---
id: 20260612-T0-14
title: "物理约束半导体生成AI：制造过程硬约束新方案"
title_en: "Physics-informed generative AI enforces semiconductor constraints"
url: https://ai.daily.yangsir.net/daily/20260612-T0-14
issue_date: 2026-06-12
publish_date: 2026-06-11T04:00:00.000Z
category: research
source_name: "arXiv cs.LG (ML)"
source_url: https://arxiv.org/abs/2606.11247
---

# 物理约束半导体生成AI：制造过程硬约束新方案

康奈尔大学提出物理约束生成模型，专为半导体制造设计。该方法直接将制造物理规则嵌入生成过程，而非依赖后验验证。实验显示，生成方案合格率提升40%，减少试错成本。适用于光刻、刻蚀等关键工艺，可加速芯片设计优化。

## English Version

**Physics-informed generative AI enforces semiconductor constraints**

Cornell researchers developed physics-informed generative AI for semiconductor manufacturing. The approach embeds physical constraints directly into generation, not post-hoc validation. Experiments show 40% higher yield rates and reduced trial costs. Applicable to photolithography and etching, accelerating chip design optimization.

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**来源**：[arXiv cs.LG (ML)](https://arxiv.org/abs/2606.11247)

**详情页**：https://ai.daily.yangsir.net/daily/20260612-T0-14

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