Autoencoder-Augmented Graph Neural Networks for Accurate and Scalable Structure Recognition in Analog/Mixed-Signal Schematics

The increasing complexity of Analog/Mixed-Signal (AMS) schematics has been posing significant challenges in structure recognition, particularly in the intellectual property (IP) industry, where data scarcity and confidentiality constraints limit model training. In this work, a novel framework has been proposed that combines the generative augmentation capabilities of convolutional Autoencoders with the structural analysis power of Graph Convolutional Networks (GCNs). Realistic schematic variants have been synthesized from limited proprietary data to enhance model generalization, while the GCN has been used to capture topological features critical to substructure recognition. The method has been validated on a curated AMS dataset, where it surpassed a GCN-only baseline by reducing reconstruction error and achieving a balanced classification accuracy of 96.7%, thereby exceeding the long-standing 95% accuracy threshold. Inference latency was measured at 5–10 ms per schematic on standard GPU hardware, confirming its applicability to interactive industrial Electronic Design Automation (EDA) workflows. These results highlight the potential of the Autoencoder–GCN pipeline as a scalable and reliable solution for AMS structure recognition under real-world constraints.