Researchers have tested eight stand-alone deep learning methods for PV cell fault detection and have found that their accuracy was as high as 73%. All methods were trained and tested on the ELPV ...

Optical 3D metrology enables fast, non-contact surface roughness measurement of defects and roughness for precise ...

Researchers have designed a robust image-based anomaly detection (AD) framework with illumination enhancement and noise suppression features that can enhance the detection of subtle defects in ...

Scientists from China have developed a new deep-learning method for detecting defects in PV cells. Analyzing electroluminescence (EL) images, the novel system utilizes the YOLOv8 convolutional neural ...

Portable 3D optical inspection with 4Di InSpec transforms surface metrology, enhancing defect detection and quality assurance ...

What if manufacturing companies could pinpoint the exact cause of a defect the moment it occurs, preventing costly production delays and ensuring top-notch quality? Generative artificial intelligence ...

Detecting sub-5nm defects creates huge challenges for chipmakers, challenges that have a direct impact on yield, reliability, and profitability. In addition to being smaller and harder to detect, ...

As semiconductor chip technology advances towards nanometre and sub-nanometre scales, the demands becomes exponentially more ...

Detecting macro-defects early in the wafer processing flow is vital for yield and process improvement, and it is driving innovations in both inspection techniques and wafer test map analysis. At the ...

In industry, the detection of anomalies such as scratches, dents, and discolorations is crucial to ensure product quality and safety. However, conventional methods rely on heavy computational ...