用多模态学习提升模拟电路检索准确率，解决跨格式查找难题。
AnalogRetriever: Learning Cross-Modal Representations for Analog Circuit Retrieval
- 三模态融合：用视觉语言模型和图网络分别处理原理图、描述和网表。
- 跨模态召回率达75.2%，显著优于现有方法。
- 适合芯片设计人员快速复用已有电路，加速模拟电路开发。
模拟电路设计高度依赖知识产权（IP）的复用，但跨异构表示（如SPICE网表、原理图和功能描述）的搜索仍具挑战。现有方法多局限于单一模态的精确匹配，无法捕捉跨模态语义关联。为此，我们提出AnalogRetriever，一个统一的三模态检索框架。基于Masala-CHAI构建高质量数据集，通过两阶段修复流程将网表编译成功率从22%提升至100%。在此基础上，AnalogRetriever利用视觉语言模型编码原理图与文本描述，使用端口感知的关系图卷积网络编码网表，通过课程对比学习将三者映射到共享嵌入空间。实验表明，AnalogRetriever在六种跨模态检索方向上平均Recall@1达75.2%，显著优于基线方法。将其集成至AnalogCoder智能体框架作为检索增强生成模块后，可持续提升功能通过率，并实现此前无法完成的任务。代码与数据集将公开。
原文摘要 · Abstract (English)
Analog circuit design relies heavily on reusing existing intellectual property (IP), yet searching across heterogeneous representations such as SPICE netlists, schematics, and functional descriptions remains challenging. Existing methods are largely limited to exact matching within a single modality, failing to capture cross-modal semantic relationships. To bridge this gap, we present AnalogRetriever, a unified tri-modal retrieval framework for analog circuit search. We first build a high-quality dataset on top of Masala-CHAI through a two-stage repair pipeline that raises the netlist compile rate from 22\% to 100\%. Built on this foundation, AnalogRetriever encodes schematics and descriptions with a vision-language model and netlists with a port-aware relational graph convolutional network, mapping all three modalities into a shared embedding space via curriculum contrastive learning. Experiments show that AnalogRetriever achieves an average Recall@1 of 75.2\% across all six cross-modal retrieval directions, significantly outperforming existing baselines. When integrated into the AnalogCoder agentic framework as a retrieval-augmented generation module, it consistently improves functional pass rates and enables previously unsolved tasks to be completed. Our code and dataset will be released.
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