How to Detect Defects in Semiconductor Manufacturing

What is Wafer sort Testing? — How wafers Are Validated. Before any chip is packaged, shipped, or even separated from the wafer, it has to pass through the first critical checkpoint of validation: wafer-level testing, also known as wafer probe. This is the moment where we ask: does the silicon actually work? -> What you’re looking at The image shows a modern probe station—a machine that forms the backbone of semiconductor validation. It enables engineers to test every die on a wafer while it’s still intact, long before packaging or integration. -> How it works Each individual chip (die) on the wafer is tested using a probe card—a fine mesh of precision-aligned contact needles that touch down on the chip’s test pads. The system is made up of several major components: A prober that precisely aligns and positions the wafer A tester head that generates and measures electrical signals A cooling/heating system that simulates thermal environments An interface cable system that routes everything through a central test controller -> Why it matters This is the first chance to spot defects: electrical shorts, opens, logic failures, and timing issues. Bad dies are marked and discarded early—improving yield, lowering costs, and eliminating waste. Key takeaway: The probe station is not just a testing machine—it’s the gatekeeper of silicon quality. Before any burn-in, system-level testing, or packaging, this machine decides which dies live on and which are scrapped. It’s where validation begins, silicon is sorted, and every chip earns its chance to move forward in the supply chain. As chips grow in complexity, probe stations are evolving too—with AI-based analytics, adaptive testing, and advanced alignment to support new 2.5D/3D architectures and ultra-small nodes. P.S. If you're looking for semiconductor news and insights, check out our blog The Semiconductor World—a guide to the chip industry in simple terms. Link in the comments. #Semiconductors #WaferTesting #ProbeStation #ChipValidation #ATE #TestFlow #ATOMS #ICDesign #AdvancedPackaging #Fabless #YieldOptimization #SemiconductorWorld

Still relying on AOI systems for defect detection and classification? Traditional inspection systems were never meant to handle today’s advanced nodes and complex defects. High false positives, missed edge cases, and endless manual review have become the norm in too many fabs. Averroes.ai is changing that. With AI-powered AOI, fabs are seeing over 96% defect detection accuracy, up to 80% fewer false positives, and a 45% boost in inspection speed. Our models learn from your data, adapt to process shifts, and integrate with your existing tools. No rip and replace needed. If your fab is still relying on rule-based inspection, it might be time to work with Averroes.ai and layer up your existing tool with AI. Read more: How AI-Powered AOI Is Revolutionizing Semiconductor Yield Control #Semiconductor #AOI #YieldControl #SmartManufacturing #DefectDetection #AIInSemiconductors #WaferInspection #AdvancedManufacturing #AverroesAI #AIPowered #ProcessEngineering

Semiconductors with AI-Driven Machine Vision No AI, no progress! Innovation in semiconductors hinges on machine vision, but it's AI that truly propels progress! Lower tolerances and smaller components present significant challenges for inspection in semiconductor manufacturing. To manage yield effectively, manufacturers are increasing inspection points throughout the process, from wafers to finished PCBs, to detect defects earlier. The key is to add more inspection steps without extending the overall inspection time, necessitating imaging systems that offer higher resolution while reducing exposure periods. New solutions, such as deep and extreme UV illumination and imaging, are crucial. However, the growing need for precise imaging places immense pressure on downstream components—the image processing hardware and software—that convert images into actionable insights for decision-making. In PCB manufacturing, the complexity of design and manufacturing has led to increasingly subtle material faults. Detecting a wide array of defects, including breakage, abrasion, contamination, fragments, and air bubbles, is essential. Traditional rule-based image processing techniques can struggle with reliability when faced with PCB components that exhibit high variations in shape, tone, contrast, and texture. As a result, manufacturers are turning to AI to augment traditional methods. AI algorithms, trained on samples of defective and non-defective PCB components, can achieve a high degree of precision in component classification. Transitioning Beyond Traditional Approaches to AI-Driven Imaging One semiconductor OEM faced a surge in undetected defective parts in its automated optical inspection (AOI) process. To address this challenge, the OEM implemented a new inspection solution that integrates both rule-based algorithms and AI functions for the AOI machine. With the aid of AI software tools, the OEM achieved an impressive 98% accuracy in continual classification, with speeds of 12-14 ms for 200 images. Moreover, the system demonstrated 100% accuracy with 453 good + 11 bad images. Additionally, the company achieved a remarkable 99.62% accuracy with 259 images, at a speed of 20 ms, for object detection, enabling simultaneous detection of multiple defects on a single part image. Source Vision Systems Design #semicon #AI #innovatingautomation #machinevision #UV