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北京时间·🕒 发布 09-07 07:38:56·⟳ 抓取 09-07 07:49:38 +10m42s·⚡ 决策 09-15 12:44:53 +8d04h·42.1 天前
DigiTimes专访默克电子科技高管：AI加速半导体系统级创新，强调先进封装与材料长期需求
这是一篇行业趋势采访，核心内容是高管对AI、先进封装、材料与协同创新的长期定性判断；没有公司层面的新增订单、业绩指引、产品量产、客户确认或资本配置变化，无法形成短线事件交易edge。
📄 原文DigiTimes↗ 原文
AI is driving force behind 'Moore's Law on steroids,' says Merck's Benjamin Hein
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不做 Merck KGaA（默克）（MRK.DE）
高管行业观点采访无新增订单、指引、客户或产能事实，且消息已发布约8天；不具备事件驱动的正期望交易edge。
展开详情(成交明细 / 研究档案 / 原文全文 / 决策记录链)
研究档案
结论摘要
默克高管确认AI正提升材料、先进封装、量测和CPO的战略重要性，但报道未给出任何可归因于默克或具体供应链公司的新增商业事实。信号偏行业叙事且已发布约8天，不具备可执行的事件驱动交易价值。
🔎 新鲜度:最早出现 2026-09-07 · 扩散 medium · price-in 风险 high ↗
关键理由
- 无可支撑入场的公司级新增事实：没有订单金额、客户名称、收入指引、产能数据或量化需求承诺。
- 即使默克长期受益于AI半导体材料趋势，该采访也只重复行业共识和公司能力介绍，不能构成短线预期差。
主要风险
- 若后续默克披露具体AI材料订单、先进封装产能投资或上调电子业务指引，需要按新事件重新评估。
- 报道中的2030年1.6万亿美元市场预测未在正文中给出可核验机构来源，不应作为交易模型输入。
反向证据
- 做多前查反向 · 来源 web · 影响 invalidates
当前档案未显示该采访附带任何公司公告、订单或上调指引；正文本身也无量化利好，LONG论点不成立。
检索:Merck Electronics AI semiconductor materials order guidance advanced packaging 2026 Benjamin Hein 原文 ↗
先例如何影响了这次决策
对本次决策的影响:先例缺失与事件性质共同支持AVOID，未产生LONG或SHORT方向。
方向:未使用历史先例：当前事件不是订单、业绩、并购、政策或产品商业化等有明确公司财务影响的事件，而是泛行业高管访谈。
涨幅量级:无可校准的价格反应幅度，因为没有增量经营指标或具体交易催化。
形态:此类科技媒体高管采访通常更接近行业叙事和公关传播，缺少可验证的公司级surprise；不应据半导体板块历史订单行情外推涨幅。
工具执行结果 · 成功 1 · 降级 0 · 失败 0 · 取消 0
- search_processed_events 成功 stock_tools HTTP · 21 ms
{"result": "search_processed_events:agent query=`merck benjamin hein ai moore's law on steroids semiconductor materials | merck electronics ai semiconductor demand packaging interview 2026 | 默克 电子科技 hein ai 先进封装 材料 需求`；结构化索引覆盖完整 72 小时；旧记录兼容扫描近 0.0/72 小时、0 条本臂决策(完整)，返回 0 条候选。未命中只表示这些 agent query 表达式没
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AI is driving force behind 'Moore's Law on steroids,' says Merck's Benjamin Hein
Rebecca Kuo, Taipei
Sep 7, 2026, 07:38
0
Benjamin Hein, member of the executive board and CEO of the Electronics Business Sector at Merck. Credit: DIGITIMES
AI is driving a new wave of technological demand and changing how the semiconductor industry innovates, according to Benjamin Hein, member of the executive board and CEO of the Electronics Business Sector at Merck. As AI becomes an integral part of semiconductor innovation, future competitive advantages in the industry will depend less on any single process or material, but rather on the ability to combine materials, processes, packaging, metrology, and software into complete processes. In this sense, AI-driven growth in the semiconductor industry is like "Moore's Law on steroids," and collaboration will become the industry's next Moore's Law.
