材料汇·2025-12-09 15:59Core Viewpoint - The article emphasizes the critical role of material innovation in driving the next generation of AI computing power, highlighting the shift from traditional silicon-based materials to advanced materials that can meet the increasing demands of AI applications [2][53]. Group 1: Key Materials for AI Computing - Advanced channel materials are essential for semiconductor transistors, directly influencing speed, power consumption, and integration [4]. - AI chips require channel materials with high mobility, high switching ratio, high stability, low power consumption, low leakage current, and ultra-thin thickness [6]. - Various materials such as MoS₂, black phosphorus, InGaAs, germanium, and carbon nanotubes are identified as promising candidates for next-generation AI chips, each with specific performance metrics [7][10][11][12][14]. Group 2: Gate and Dielectric Materials - Gate and dielectric materials are crucial for controlling the flow of current in transistors, affecting switching speed, power consumption, and reliability [17]. - Hafnium oxide (HfO₂) and its doped variants are highlighted for their low leakage currents and high dielectric constants, suitable for advanced logic chips [18][20][21]. Group 3: Substrate Materials - Substrate materials provide physical support and thermal management for semiconductor chips, impacting performance and reliability [23]. - Silicon carbide (SiC) and gallium oxide (β-Ga₂O₃) are noted for their high breakdown fields and thermal conductivity, making them suitable for AI power modules [24][25]. Group 4: Non-volatile Storage Materials - Phase change materials and resistive switching materials are identified for their potential in next-generation memory applications, offering high speed and low power consumption [26][27]. Group 5: Advanced Packaging and Integration Materials - Materials for substrate and interconnects, such as silicon photonic intermediates and glass substrates, are crucial for enhancing signal transmission speed and reducing power loss [29][30]. - Diamond-based thermal management materials are highlighted for their superior heat dissipation capabilities, essential for high-performance AI chips [32]. Group 6: New Computing Paradigms - Photonic computing materials, such as lithium niobate and silicon-based photonic materials, are discussed for their potential to significantly increase processing speed while reducing energy consumption [35][36]. - Quantum computing materials, including superconductors and diamond nitrogen-vacancy centers, are essential for developing quantum computing hardware [38][39]. Group 7: Investment Logic - The investment opportunity lies in material innovation that can replace traditional silicon technologies, aligning with national strategies for semiconductor supply chain security [53]. - Focus areas for investment include advanced logic and storage materials, packaging and thermal management materials, and frontier materials for emerging computing paradigms [54]. Group 8: Conclusion - The article presents a comprehensive overview of the material innovations driving the AI computing revolution, emphasizing the importance of these advancements for China's semiconductor industry and global competitiveness [56].