Automated Measurement Recipes for Photothermal AFM‑IR


Recipe‑based automation for atomic force microscopy (AFM) workflows ensures consistent, repeatable data acquisition, reduces operator dependency, and streamlines complex measurement routines. Bruker’s AutoMET automation software, widely adopted for conventional AFM characterization, now also brings automation to nanoscale IR (nanoIR) spectroscopy and imaging. AutoMET provides except... » read more

Surface Metrology for Hybrid Bonding in Advanced Semiconductor Packaging


Achieving a reliable hybrid bond requires both surfaces to be pristine. To support this requirement, metrology methods such as atomic force microscopy (AFM) and atomic force profilometry (AFP) are critical for surface characterization and process optimization. AFM delivers localized, high-resolution surface measurements, while AFP provides complementary large-area topography scans that ... » read more

Lightweight AI Techniques For Automated Inspection of Silicon Wafers (Fraunhofer)


A new technical paper titled "Towards efficient wafer visual inspection: Exploring novel lightweight approaches for anomaly detection and defect segmentation" was published by researchers at Fraunhofer Portugal AICOS. Excerpt "AI has made significant strides in unsupervised anomaly detection and supervised defect segmentation, yet its application to wafer inspection remains underexplored. T... » read more

Overview Of 103 Research Papers On Automatic SEM Image Analysis Algorithms For Semiconductor Defect Inspection (KU Leuven, Imec)


A new technical paper titled "Scanning electron microscopy-based automatic defect inspection for semiconductor manufacturing: a systematic review" was published by researchers at KU Leuven and imec. "We identified, categorized, and discussed automatic defect inspection algorithms that analyze scanning electron microscopy (SEM) images for semiconductor manufacturing (SM). This is a topic of c... » read more

Wafer Bin Map Defect Classification Using Semi-Supervised Learning


A new technical paper titled "Semi-Supervised Learning with Wafer-Specific Augmentations for Wafer Defect Classification" was published by researchers at Korea University. Abstract "Semi-supervised learning (SSL) models, which leverage both labeled and unlabeled datasets, have been increasingly applied to classify wafer bin map patterns in semiconductor manufacturing. These models typical... » read more

Outsmarting Silent Data Corruption In AI Processors With Two-Stage Detection


Silent data corruption is on the rise following advancements in semiconductor technology. The explosion in AI for speech, image, video, and text processing leads to a growing complexity and diversity of hardware systems, bringing an increased risk to data integrity. SDC rate is much higher than software engineers expect, undermining the hardware reliability they used to take for granted. Rec... » read more

Characterizing Defects Inside Hexagonal Boron Nitride (KAIST, NYU, et al.)


A new technical paper titled "Characterizing Defects Inside Hexagonal Boron Nitride Using Random Telegraph Signals in van der Waals 2D Transistors" was published by researchers at KAIST, NYU, Brookhaven National Laboratory, and National Institute for Materials Science. Abstract: "Single-crystal hexagonal boron nitride (hBN) is used extensively in many two-dimensional electronic and quantu... » read more

Review of Automatic EM Image Algorithms for Semiconductor Defect Inspection (KU Leuven, Imec)


A new technical paper titled "Electron Microscopy-based Automatic Defect Inspection for Semiconductor Manufacturing: A Systematic Review" was published by researchers at KU Leuven and imec. Abstract: "In this review, automatic defect inspection algorithms that analyze Electron Microscope (EM) images of Semiconductor Manufacturing (SM) products are identified, categorized, and discussed. Thi... » read more

Predicting Defect Properties In Semiconductors With Graph Neural Networks


A technical paper titled “Accelerating Defect Predictions in Semiconductors Using Graph Neural Networks” was published by researchers at Purdue University, Indian Institute of Technology (IIT) Madras, GE Research, and National Institute of Standards and Technology (NIST). Abstract: "Here, we develop a framework for the prediction and screening of native defects and functional impurities i... » read more

More Accurate And Detailed Analysis of Semiconductor Defects In SEM Images Using SEMI-PointRend


A technical paper titled "SEMI-PointRend: Improved Semiconductor Wafer Defect Classification and Segmentation as Rendering" was published (preprint) by researchers at imec, University of Ulsan, and KU Leuven. Abstract: "In this study, we applied the PointRend (Point-based Rendering) method to semiconductor defect segmentation. PointRend is an iterative segmentation algorithm inspired by ima... » read more

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