公众号记得加星标⭐️，第一时间看推送不会错过。 韩国半导体工程师学会在其发布的《2026 年半导体技术路线图》中，公布了未来 15 年硅基半导体 技术的发展预测。三星近期才刚推出全球首款 2 纳米全环绕栅极（GAA）芯片 ——Exynos 2600， 而路线图预计，到 2040 年半导体电路制程将突破至 0.2 纳米，正式迈入埃米级（Å）技术时代。不 过，从当下到未来的 15 年间，行业仍需攻克诸多难题，实现 1 纳米以下晶圆制程的目标道阻且长。 据 ETNews 报道，该技术路线图的核心目标是助力提升半导体领域的长期技术与产业竞争力、推动 学术研究落地、完善人才培养体系。路线图重点聚焦九大核心技术方向，分别为：半导体器件与制造 工艺、人工智能半导体、光互连半导体、无线连接半导体传感器、有线连接半导体、功率集成电路模 块（PI M）、芯片封装技术以及量子计算。 据IT之家了解，目前，三星的 2 纳米 GAA 技术代表着全球光刻制程的最高水平。据悉，这家韩国科 技巨头已在规划该工艺的迭代升级方案：不仅完成了第二代 2 纳米 GAA 工艺节点的基础设计，还计 划在两年内落地第三代 2 纳米 GAA 技术 ——SF ...

点这里加关注，锁定更多原创内容 *免责声明：文章内容系作者个人观点，半导体芯闻转载仅为了传达一种不同的观点，不代表半导体芯闻对该 如果您希望可以时常见面，欢迎标星收藏哦~ 在 NAND 闪存领域，SK 海力士已研发出 321 层堆叠的 QLC 技术，而技术路线图预测，未来该 领域将实现 2000 层堆叠的 QLC NAND 闪存。此外，当前的人工智能处理器算力最高可达 10 TOPS（每秒万亿次运算），路线图预计，15 年后的 AI 芯片将实现算力大幅跃升：用于模型训练 的芯片算力可达 1000 TOPS，用于推理任务的芯片算力也将达到 100 TOPS。 （来源 ：IT之家 ） 观点赞同或支持，如果有任何异议，欢迎联系我们。 推荐阅读 10万亿，投向半导体 芯片巨头，市值大跌 黄仁勋：HBM是个技术奇迹 Jim Keller：RISC-V一定会胜出 全球市值最高的10家芯片公司 韩国半导体工程师学会在其发布的《2026 年半导体技术路线图》中，公布了未来 15 年硅基半导 体技术的发展预测。三星近期才刚推出全球首款 2 纳米全环绕栅极（GAA）芯片 ——Exynos 2600，而路线图预计，到 2040 年 ...

Core Viewpoint - Kioxia is experiencing a significant decline in profits despite the booming demand for memory driven by artificial intelligence, with a net profit drop of 62% year-on-year in Q2 FY2025, falling short of market expectations [2][3] Group 1: Financial Performance - Kioxia reported a net profit of 40.7 billion yen for Q2 FY2025, down 62% from the previous year [2] - The company's profit was below market expectations of 47.4 billion yen, following a 74% drop in the previous quarter [2] - Despite short-term challenges, Kioxia remains optimistic about future quarters, forecasting a revenue increase of 12% to 23% in Q3 FY2025, reaching between 500 billion to 550 billion yen [3] Group 2: Market Outlook - Kioxia predicts that NAND flash demand will exceed supply by 2025, with a bit growth rate of around 15% [3] - The company expects this growth rate to accelerate to over 10% by 2026 due to tightening supply [3] - Kioxia's 8th generation BiCS flash memory is anticipated to drive AI demand starting in early 2026 [3] Group 3: Industry Trends - Major NAND flash manufacturers, including Kioxia, are expected to cut production in the second half of 2025 to boost prices, as indicated by SanDisk [4][5] - NAND flash prices have been hovering around cost levels, with a potential increase of 20% to 30% being discussed among major suppliers [5] - Recent data shows NAND flash prices rose by 15% last quarter, with expectations of further increases of 40% to 50% in the coming months [5][6] Group 4: Technological Developments - The industry is shifting towards QLC NAND flash due to strong demand from AI data centers, with Kioxia and other manufacturers ramping up production [7] - SK Hynix plans to ship 321-layer QLC NAND products by the second half of 2026, while Samsung is increasing investments in QLC NAND flash [7]

Core Insights - The race to build data centers for achieving Artificial General Intelligence (AGI) is accelerating, outpacing manufacturing capacity, leading to significant shortages in DRAM and storage devices [2][3] - The delivery time for enterprise-grade hard drives has extended to two years, forcing companies to turn to QLC NAND flash SSDs to avoid backlogs [2] - The demand for QLC NAND flash is causing shortages, with North American and Chinese cloud service providers competing for supplies, potentially driving up global SSD prices [2][3] Summary by Sections - **AGI and Data Center Investment** - Companies are heavily investing in data centers to support AGI, resulting in a rapid increase in demand for memory and storage solutions [2] - **Current Market Conditions** - DRAM prices have more than doubled in recent months, and enterprise-grade hard drive delivery times have reached 24 months [2] - The shift towards QLC NAND flash SSDs is a response to the long delivery times of traditional storage solutions [2] - **Future Projections** - By early 2027, QLC NAND is expected to surpass TLC in market share, indicating a significant shift in storage technology [3] - NAND flash manufacturers are experiencing unprecedented demand, with some QLC production capacities already booked until 2026 [2][3] - **Impact on Consumers and Companies** - The current shortages are benefiting manufacturers as they sell capacity to AI customers willing to pay high prices, while ordinary consumers face electronic product shortages [3]