AI has accelerated the semiconductor industry's shift from optimizing components to optimizing systems, and highlighted the importance of integrating advanced packaging, materials, metrology, and processes. Hein notes that traditional computing dating back to roughly two decades ago focused primarily on the component level: improvements in CPU performance could help lift overall system performance. However, AI is driven by system-level requirements, with systems defining key performance indicators, so optimizations must target the entire technology stack.
'Moore's Law on steroids'
Improvements across different technological layers are now compounding to create a combined effect. As an example, the industry has turned its attention to the number of tokens generated per watt, and from a full-stack perspective, both cost and performance could improve by several times each year.
In contrast to the conventional Moore's Law that described the industry in the past, Hein is now seeing an accelerated version of Moore's Law: what matters is not the exact rate of speedup, but that the pace of technological progress is clearly higher than before, with innovation cycles shortening as well. Major product iterations might have arrived once every three to four years in the past, but new products or architectures may now appear in less than one or two years.
More crucially, AI is also helping develop the next generation of AI, since each step of the process—from chip design and materials discovery to R&D workflows—can be accelerated with AI. A self-reinforcing cycle could thus initiate in the industry, which explains why AI has advanced faster than many had expected.
In addition, Hein also points to new challenges in forecasting the semiconductor market. In the past, a rough estimate of market size could be drawn by simply aggregating demand plans from each company, but nowadays demand can grow severalfold in just a few months or quarters, making conventional forecasting less and less able to reflect actual market changes.
Some market forecasts now put global semiconductor output in 2030 at about US$1.6 trillion, up from earlier estimates of roughly US$1 trillion. Beyond factors such as memory pricing, the most fundamental driver remains the rapid AI-driven surge in demand, which means the semiconductor industry must step up innovation even further.
Importance of advanced packaging and materials
The importance of advanced packaging has risen as AI computing architectures grow ever more complex, says Hein. Over the past decade, optimizations in the industry centered primarily on the component level, but the focus is now shifting toward system-level integration. Different AI architectures require different chip combinations and integration methods, creating new demands for materials, processes, and integration capabilities.
Modern advanced computing systems need to integrate large numbers of chips, which may number in the thousands for some complex systems. The quality of each individual chip has thus become even more critical, since any problem in any part of the link could affect system performance and reliability.
Since the price of a high-end AI chip can rival that of a BMW or Mercedes-Benz vehicle, the importance of material quality, purity, and process stability has become more crucial than ever before. Rather than pointing the industry to new directions for transformation, Hein points out that AI is instead accelerating transformations that are already occurring.
In Merck's case, this has pushed the company to evolve from a materials supplier to become a partner in providing integrated process solutions. In addition to strengthening its materials capabilities, Merck is also building out vertical integration, material supply systems, metrology, and inspection technologies, aiming to move deeper into customers' processes and create higher value for the entire ecosystem.
Merck's materials business spans multiple major areas of chip production, supporting complete process flows using different materials and technological capabilities. In addition, the company is leveraging its 125 years of photonics expertise as it expands into optical interconnect and CPO applications.
In advanced packaging, Merck aims to bring the front-end process know-how, quality standards, and process control experience it has accumulated throughout the years into back-end manufacturing, for which requirements for materials, precision, and reliability are fast approaching front-end wafer manufacturing standards with the advancement of 2.5D and 3D integration.
According to Hein, Merck possesses material advantages in all six major areas of front-end processing, and can also provide products and solutions in several key advanced packaging segments, but it is not simply copying and pasting existing technologies into advanced packaging.
Wide array of capabilities
Adjustments must be made for different architectures, based on new process conditions and the needs for specific applications. Patterning, for example, was once used mainly in front-end processing, but has seen growing use in advanced packaging, where quality control can reach the ppb level. Even 2.5D packaging increasingly requires precision and reliability close to front-end manufacturing standards. AI is also changing R&D in materials, as innovation in materials research gains prominence with semiconductor processes moving toward 1.4nm processes and beyond.
Moreover, Hein notes that Merck's electronics business is backed by scientific capabilities accumulated through many years in the fields of life science and healthcare, allowing data and expertise from wide-ranging fields to be applied to materials discovery in the electronics business.
Merck has also set up AI and data centers at its headquarters in Germany, using AI for materials simulation and prediction. Current models can reach about 85% accuracy in predicting key material properties, allowing researchers to screen candidate materials first and then focus laboratory resources on the most promising ones, which has helped to reduce unnecessary experiments and speed up materials development.
