Circuit design is a complex and iterative process that requires expertise in electronic engineering. It involves selecting components while meeting performance constraints, such as power efficiency, cost-effectiveness, and signal integrity. However, manual design is time-consuming and prone to errors. Although other stages of the manufacturing pipeline have benefited from AI-driven optimizations, circuit design remains a bottleneck, limiting overall productivity. Generative AI and machine learning offer the potential to automate and improve this stage, boosting efficiency and accuracy. To address this, we introduce a dual-transformer architecture that bridges the gap between AI and circuit design by leveraging attention mechanisms to model complex, non-sequential circuit relationships. Our approach structures netlist data into graph-based representations, enabling effective learning of circuit topology and component interactions. The system consists of two interlinked models: a node prediction model that proposes components and an edge prediction model that infers valid connections. This collaborative and decoupled design captures both component-level semantics and global structural coherence. In our experiments, this architecture outperforms recent models such as AnalogGenie and cktGNN in the validity of generated circuits. By addressing key limitations in existing methods, our work advances automation in electronics engineering and contributes a benchmark for AI-driven circuit synthesis.

Traditional methods for identifying defects in industrial settings are often complicated, inefficient, and rely heavily on expert knowledge for manual feature extraction and pipeline development. However, with the emergence of machine learning and deep learning, there’s been a shift towards using these technologies for defect detection. Particularly, Generative AI has garnered attention for its ability to generate accurate defect identifications across various sectors. Our contribution lies in leveraging generative AI to revolutionize wafer manufacturing defect detection. We explore the balance between discriminative inference, known for its speed but susceptibility to shortcuts, and generative modeling, which offers enhanced robustness despite slower operation. Our investigation emphasizes the need for a model distinct from conventional discriminative ones, capable of adaptive learning to distinguish between normal and abnormal patterns in wafer manufacturing, aligning with expert judgment and utilizing historical data for predictive maintenance. We propose two innovative approaches using Generative AI: first, augmenting existing discriminative models with generative models for improved practicality; second, utilizing generative modeling to convert image-to-text models into classifiers.

Maintaining and inspecting infrastructure is crucial due to the safety hazards and economic costs of structural failures. Traditional methods are labor-intensive, time-consuming, and reactive. We propose an automated inspection system leveraging generative AI to enhance efficiency, predictive maintenance capabilities, and comprehensive data analysis. Our framework uses drone-based data acquisition with high-definition cameras and depth sensors, and a custom deep learning model, EyeNet, for precise crack detection. Generative AI techniques, including a Visual Question-Answering (VQA) model and an image-to-image model, are employed for detailed crack analysis and future crack pattern visualization, enabling proactive maintenance. The VQA model achieves an average Root Mean Square Error (RMSE) of 0.394 and an average Symmetric Mean Absolute Percentage Error (SMAPE) of 31.22%. A Large Language Model generates comprehensive reports with visualizations, accessible via a dedicated website. Our system significantly improves the inspection process compared to traditional methods, setting a new benchmark by combining generative AI for detailed crack analysis and predictive maintenance capabilities, creating a comprehensive inspection system for civil infrastructure.