布局背后的代码：面向模拟版图生成器的 AI 智能体研究
The Code Behind the Layout: AI Agents for Analog Layout Generators
AI 导读
arXiv 论文提出用 LLM 辅助模拟版图自动化，在 SKY130 工艺上对 gLayout 生成器框架做受控实验，发现模型直接绘制版图的主要障碍是时间与 token 成本，而基于生成器框架的核心优势是可复用性：一次经验证的修改可通过参数作用于整个版图家族。论文还用家族测试结果监督本地模型，以更低推理成本学到跨家族成立的修改。
正文 · 原文
Abstract:Analog layout remains largely manual and hard to automate. Generator frameworks such as gLayout express layouts as programs, producing layouts from parameterized, design-rule-aware generators; however, writing such generators still takes expert effort. Large language models (LLMs) could now write generator code, and frontier models can even draw layout geometry from design intent. This raises a practical question: if a model can draw the layout itself, what does a generator framework still contribute, and is applying these models to a generator framework better than having them draw a layout from scratch? Through controlled experiments on gLayout in SKY130, we find that the main obstacle to drawing a layout directly is its time and token cost, and that the main advantage of a model building on a generator framework is reusability. Its verified primitives keep each model edit small enough to check, and its parameters allow a single edit to be evaluated across an entire family of layouts. Accordingly, a model is most effective when it develops the layout from the generator source of the family that the design intent belongs to, and tests each edit on the family members most likely to fail, so that one verified change improves every layout the generator produces. The results of these family tests can then serve as supervision for a local model, which learns edits that hold across the family at a fraction of the inference cost.
| Subjects: | Hardware Architecture (cs.AR); Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2610.38426 [cs.AR] |
| (or arXiv:2610.38426v1 [cs.AR] for this version) | |
| https://doi.org/10.48550/arXiv.2610.38426 arXiv-issued DOI via DataCite |
Submission history
From: Minxing Chu [view email]
[v1]
Wed, 28 Oct 2026 08:10:12 UTC (242 KB)
来源：arXiv cs.AI · arxiv.org