The increasing complexity of systems nowadays means that optimization cannot be accomplished via any single material, technology, or even company alone, says Hein. Cross-sector collaboration is thus becoming the new "Moore's Law" in the semiconductor industry, and no single company can master all technologies on its own with the increasing pace of innovation. Going forward, innovation can only be accomplished by linking materials, equipment, chip manufacturing, packaging, metrology, software, and expertise between partners.
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translated by Kevin Wang and edited by Jack Wu
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Corning sees CPO and glass core as new semiconductor battlegrounds
Tomorrow's Headlines
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China optical transceivers avoid US FCC curbs as NPO and CPO compete in high-speed links
Tomorrow's Headlines
Sep 14, 18:46
China's AIDC token costs hinge on access to advanced GPUs
Tomorrow's Headlines
Sep 14, 18:46
Home
Tech
Semiconductors
AI is driving force behind 'Moore's Law on steroids,' says Merck's Benjamin Hein
Rebecca Kuo, Taipei
Sep 7, 2026, 07:38
0
Benjamin Hein, member of the executive board and CEO of the Electronics Business Sector at Merck. Credit: DIGITIMES
AI is driving a new wave of technological demand and changing how the semiconductor industry innovates, according to Benjamin Hein, member of the executive board and CEO of the Electronics Business Sector at Merck. As AI becomes an integral part of semiconductor innovation, future competitive advantages in the industry will depend less on any single process or material, but rather on the ability to combine materials, processes, packaging, metrology, and software into complete processes. In this sense, AI-driven growth in the semiconductor industry is like "Moore's Law on steroids," and collaboration will become the industry's next Moore's Law.
AI has accelerated the semiconductor industry's shift from optimizing components to optimizing systems, and highlighted the importance of integrating advanced packaging, materials, metrology, and processes. Hein notes that traditional computing dating back to roughly two decades ago focused primarily on the component level: improvements in CPU performance could help lift overall system performance. However, AI is driven by system-level requirements, with systems defining key performance indicators, so optimizations must target the entire technology stack.
'Moore's Law on steroids'
Improvements across different technological layers are now compounding to create a combined effect. As an example, the industry has turned its attention to the number of tokens generated per watt, and from a full-stack perspective, both cost and performance could improve by several times each year.
In contrast to the conventional Moore's Law that described the industry in the past, Hein is now seeing an accelerated version of Moore's Law: what matters is not the exact rate of speedup, but that the pace of technological progress is clearly higher than before, with innovation cycles shortening as well. Major product iterations might have arrived once every three to four years in the past, but new products or architectures may now appear in less than one or two years.
More crucially, AI is also helping develop the next generation of AI, since each step of the process—from chip design and materials discovery to R&D workflows—can be accelerated with AI. A self-reinforcing cycle could thus initiate in the industry, which explains why AI has advanced faster than many had expected.
In addition, Hein also points to new challenges in forecasting the semiconductor market. In the past, a rough estimate of market size could be drawn by simply aggregating demand plans from each company, but nowadays demand can grow severalfold in just a few months or quarters, making conventional forecasting less and less able to reflect actual market changes.
Some market forecasts now put global semiconductor output in 2030 at about US$1.6 trillion, up from earlier estimates of roughly US$1 trillion. Beyond factors such as memory pricing, the most fundamental driver remains the rapid AI-driven surge in demand, which means the semiconductor industry must step up innovation even further.
Importance of advanced packaging and materials
The importance of advanced packaging has risen as AI computing architectures grow ever more complex, says Hein. Over the past decade, optimizations in the industry centered primarily on the component level, but the focus is now shifting toward system-level integration. Different AI architectures require different chip combinations and integration methods, creating new demands for materials, processes, and integration capabilities.
Modern advanced computing systems need to integrate large numbers of chips, which may number in the thousands for some complex systems. The quality of each individual chip has thus become even more critical, since any problem in any part of the link could affect system performance and reliability.
Since the price of a high-end AI chip can rival that of a BMW or Mercedes-Benz vehicle, the importance of material quality, purity, and process stability has become more crucial than ever before. Rather than pointing the industry to new directions for transformation, Hein points out that AI is instead accelerating transformations that are already occurring.
