SINA: A Circuit Schematic Image-to-Netlist Generator Using Artificial Intelligence

TL;DR

SINA is an AI-powered tool that automates converting circuit schematic images into accurate netlists by combining deep learning, CCL, OCR, and VLM techniques, significantly outperforming existing methods.

Contribution

This paper introduces SINA, a novel open-source system that integrates multiple AI techniques for high-accuracy circuit schematic image-to-netlist conversion.

Findings

Achieves 96.47% netlist-generation accuracy

Outperforms state-of-the-art methods by 2.72 times

Successfully automates component recognition and connectivity inference

Abstract

Current methods for converting circuit schematic images into machine-readable netlists struggle with component recognition and connectivity inference. In this paper, we present SINA, an open-source, fully automated circuit schematic image-to-netlist generator. SINA integrates deep learning for accurate component detection, Connected-Component Labeling (CCL) for precise connectivity extraction, and Optical Character Recognition (OCR) for component reference designator retrieval, while employing a Vision-Language Model (VLM) for reliable reference designator assignments. In our experiments, SINA achieves 96.47% overall netlist-generation accuracy, which is 2.72x higher than state-of-the-art approaches.