SuperSplat

Facing strict quality demands from the automotive industry, semiconductor companies are increasingly applying Part Average Testing (PAT) to enhance reliability. PAT identifies and removes "outlier" parts that pass standard tests but exhibit abnormal characteristics, reducing long-term quality and reliability issues. By statistically analyzing multiple devices and adjusting pass/fail limits, PAT proactively identifies these problematic parts for exclusion, ensuring only the most reliable components reach production shipments. Part Average Testing ( PAT ) as detailed in the Automotive Electronics Council AEC-Q001-Rev C specifications only covers DPM techniques for normal (Gaussian) distributions. However, many distributions do not fall into the Gaussian category and require adapted PAT outlier detection methods to avoid excessive yield loss or incorrect outlier detection. PAT-Man is a comprehensive solution for Part Average Testing (PAT).

SpAItial is an AI platform focused on building and deploying Spatial Foundation Models (SFMs), a new class of generative AI systems designed to create and understand 3D environments with physical realism and spatial awareness. Unlike traditional models that generate pixels or text independently, SpAItial’s technology operates directly on 3D structures, capturing geometry, materials, lighting, and physics from the outset to produce coherent, interactive worlds. Its flagship model, Echo-2, can transform a single image into a fully explorable, photorealistic 3D scene using techniques like Gaussian splatting, enabling users to navigate and render environments in real time. It is built around a physically grounded understanding of space-time, allowing AI to reason about how objects exist, interact, and evolve within an environment rather than producing disconnected outputs. This approach reduces inconsistencies common in traditional generative AI and enables more accurate simulation.