Generative models are increasingly used to propose designs, data, and control actions for physical systems, yet many such systems are governed by hard physical constraints rather than by perceptual plausibility. Semiconductor manufacturing provides a demanding test case: generated masks, layouts, synthetic defect data, and process recipes must obey lithography, transport, reaction, and device-physics constraints, because physically invalid samples are not merely low quality but unusable. This Perspective argues that semiconductor manufacturing exposes a broader computational-science challenge, namely that generative AI for constrained physical domains must be physics-informed by construction, not corrected only through post-hoc filtering. We survey the emerging architectural toolkit, including physics-informed diffusion, PDE-constrained variational models, neural-operator priors, and conservation-law-respecting generative networks, and show how it connects to differentiable lithography, TCAD, process simulation, and autonomous experimentation. We identify four integration patterns between generative models and physics-based simulators, and we propose a research agenda centered on physics-fidelity benchmarks, differentiable simulator infrastructure, and multimodal foundation models for physical design and manufacturing. The central claim is analytical rather than rhetorical: where physical validity is the binding criterion of success, architectures that enforce it by construction should be expected to outperform those that filter for it after the fact, and the fab is the setting where this distinction is sharpest.

翻译：生成模型越来越多地被用于为物理系统提出设计、数据和控制动作，然而这类系统往往受硬物理约束支配，而非感知层面的合理性。半导体制造提供了一个具有挑战性的测试案例：生成的掩模版、版图、合成缺陷数据和工艺配方必须符合光刻、传输、反应和器件物理约束，因为物理无效的样本不仅质量低下，而且完全不可用。本文认为，半导体制造揭示了一个更广泛的计算科学挑战：面向约束物理领域的生成式AI必须通过构建实现物理信息建模，而不能仅通过事后过滤进行修正。我们梳理了新兴的架构工具包，包括物理信息扩散、偏微分方程约束变分模型、神经算子先验以及守恒律尊重型生成网络，并展示了其如何与可微分光刻、TCAD、工艺仿真和自主实验相结合。我们识别出生成模型与基于物理的仿真器之间的四种集成模式，并提出以物理保真度基准、可微分仿真器基础设施以及面向物理设计与制造的多模态基础模型为核心的研究议程。核心论点具有分析性而非修辞性：在物理有效性被作为成功决定性标准的场景中，通过构建强制执行该标准的架构应预期优于事后过滤的架构，而晶圆厂正是这一区分最为鲜明的环境。