Laser scanning confocal IR microscopy for non-destructive testing of semiconductors

Ukolov, D. S.; Baluev, Arseniy A.; Gromova, P. S.; Pechenkin, Alexander; Mozhaev, R. K.

doi:10.1109/mwent55238.2022.9802329

Cited by 2 publications

(2 citation statements)

References 8 publications

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“…Non-destructive testing (NDT) techniques such as scanning electron microscopy (SEM) and scanning helium ion microscopy (SHIM) play an important role in inspecting secondary electron (SE) signals in semiconductor chips without compromising the integrity of the material [1][2][3][4][5]. Similarly, in welding applications used in automotive and ship manufacturing, NDT plays a key role in inspecting welded joints for defects [6][7][8][9][10].…”

Section: Introductionmentioning

confidence: 99%

Multi-Branch Deep Fusion Network-Based Automatic Detection of Weld Defects Using Non-Destructive Ultrasonic Test

Kim,

Park

2023

IEEE Access

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This study introduces a deep learning engine designed for the non-destructive automatic detection of defects within weld beads. A 1D waveform ultrasound signal was collected using an A-scan pulser receiver to gather defect signals from inside the weld bead. We established 5,108 training datasets and 500 test datasets for five pass/fail labels in this study. We developed a multi-branch deep fusion network (MBDFN) model that independently trains 1D-CNN for local pattern learning within a sequence and 2D-CNN for spatial feature extraction and then combines them in an ensemble method, achieving a classification accuracy of 92.2%. The resulting deep learning engine has potential applications in automatic welding robots or welding inspection systems, allowing for rapid determination of internal defects without compromising the integrity of the finished product.

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Section: Introductionmentioning

confidence: 99%

Multi-Branch Deep Fusion Network-Based Automatic Detection of Weld Defects Using Non-Destructive Ultrasonic Test

Kim,

Park

2023

IEEE Access

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“…Keywords: machine vision, laser scanning microscopy, topology correction, microelectronics, laser scribing, automation. Машинное зрение и лазерная сканирующая микроскопия (ЛСМ) [1][2][3] находят применение в определении и контроле доверенности изделий электронной компонентной базы (ЭКБ). Доверенность ЭКБ подразумевает соответствие изделия требованиям к функционально-техническим, эксплуатационным характеристикам и предъявляемым требованиям по технологической, функциональной и информационной безопасности.…”

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Application of machine vision technology for focused laser effect accuracy improvement in microelectronic structures research and semiconductor microprocessing

Mozhaev,

Pechenkin,

Tsirkov

et al. 2023

BIT

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Laser scanning confocal IR microscopy for non-destructive testing of semiconductors

Cited by 2 publications

References 8 publications

Multi-Branch Deep Fusion Network-Based Automatic Detection of Weld Defects Using Non-Destructive Ultrasonic Test

Multi-Branch Deep Fusion Network-Based Automatic Detection of Weld Defects Using Non-Destructive Ultrasonic Test

Application of machine vision technology for focused laser effect accuracy improvement in microelectronic structures research and semiconductor microprocessing

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