Core Insights - The AI semiconductor industry is expected to experience significant growth in 2026, with a shift in investment logic from upstream to downstream infrastructure [2][10] - The bottleneck in AI development has transitioned from chip manufacturing and packaging to downstream components such as data center space, power supply, and cooling systems [2][5] Upstream Capacity No Longer the Sole Bottleneck - Chip manufacturing and packaging have significantly expanded, alleviating previous supply concerns [4] - TSMC reported stronger-than-expected AI demand and a quick ramp-up in CoWoS capacity, indicating flexibility in the supply chain [4] - Despite ongoing tightness in advanced node wafer front-end capacity, AI semiconductors are prioritized over other applications like cryptocurrency ASICs [4] Bottleneck Shift - The current constraints are now focused on data center space, power availability, and supporting infrastructure, which have longer construction cycles than chip manufacturing [6] - The deployment of large-scale GPU clusters presents challenges in power consumption and heat dissipation, leading to a shift towards liquid cooling and high-voltage direct current (HVDC) solutions [6] Storage and Memory - AI workloads demand high-speed data storage and access, with companies like Meta opting for QLC NAND flash for cost efficiency [8] - The global demand for HBM (High Bandwidth Memory) is projected to surge, with NVIDIA expected to consume 54% of the total HBM by 2026 [8] Racks and Networking - OCP has introduced standardized blueprints for "AI Open Data Centers" and "AI Open Cluster Designs" to facilitate large-scale deployments [9] - Companies like Alibaba are focusing on pluggable optics for their cost-effectiveness and flexibility, while new technologies like CPO/NPO are gaining attention [9] Demand Forecast Indicates Explosive Growth for Downstream Components - Global cloud service capital expenditure is expected to grow by 31% in 2026, reaching $582 billion, significantly exceeding market expectations [11] - AI server capital expenditure could see approximately 70% year-over-year growth if its share in overall capital spending increases [11] AI Chip Demand Breakdown - NVIDIA is projected to dominate the CoWoS capacity consumption with a 59% share, followed by Broadcom, AMD, and AWS [12] - In AI computing wafer consumption, NVIDIA leads with a 55% share, followed by Google, AMD, and AWS [12] Investment Focus Shift - The signals from the OCP conference and industry data indicate a new direction for AI hardware investment, emphasizing the importance of downstream infrastructure [13] - Investors are encouraged to broaden their focus from individual chip companies to the entire data center ecosystem, identifying key players in power, cooling, storage, memory, and networking [13]

Core Insights - The core argument of the article is that the bottleneck in AI development has shifted from chip manufacturing and packaging to downstream infrastructure, including data center power supply, liquid cooling, high bandwidth memory (HBM), server racks, and optical modules [2][4][9]. Upstream Capacity Expansion - Chip manufacturing and packaging have significantly expanded, alleviating previous concerns about supply shortages [5][6]. - TSMC has reported strong AI demand and is working to close the supply-demand gap, with a lead time of only six months for expanding CoWoS capacity [6][9]. - The report predicts that global CoWoS demand will reach 1.154 million wafers by 2026, a 70% year-on-year increase, indicating a robust supply response [6][12]. Downstream Infrastructure Challenges - As chip supply is no longer the main issue, the focus has shifted to the availability of data center space, power, and supporting infrastructure, which have longer construction cycles than chip manufacturing [9][12]. - The deployment of large-scale GPU clusters presents significant challenges in power consumption and heat dissipation, leading to a preference for liquid cooling solutions and high-voltage direct current (HVDC) power supply systems [9][12]. - The demand for HBM is expected to explode, with global consumption projected to reach 26 billion GB by 2026, with NVIDIA alone accounting for 54% of this demand [9][12]. Investment Opportunities - The shift in focus towards downstream infrastructure opens new investment opportunities beyond traditional chip manufacturers, emphasizing the importance of companies that excel in power, cooling, storage, memory, and networking [12][13]. - Global cloud service capital expenditure is expected to grow by 31% to $582 billion by 2026, significantly higher than the market's general expectation of 16% [12]. - AI server capital expenditure could see approximately 70% year-on-year growth if AI servers' share of capital expenditure increases [12][13].

