The paper introduces Sketch2Sim, an end‑to‑end modular framework that transforms hand‑drawn circuit diagrams into SPICE‑compatible netlists and automatically computes nodal voltages and currents. It employs a three‑stage pipeline: dual YOLO detectors for components and text, a CRNN‑CTC OCR module for symbol value extraction, and image‑processing techniques (thresholding, skeletonization, DBSCAN) to identify nodes and map components. Experiments on a curated dataset of 1,000 RLC circuits show robust performance across diverse handwriting styles, lighting, and clutter, and the system includes a user interface for rapid digitization and simulation.
Recent advances in Artificial Intelligence (AI) have revolutionized Electronic Design Automation (EDA), particularly through Large Language Models (LLMs) for circuit design tasks. However, their application to analog and mixed-signal domains remains limited by the lack of machine-readable representations of existing circuit design knowledge.
arXiv:2607. 01609v1 Announce Type: new Abstract: Recent advances in Artificial Intelligence (AI) have revolutionized Electronic Design Automation (EDA), particularly through Large Language Models (LLMs) for circuit design tasks.
The paper introduces an enhanced end‑to‑end circuit analysis framework built on Gemini 2.5 Pro, targeting engineering education. It addresses two key failure modes—circuit‑recognition hallucinations and reasoning‑process hallucinations—by adding a YOLO detector for source polarity re‑identification and an ngspice verification loop for iterative refinement. The resulting pipeline achieves 97.59 % accuracy on 83 undergraduate problems, markedly outperforming the baseline Gemini model and demonstrating significant gains across varied diagram styles and textbooks.
arXiv:2609.08254v1 Announce Type: new Abstract: Learning direct current circuit concepts requires learners to connect invisible physical quantities, such as current, voltage, resistance, and power, w...
PCBnet is a new large‑scale dataset of printed circuit board (PCB) schematics that includes over 300 real‑world designs, more than 50,000 component instances, 150,000 wires, 100,000 text regions, and 400,000 characters, each paired with a SPICE netlist. The authors also introduce an automated pipeline that converts schematic images into netlists, achieving 94.54% component detection mAP, 98.57% text recognition accuracy, and 84.47% end‑to‑end connectivity accuracy. This resource aims to serve as a benchmark and data foundation for future AI‑driven PCB design automation.
arXiv:2608. 09374v1 Announce Type: new Abstract: Electrical circuit analysis requires more than recognizing components in an image.
arXiv:2504. 03711v2 Announce Type: replace-cross Abstract: Artificial intelligence (AI)-driven electronic design automation (EDA) techniques have been extensively explored for VLSI circuit design applications.
arXiv:2608. 03913v1 Announce Type: new Abstract: Dense pretrained transformers do not naturally expose interpretable units for circuit extraction.
Recovering Parametric CAD sequences from raster-format 2D Computer-Aided Design (CAD) drawings accumulated prior to digital transformation is important for part reproduction and manufacturing process automation. However, existing studies either process only vector drawings or are limited to specific domains, and fail to explicitly connect dimensional annotations to geometric information, limiting their use of dimensional information for 3D Parametric CAD sequences recovery.
arXiv:2609.24384v1 Announce Type: cross Abstract: Recognizing hand-drawn geometric shapes is a foundational sub-problem of sketch recognition, with applications in education, human-computer interacti...
arXiv:2607. 11338v1 Announce Type: new Abstract: Symbolic expressions can effectively characterize and predict circuit behavior, but deriving them directly from circuit schematics is challenging.
arXiv:2607. 03835v1 Announce Type: cross Abstract: Converting a SPICE netlist into a human-readable schematic is a longstanding problem in electronic design automation: simulators and machine-learning pipelines readily produce netlists, but designers reason about circuits through diagrams.