对先进芯片的需求不断增加。 建造这些设施的过程越来越复杂。 而误差的幅度正在缩小。
对于工程师来说，理解一个过程变化将如何表现，一个材料将如何与一个新的步骤相互作用，一个缺陷图回溯到一个根源是如何是半导体制造中的关键，具有挑战性的步骤——这些分析可能需要数天时间。 法布线上的物理实验缓慢且昂贵。 对产量或加工质量的错误要求会产生实际后果。
AI正在改造半导体制造，影响工程师能做的——以及多快。
加速计算缩短模拟时间，使工程师在承诺进行物理试验前能够评价更多的替代品。
基于物理的AI帮助工程师预测过程行为，而不是每次都进行完整的模拟。
AI agents处理一度耗时的工程任务序列。
而数码双胞胎让团队在进行物理试验之前，在虚拟环境中测试变化。
这些能力一起描述了AI定义的fab：一个半导体制造工厂，AI帮助工程师优化工艺，更早地抓住问题，并在整个制造芯片生命周期做出更好的决定。 工程师根据自己的专长指导工作并作出决定，同时减少等待答案的时间。
更短的模拟循环改变工程师可以探索的东西
加速计算并不只是加快个人计算
多物理学模拟模型在物理效应之间的相互作用，如热传导，电动行为和机械应力。
利用NVIDIA cuDSS库，多物理学软件提供商COMSOL正在加速这种半导体制造模拟最多10x. 这有助于工程师优化设备和流程，比较替代品，并决定哪些变化进行物理测试。
应用材料和NVIDIA正在合作加速原子尺度材料的发现和工艺开发。 通过集成NVIDIA cuDSS,Applied的Ginestra模拟平台运行速度可达10x，快于只使用CPU的方法。 并且与NVIDIA PhysicsNeMo一起，应用的ACE+多物理学平台提供高达35x的地形模拟更快。
有些情况下，加速度并不只是缩短计算时间，而是使其完全得以实现。 计算法——它定义了电路图案如何印刷到硅上——涉及计算过于密集，无法在生产规模的CPU上完成。 Siemens正在将NVIDIA cuLitho集成到其卡利布雷立体法软件中，在关键模拟工作量上提供多达17x加速。 那个扩展工程师在打印之前可以核实
因此，更快的计算可以帮助研究人员探索影响下一代半导体过程的物质行为和反应途径。
在不进行每次模拟的情况下预测进程行为
除了加速模拟外，接受物理训练的AI还可以预测材料和过程将如何表现——而不是每次进行完整的模拟。
回报率在复杂的计算中最为明显，即使在加速硬件上也是昂贵的。
Plasma——半导体牵引工艺中使用的电荷气体——就是一个例子。 建模等离子体行为需要解析等式，这些等式可以捕捉电气，化学和热过程的相互作用，使重复模拟的成本很高。 Lam Research显示，NVIDIA PhysicsNeMo框架可以在毫秒内预测等离子体行为，与全面模拟的天数相比——让工程师可以更快地探索过程条件而无需每次重运行完整的模型。
Agents 处理决定之间的步骤
工程师们把分析变成决定——修改设计，调查测试失败，调整生产过程等等。
AI agents可以帮助执行这些决定之间的步骤。
评估哪一种AI模型适用于特定任务就是一个例子：一个可以消耗试验层时数的连续的手工过程。
Advantest正在使用NVIDIA NeMo Switchyard AI agents实现该选择的自动化，将响应时间从小时减少到分钟。
测试厂前更改他们到了线上
数字双胞胎让团队模拟虚拟环境中的设备配置，布局和处理变化，然后承诺进行物理试验。 这可以降低实验成本，特别是因为fabs变得更加复杂，错误变化的利害关系也变得更加严重。
应用材料使用NVIDIA Omniverse库来构建整个fabs的物理精确数字双胞胎。 团队可以模拟材料运动，识别瓶颈并评价布局和操作策略——将虚拟实验扩展到单个过程以外的工厂整体工作方式。
连接循环
半导体制造的未来在于将这些能力连接起来：利用生产数据为模型和模型提供参考，以评价变化，然后将这些发现带回工程师作出生产决定。
AI帮助人们学习每个生产周期
探索NVIDIA如何与芯片整个生命周期的半导体领导者合作。
本文由 AI图鉴自动抓取并翻译，版权归原作者所有；如需查看原文请点下方链接。
Demand for advanced chips is rising. The processes to build them are getting more complex. And the margin for error is shrinking.
