AnalogRetriever：跨模态表示学习助力模拟电路检索

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Abstract: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 22.0\% to 99.97\%. 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.

Comments:	ICCAD Camera Ready Version, Code and data: \url{this https URL}
Subjects:	Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
Cite as:	arXiv:2604.23195 [cs.CV]
	(or arXiv:2604.23195v2 [cs.CV] for this version)
	https://doi.org/10.48550/arXiv.2604.23195 arXiv-issued DOI via DataCite

Submission history

From: Lei Li [view email]
[v1] Sat, 25 Apr 2026 08:01:14 UTC (1,871 KB)
[v2] Tue, 10 Nov 2026 07:04:09 UTC (1,501 KB)