Core Insights - The core argument of the article is that the bottleneck in AI development has shifted from chip manufacturing and packaging to downstream infrastructure, including data center power, liquid cooling, HBM memory, racks, and optical modules [4][9][19] Group 1: Shifts in Industry Focus - The focus of the market has transitioned from TSMC's CoWoS packaging and advanced processes to downstream supply chain challenges [4][5] - Chip manufacturing and packaging have significantly expanded, alleviating previous supply concerns [5][6] - The demand for AI semiconductors is expected to grow robustly, with the global CoWoS demand projected to reach 1.154 million wafers by 2026, a 70% year-on-year increase [7][14] Group 2: Downstream Infrastructure Challenges - The new bottlenecks are centered around data center space, power supply, and supporting infrastructure, which have longer construction cycles than chip manufacturing [9][10] - The OCP conference highlighted the need for redesigning data centers to accommodate large-scale AI clusters, emphasizing power and cooling requirements [10][18] - The demand for HBM is expected to surge, with global consumption projected to reach 26 billion GB by 2026, where NVIDIA alone is expected to consume 54% [18] Group 3: Investment Opportunities - Investment opportunities are shifting from upstream wafer foundries and packaging to a broader downstream supply chain [4][19] - Companies with robust power and space resources in data centers will have a competitive edge in the AI computing race [4][19] - The report suggests that investors should broaden their focus from individual chip companies to the entire data center ecosystem, identifying key players in power, cooling, storage, memory, and networking [19]

Core Viewpoint - The article discusses the pricing and technology behind the new iPhone 17 Pro Max, particularly focusing on the controversial use of QLC (Quad-Level Cell) NAND flash storage in its highest configuration, which is priced at 17,999 RMB for 2TB, making it the most expensive iPhone ever [5][19]. Pricing Analysis - The starting price of the new iPhone is perceived as competitive, but the top-tier model's price has reached an unprecedented level, with the 2TB version costing 4,000 RMB more than the 1TB version [5][19]. - The article highlights the stark contrast in value, suggesting that the additional 4,000 RMB could buy several 2TB SSDs from other brands [5]. Technology Overview - The iPhone 17 Pro Max's 2TB storage option likely utilizes QLC NAND flash, which is a departure from the traditional use of multiple lower-capacity TLC chips [8][19]. - QLC technology allows for higher storage density but comes with trade-offs in terms of performance and longevity compared to SLC, MLC, and TLC [11][13]. NAND Flash Types - NAND flash types include SLC (Single-Level Cell), MLC (Multi-Level Cell), TLC (Triple-Level Cell), and QLC, with QLC storing the most bits per cell but having lower endurance and speed [11][15]. - QLC's lower endurance is highlighted, with a lifespan of approximately 1,000 write cycles compared to SLC's 100,000 cycles [15][16]. Performance Considerations - Despite concerns about QLC's performance, the article argues that for typical smartphone usage, the lifespan of QLC storage is sufficient, estimating that a 2TB QLC drive could last nearly 30 years under normal conditions [19][21]. - The use of SLC caching is mentioned as a method to mitigate QLC's slower write speeds, allowing for faster data handling in everyday use [23][24]. Market Implications - The adoption of QLC in smartphones may lead to more affordable high-capacity storage options in the future as supply chains mature [28]. - The article suggests that while consumers may initially be wary of QLC, its integration into devices like the iPhone could ultimately benefit the market by providing larger storage capacities at lower prices [28][29].

Core Viewpoint - The new iPhone 17 Pro Max features a top configuration with a staggering price of 17,999 RMB for the 2TB version, making it the most expensive iPhone ever, with a 4,000 RMB premium for the additional 1TB of storage compared to the 1TB version priced at 13,999 RMB [2][3]. Pricing and Storage - The iPhone 17 Pro Max's 2TB model is priced at 17,999 RMB, which is significantly higher than the 1TB model at 13,999 RMB, indicating a steep price increase for additional storage [2]. - The additional 1TB of storage in the 2TB model costs 4,000 RMB, raising questions about the value proposition given the cost of alternative storage solutions [2]. Technology and Specifications - The 2TB version likely utilizes QLC (Quad-Level Cell) NAND flash memory, which has raised concerns among users due to its lower performance and lifespan compared to other types of NAND flash [3][6]. - Apple has achieved a "world first" by using a single 2TB QLC flash chip instead of combining multiple lower-capacity chips, which is a departure from the practices of other manufacturers [6]. NAND Flash Memory Types - NAND flash memory types include SLC (Single-Level Cell), MLC (Multi-Level Cell), TLC (Triple-Level Cell), and QLC, with QLC being the least durable and slower due to its higher data density [8][12]. - QLC can store 4 bits of data per cell, which reduces costs but also leads to lower performance and lifespan, with QLC having a write endurance of only 1,000 cycles compared to SLC's 100,000 cycles [20][21]. Performance Considerations - Despite concerns about QLC's lifespan, calculations suggest that a 2TB QLC drive could theoretically last up to 30 years under typical usage conditions, as the total data written can be substantial [26][27]. - The use of SLC caching is a common method to mitigate the slower speeds of QLC, allowing for faster write operations by temporarily using part of the QLC storage as SLC [34][41]. Market Implications - The integration of QLC technology in smartphones may lead to more affordable options for consumers in the future as supply chains mature, potentially allowing for larger storage capacities at lower prices [45].