For engineers, understanding how a process change will behave, how a material will interact with a new step, how a defect maps back to a root cause are critical, challenging steps in semiconductor manufacturing — and these analyses can take days. Physical experiments on a fab line are slow and expensive. A wrong call on yield or process quality has real consequences.
AI is transforming semiconductor manufacturing, impacting what engineers can do — and how fast.
Accelerated computing shortens simulation time, allowing engineers to evaluate more alternatives before committing to physical trials.
Physics-based AI helps engineers predict process behavior without running a full simulation every time.
AI agents handle sequences of engineering tasks that once consumed hours.
And digital twins let teams test changes in a virtual environment before committing to physical trials.
Together, these capabilities describe an AI-defined fab: a semiconductor manufacturing factory where AI helps engineers optimize processes, catch problems earlier and make better decisions across the full manufacturing chip lifecycle. Engineers direct the work and make decisions based on their expertise while spending less time waiting for answers.
Shorter Simulation Cycles Change What Engineers Can Explore
Accelerated computing doesn’t just speed up individual calculations — it changes how many alternatives engineers can test before committing.
Multiphysics simulations model interactions among physical effects, such as heat transfer, electrical behavior and mechanical stress.
Using the NVIDIA cuDSS library, multiphysics software provider COMSOL is accelerating such simulations for semiconductor manufacturing by up to 10x. This helps engineers optimize equipment and processes, compare alternatives and decide which changes to test physically.
Applied Materials and NVIDIA are collaborating to accelerate atomic-scale materials discovery and process development. By integrating NVIDIA cuDSS, Applied’s Ginestra simulation platform runs up to 10x faster than CPU-only approaches. And with NVIDIA PhysicsNeMo, Applied’s ACE+ multiphysics platform delivers topography simulations up to 35x faster.
In some cases, acceleration doesn’t just shorten the calculation — it enables it altogether. Computational lithography — which defines how circuit patterns are printed onto silicon — involves calculations too intensive to complete on CPU at production scale. Siemens is integrating NVIDIA cuLitho into its Calibre lithography software, delivering up to 17x acceleration on key simulation workloads. That expands what engineers can verify before a wafer goes to print.
As such, faster calculations can help researchers explore material behavior and reaction pathways that influence next-generation semiconductor processes.
Predicting Process Behavior Without Running Every Simulation
In addition to accelerating simulations, AI trained on physics can predict how materials and processes will behave — without running a full simulation each time.
The payoff is most visible in complex calculations that are expensive even on accelerated hardware.
Plasma — the electrically charged gas used in semiconductor etch processes — is one example. Modeling plasma behavior requires solving equations that capture how electrical, chemical and thermal processes interact, making repeated simulation costly. Lam Research demonstrated that the NVIDIA PhysicsNeMo framework can predict plasma behavior in milliseconds, compared with days for a full simulation — giving engineers a faster way to explore process conditions without rerunning the full model each time.
Agents Handle the Steps Between Decisions
Engineers turn analyses into decisions — revising a design, investigating a test failure, adjusting a production process and more.
AI agents can help carry out the steps between those decisions.
Evaluating which AI model to apply to a given task is one example: a sequential, manual process that can consume hours on the test floor.
Advantest is using NVIDIA NeMo Switchyard AI agents to automate that selection, reducing response times from hours to minutes.
Testing Factory Changes Before They Reach the Line
Digital twins let teams simulate equipment configurations, layouts and process changes in a virtual environment before committing to physical trials. This reduces experimentation costs, especially as fabs become more complex and the stakes of a wrong change get higher.
Applied Materials uses NVIDIA Omniverse libraries to build physically accurate digital twins of entire fabs. Teams can simulate material movement, identify bottlenecks and evaluate layouts and operating strategies — extending virtual experimentation beyond individual processes to how the factory works as a whole.
The Connected Loop
The future of semiconductor manufacturing lies in connecting these capabilities: using production data to inform models and models to evaluate changes, then bringing those findings back to the engineers making production decisions.
That’s the promise of the AI-defined fab: where AI helps people learn from each production cycle and act on that knowledge sooner.
Explore how NVIDIA is working with semiconductor leaders across the chip lifecycle.
Auto-fetched and translated by AI Atlas. All rights belong to the original author.