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Screening For Silent Data Errors


Engineers are beginning to understand the causes of silent data errors (SDEs) and the data center failures they cause, both of which can be reduced by increasing test coverage and boosting inspection on critical layers. Silent data errors are so named because if engineers don’t look for them, then they don’t know they exist. Unlike other kinds of faulty behaviors, these errors also can c... » read more

Systematic Yield Issues Now Top Priority At Advanced Nodes


Systematic yield issues are supplanting random defects as the dominant concern in semiconductor manufacturing at the most advanced process nodes, requiring more time, effort, and cost to achieve sufficient yield. Yield is the ultimate hush hush topic in semiconductor manufacturing, but it's also the most critical because it determines how many chips can be profitably sold. "At older nodes, b... » read more

Metrology Of Thin Resist For High NA EUVL


One of the many constrains of high numerical aperture extreme ultraviolet lithography (High NA EUVL) is related to resist thickness. In fact, one of the consequences of moving from current 0.33NA to 0.55NA (high NA) is the depth of focus (DOF) reduction. In addition, as the resist feature lines shrink down to 8nm half pitch, it is essential to limit the aspect ratio to avoid pattern collapse. T... » read more

Bump Height Uniformity And 3D Sensing


Achieving 3D sensing for semiconductor bump height uniformity is essential before adding photoresist. But there are challenges in using traditional methods for measuring uniformity after copper plating, which requires a combination of 3D fringe projection technology and NanoResolution inspection and metrology. Here’s what we’ve learned in a bump height uniformity case study: » read more

Active Learning to Reduce Data Requirements For Defect Identification in Semiconductor Manufacturing


A new technical paper titled "Exploring Active Learning for Semiconductor Defect Segmentation" was published by researchers at Agency for Science, Technology and Research (A*STAR) in Singapore. "We identify two unique challenges when applying AL on semiconductor XRM scans: large domain shift and severe class-imbalance. To address these challenges, we propose to perform contrastive pretrainin... » read more

The Human Hand: Curating Good Data And Creating An Effective Deep-Learning R2R Strategy For High-Volume Manufacturing


Currently, the semiconductor manufacturing industry uses artificial intelligence and machine learning to take data and autonomously learn from that data. With the additional data, AI and ML can be used to quickly discover patterns and determine correlations in various applications, most notably those applications involving metrology and inspection, whether in the front-end of the manufacturing ... » read more

Publicly Available Dataset for PCB X-Ray Inspection (FICS- University of Florida)


Researchers from the Florida Institute for Cybersecurity (FICS) at the University of Florida published this technical paper titled "FICS PCB X-ray: A dataset for automated printed circuit board inter-layers inspection." Abstract "Advancements in computer vision and machine learning breakthroughs over the years have paved the way for automated X-ray inspection (AXI) of printed circuit bo... » read more

Finding Wafer Defects Using Quantum DL


New research paper titled "Semiconductor Defect Detection by Hybrid Classical-Quantum Deep Learning" by researchers at National Tsing Hua University. Abstract "With the rapid development of artificial intelligence and autonomous driving technology, the demand for semiconductors is projected to rise substantially. However, the massive expansion of semiconductor manufacturing and the develo... » read more

The Race To Zero Defects In Auto ICs


Assembly houses are fine-tuning their methodologies and processes for automotive ICs, optimizing everything from inspection and metrology to data management in order to prevent escapes and reduce the number of costly returns. Today, assembly defects account for between 12% and 15% of semiconductor customer returns in the automotive chip market. As component counts in vehicles climb from the ... » read more

Deep Learning In Industrial Inspection


Deep learning is at the upper end of AI complexity, sifting through more data to achieve more accurate results. Charlie Zhu, vice president of R&D at CyberOptics, talks about how DL can be utilized with inspection to identify defects in chips that are not discernible by traditional computer vision algorithms, classifying multiple objects simultaneously from multiple angles and taking into accou... » read more

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