Physics-Informed Neural Networks (PINNs) are an emerging class of Artificial Intelligence (AI) models that incorporate physical laws directly into their architecture, enabling fast and accurate simulations even with limited or noisy data. They show significant promise for electromagnetic (EM) simulations, particularly in managing parameter variations in real time. However, ensuring both accuracy and stability in PINN training remains a major challenge, often requiring large datasets and exhibiting sensitivity to minor input changes. To address these limitations, researchers from Stevens Institute of Technology (SIT) and The Ohio State University (OSU) are developing an Open-Source AI-Driven Electronic Design Automation (EDA) Tool for Real-Time Synthesis of Short-Distance Wireless Interconnects on Silicon (OASIS), the first open-source, AI-powered EDA tool for real-time parametric EM simulation. OASIS will explore scalable strategies for training large-scale PINNs efficiently and robustly. This research will focus on the design of short-range (~10 mm) wireless interconnects on silicon for two cutting-edge applications: (1) contactless connectors that leverage spatial multiplexing to minimize interference and enhance data throughput, and (2) batteryless brain-machine interfaces (BMIs) that depend on real-time signal cancellation and sensitivity optimization. By replacing traditional slow solvers with a faster, AI-driven alternative, OASIS aims to transform next-generation EM de