AlphaGalileo

KAIST detects ‘hidden defects’ that degrade semiconductor performance with 1,000× higher sensitivity

Semiconductors are used in devices such as memory chips and solar cells, and within them may exist invisible defects that ...
pv magazine International

US researchers use machine learning to detect hidden solar array defects

Researchers from Stony Brook University, in collaboration with Ecosuite and Ecogy Energy, have developed a self-supervised machine-learning algorithm designed to identify physical anomalies in solar ...
Scientific Research Publishing

Cheng, J.C.P. and Wang, M. (2018) Automated Detection of Sewer Pipe Defects in Closed-Circuit Television Images Using Deep Learning Techniques. Automation in Construction, 95 ...

ABSTRACT: Regular pipeline inspections are crucial for timely identification of critical defects and ensuring pipeline integrity. To address the challenges of detecting defects in PE gas pipelines ...
Scientific Research Publishing

BCN-YOLO: A Deep Learning Network for PCB Defect Detection ()

To address the issues of missed detection and false detection during the defect inspection process of the PCB, an improved YOLOv7-based algorithm for PCB defect detection is proposed. Firstly, the ...
EDN

Real-time AI fuels faster, smarter defect detection

TDK SensEI’s edgeRX Vision system, powered by advanced AI, accurately detects defects in components as small as 1.0×0.5 mm in real time. Operating at speeds up to 2000 parts per minute, it reduces ...
Semiconductor Engineering

Detecting Slips, Scratches, Cracks In Wafers And Dies Becoming Harder

Defect detection requirements on the order of 10 defective parts per million (DPPM) are driving improvements in inspection tools’ resolution and throughput at foundries and OSATs. However, defects ...
IEEE

AI-Powered Defect Detection using Deep Learning: A Pattern-Agnostic Faster R-CNN Approach for SEM Images with GPU Acceleration

Abstract: Semiconductor manufacturing requires highly precise defect detection to ensure product quality and yield. This paper presents a deep learning-based defect detection framework using Faster ...
IEEE

Advances and Challenges in Deep Learning for Automated Welding Defect Detection: A Technical Survey

Abstract: Automated welding defect detection has emerged as a pivotal aspect of quality assurance in high-stakes industries such as aerospace, oil and gas, and construction. This paper presents a ...