Semiconductor Engineering

AI Models Transform Defect Inspection And Review, But Can Fail To Scale

AI plays a role in improving defect capture rate and distinguishing between yield-killing and nuisance defects. New developments in wafer edge inspection are proving essential to bonded wafer yields.
EurekAlert!

A review of machine learning advances in reliability-based design, integrity assessment, inspection and maintenance of pipelines

Machine learning (ML) is reshaping pipeline integrity management (PIM) from physics-based to data-driven paradigms. This ...
AHA/ASA Journals

Aberrant Phase Separation of Endothelial MAML1 Causes Congenital Heart Disease by Suppressing Notch Activity

BACKGROUND: Congenital heart disease (CHD), the most common birth defect and a leading cause of infant mortality, is ...
Design And Reuse

NVIDIA and TSMC Bring AI Into Fabs to Advance Semiconductor Design and Manufacturing

NVIDIA GTC Taipei — NVIDIA today announced that TSMC, the world’s leading semiconductor company, is using NVIDIA accelerated computing and AI to advance semiconductor design and manufacturing.
Electronics360

Computex 2026: Nvidia and TSMC are bringing AI into the fab itself

Nvidia and the world’s largest foundry TSMC are collaborating to speed up semiconductor design and manufacturing. Under the deal, TSMC will use Nvidia’s AI and computing chips across its semiconductor ...
IEEE

A Domain-Incremental Learning Framework Based on Defect Replay and Gaussian Mixup for Industrial Defect Detection

Abstract: Incremental learning is a critical yet challenging problem in automation engineering, especially across heterogeneous domains. Existing incremental learning methods utilize mixup and mosaic ...
Nature

Ball bearing fault detection using an acoustic based machine learning approach

A ball bearing is a special rolling-element bearing with three primary functions: to position moving machine parts, support radial and axial loads, and, most importantly, reduce friction, thus giving ...
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 ...
Aerospace and Mechanical Insider on MSN

AI-driven inspection and digital thread transform aerospace quality engineering

How might aerospace quality engineers progress from defect detection to making defects obsolete entirely? The key to doing so lies in the intersection of AI-based inspection technology, predictive ...
IEEE

Machine Learning-Based Defect Detection, Output Matching Assessment, and Performance Recentering of a 79-GHz Four-Way CMOS Power Amplifier

Abstract: This work proposes the use of machine learning-based techniques for enhanced testability and performance calibration of an industrial 79-GHz power amplifier (PA) designed for an automotive ...
Nature

Real-time defect detection in concrete structures using attention-based deep learning and GPR imaging

As the backbone of modern infrastructure, concrete structures serve as critical load-bearing elements in bridges, dams, and high-rise buildings worldwide. However, these essential components are ...
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 ...