1nm，最新进展

Core Viewpoint - The semiconductor industry is witnessing intense competition among leading foundries like TSMC, Intel, and Samsung in the development of advanced 2nm and 1nm technologies, with TSMC planning to establish a 1nm fab in Taiwan to maintain its market leadership [1][6][7]. Group 1: Advanced Technology Development - ASML and Imec have formed a five-year partnership to enhance research capabilities for technologies below 2nm, focusing on integrating ASML's latest lithography tools into advanced semiconductor manufacturing [3][4]. - Imec will utilize ASML's advanced wafer fabrication equipment, including High-NA EUV tools, to accelerate the development of next-generation semiconductor production technologies [4][5]. - The cost of High-NA EUV systems can reach $350 million, posing a barrier for new entrants and researchers in the semiconductor field [4]. Group 2: TSMC's 1nm Fab Plans - TSMC is accelerating its 1nm technology development and plans to build a large Giga-Fab in Tainan, Taiwan, which will house six production lines for 1nm and 1.4nm chips [6][7]. - The new fab aims to outpace competitors like Samsung and Intel in the race to commercialize 1nm technology, which is critical for producing high-performance chips with lower power consumption [6][7]. Group 3: EUV Lithography Advancements - DNP has successfully developed the first generation of EUV masks required for 2nm and beyond, achieving fine pattern resolution necessary for advanced semiconductor manufacturing [9][10]. - The development of High-NA EUV masks is crucial for achieving the required precision for 2nm and smaller nodes, with DNP aiming for mass production of these masks by FY2027 [10]. Group 4: Future Roadmap and Challenges - Imec's roadmap for transistor technology includes advancements from FinFET to GAA (Gate-All-Around) designs, with expectations for CFET (Complementary FET) and atomic channel transistors to emerge by 2032 [12][13]. - The semiconductor industry faces challenges in meeting the growing demand for computational power, particularly for machine learning and AI applications, which require rapid advancements in transistor density and performance [14][17]. - Innovations in interconnect technologies and materials, such as the potential use of graphene, are being explored to overcome scaling challenges in semiconductor manufacturing [18][19].