel plans to implement BSPDN in its 18A process, expected to enter mass production in late 2025, focusing on complete separation of power and signal [11]. - Samsung will introduce BSPDN technology in its SF2Z process, with mass production anticipated in 2027 [12]. - TSMC's approach involves using Super Power Rail to direct power to the front transistors, which is crucial for maintaining its competitive edge in advanced processes [13]. Group 4: Implications for the Semiconductor Industry - BSPDN is seen as a key technology for extending Moore's Law, especially as traditional methods of shrinking transistors face limitations [15]. - The competition among major players to mature and commercialize this technology will determine their influence in the semiconductor industry over the next decade [13].

Core Viewpoint - The rapid development of artificial intelligence (AI) is pushing the limits of traditional computing technologies, necessitating sustainable and energy-efficient solutions for exponential scaling of parallel computing systems [1][2][30]. Group 1: Technological Advancements - The article emphasizes the importance of optimizing the entire system from software and system architecture to silicon and packaging to maximize performance, power consumption, and cost [2]. - Key technologies such as RibbonFET and PowerVia are highlighted for their potential to enhance performance and efficiency in semiconductor design [4][5]. - High NA EUV technology is noted for its ability to simplify electronic design automation (EDA) and improve yield and reliability [7][8]. Group 2: 3D Integration and Packaging - 3D Integrated Circuits (3DIC) are crucial for achieving higher computational power in smaller areas while reducing energy consumption [11]. - The need for advanced packaging techniques to enhance interconnect density and energy efficiency is discussed, with a focus on modular design environments [12][15]. - The integration of glass in packaging to scale interconnect geometries and improve power transmission efficiency is identified as a significant technological advancement [14]. Group 3: Power Delivery and Efficiency - The article discusses the increasing power demands for AI workloads and the limitations of traditional motherboard voltage regulators (MBVR) [21][22]. - Fully Integrated Voltage Regulators (FIVR) are proposed as a solution to improve power conversion efficiency by bringing voltage regulation closer to the chip [23][24]. - The potential of pairing high-voltage switch-capacitor voltage regulators with low-voltage integrated voltage regulators for enhanced power density and efficiency is explored [24]. Group 4: Software and Ecosystem Collaboration - Software is deemed a critical component of the innovation matrix, requiring collaboration within the open-source ecosystem to enhance security and streamline processes [25]. - The need for industry-wide collaboration to develop next-generation advanced computing systems is emphasized, ensuring alignment with market demands and sustainability [28]. Group 5: Industry Challenges and Opportunities - The article outlines the challenges faced in achieving exponential performance improvements for AI, including power, connectivity, and cost issues [30]. - It calls for innovative approaches across various domains, including process technology, 3DIC system design, and power delivery, to meet the industry's computational demands [30].