In Merck's case, this has pushed the company to evolve from a materials supplier to become a partner in providing integrated process solutions. In addition to strengthening its materials capabilities, Merck is also building out vertical integration, material supply systems, metrology, and inspection technologies, aiming to move deeper into customers' processes and create higher value for the entire ecosystem.
Merck's materials business spans multiple major areas of chip production, supporting complete process flows using different materials and technological capabilities. In addition, the company is leveraging its 125 years of photonics expertise as it expands into optical interconnect and CPO applications.
In advanced packaging, Merck aims to bring the front-end process know-how, quality standards, and process control experience it has accumulated throughout the years into back-end manufacturing, for which requirements for materials, precision, and reliability are fast approaching front-end wafer manufacturing standards with the advancement of 2.5D and 3D integration.
According to Hein, Merck possesses material advantages in all six major areas of front-end processing, and can also provide products and solutions in several key advanced packaging segments, but it is not simply copying and pasting existing technologies into advanced packaging.
Wide array of capabilities
Adjustments must be made for different architectures, based on new process conditions and the needs for specific applications. Patterning, for example, was once used mainly in front-end processing, but has seen growing use in advanced packaging, where quality control can reach the ppb level. Even 2.5D packaging increasingly requires precision and reliability close to front-end manufacturing standards. AI is also changing R&D in materials, as innovation in materials research gains prominence with semiconductor processes moving toward 1.4nm processes and beyond.
Moreover, Hein notes that Merck's electronics business is backed by scientific capabilities accumulated through many years in the fields of life science and healthcare, allowing data and expertise from wide-ranging fields to be applied to materials discovery in the electronics business.
Merck has also set up AI and data centers at its headquarters in Germany, using AI for materials simulation and prediction. Current models can reach about 85% accuracy in predicting key material properties, allowing researchers to screen candidate materials first and then focus laboratory resources on the most promising ones, which has helped to reduce unnecessary experiments and speed up materials development.
The increasing complexity of systems nowadays means that optimization cannot be accomplished via any single material, technology, or even company alone, says Hein. Cross-sector collaboration is thus becoming the new "Moore's Law" in the semiconductor industry, and no single company can master all technologies on its own with the increasing pace of innovation. Going forward, innovation can only be accomplished by linking materials, equipment, chip manufacturing, packaging, metrology, software, and expertise between partners.
Article
translated by Kevin Wang and edited by Jack Wu
AI Search
Related stories
Merck makes 'AI for AI' its new semiconductor materials strategy
Merck ramps up AI semiconductor materials push as Level Up nears completion
Applied Materials CEO: AI reshapes semiconductor innovation
Applied Materials VP warns AI growth may hit energy, not chip limits
Commentary: Advanced AI packaging driving semiconductor industry toward 'virtual integration'
Show more
Categories
AI Chips + components IC design, distribution IC manufacturing IT + CE
Tags
demand market materials Merck metrology packaging performance
Share this article
Other links
Related topics
Global supply chain: Key components
Global supply chain: Emerging tech, businesses
Global supply chain: China market
BIZ FOCUS
Sep 14, 08:00
Hauman Unveils Micro LED Testing Breakthrough for AI Optical Interconnects
Monday 14 September 2026
Scaling Photonics: Assembly, Test and Fiber Automation at SEMICON Taiwan
Friday 11 September 2026
DAS Expands in Taiwan and Unveils World First at SEMICON
Friday 11 September 2026
Toward Technologies Redefines Relay Solutions at SEMICON Taiwan 2026
MOST-READ
7 DAYS NEWS
Google's v10 TPU opens door to AMD without sidelining Broadcom, MediaTek
Update: ABF substrate maker Unimicron searched in origin-labeling probe
Qualcomm's HBC play pulls Samsung and SK hynix into a new race beyond HBM
SK keyfoundry expands 8-inch capacity as China demand strains supply
Samsung, SK Hynix step up China NAND upgrades as US tool risks grow
Column: When digital trust breaks — quantum risk rewrites supply chain resilience
Wuliangye skips carmaking, turns liquor waste into silicon-carbon anodes
The 1,000-watt problem: a TSMC veteran on the brute force forging Nvidia's AI chips
eCloudvalley expands enterprise AI Agent portfolio
CXMT rides AI boom to 80% operating margin
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