Empowering Circuit Prototyping with Generative AI: A Framework for Vision-Based Real-Time Code Generation and Adaptation

Circuit prototyping is crucial in electronic design but remains a challenging and time-intensive process, often demanding manual coding, iterative testing, and domain expertise. Despite the popularity of platforms like Arduino and Raspberry Pi, translating designs into functional code continues to be a bottleneck, especially for non-experts. This research introduces a generative AI-driven framework to automate and adapt code generation in real-time, streamlining prototyping workflows and enhancing accessibility and efficiency. Leveraging state-of-the-art language models, the proposed system monitors physical changes in circuit configurations on breadboards, focusing on the Arduino platform, and dynamically translates visual data into functional code. The study explores AI's potential in generating Arduino code, presenting approaches to achieve real-time adaptation during circuit design. Empirical results demonstrate the framework's effectiveness through time-series comparisons, API usage analysis, and multi-angle viewport assessments. This foundational work highlights AI's transformative potential in modernizing and simplifying circuit prototyping and design workflows.