Machine Vision

Traditional imaging systems mainly rely on visible light cameras to capture scene information. While visible imaging provides rich colors, textures, and details, its performance can be significantly affected in challenging environments such as darkness, low illumination, glare, smoke, and complex backgrounds. Infrared imaging introduces a new sensing dimension by detecting thermal radiation rather than relying […]

Traditional factory cameras see even less. For decades, global manufacturing, machine vision automation, and deep-space research have been bottlenecked by what is visible on the surface. If your industrial automation infrastructure relies solely on visible or UV light, you are operating half-blind. SWIR cameras are changing the game. By capturing light in the 900 nm to

In next-generation semiconductor manufacturing and high-density PCBA production, semiconductor inspection accuracy directly impacts yield, reliability, and production efficiency. As chip architectures become smaller, thinner, and more complex, traditional visible light inspection systems face fundamental limitations. Visible cameras can only capture surface information because silicon wafers behave like optical mirrors, preventing engineers from seeing hidden structures

In the rapidly evolving machine vision landscape, relying on a single optical spectrum introduces physical constraints that limit edge-computing reliability. High-resolution visible light cameras capture intricate textures and color data but fail under low-light conditions, dense smoke, or high-contrast backlighting. Conversely, uncooled infrared thermal imaging operates independently of ambient light but lacks structural edge details and