AnalogQuad: Learning Analog Circuit Representations through Template–Instance Neural-Symbolic Alignment

Analog circuit representations should support reuse across designs while retaining the behavior induced by device sizing and bias. Graph-centric pretraining captures topology, but it does not directly expose a structured small-signal symbolic behavior view that organizes gain, poles, zeros, and transfer functions; global graph–symbolic alignment may recognize a circuit family without resolving its different realizations. Unlike ordinary paired modalities, a parameterized symbolic record is instantiated with the sizing parameters in the netlist. We represent graph and symbolic views at template and sized-instance levels, $(G^T,S^T,G_{\theta }^I,S_{\theta }^I)$, with a sizing assignment $\theta$ linking the two levels. We introduce AnalogQuad, which combines sizing injection into graph node features and symbolic records with shared template–instance encoders and a hierarchical symbolic tower that preserves record-entry structure. A symmetric contrastive objective aligns graph–symbolic pairs through either sequential training on the two domains or joint training on mixed-domain batches. Experimental results demonstrate that template-level pretraining alone has limited transferability, whereas subsequent adaptation to sized instances consistently improves performance on downstream tasks.