Three-Stage Optimization of Pre-Bond Diagnosis of TSV Defects

Zhang, Bei; Agrawal, Vishwani D.

doi:10.1007/s10836-017-5681-x

Cited by 4 publications

(1 citation statement)

References 28 publications

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“…15 Electrical inspection will usually be able to detect short-defect and open-defect that make changes in electrical parameters, greatly limiting the types of defects that can be detected. 16,17 With the gradual reduction of critical dimensions such as TSV, X-ray has found great application in practical applications with its resolution below 15 nm. First, the detection image is captured by an X-ray detector, and the image is processed by image processing techniques, 18 such as Canny operator and morphological modification, then a specific neural network model is trained by extracting the corresponding feature parameters.…”

Section: Introductionmentioning

confidence: 99%

Internal defect identification method of TSV 3D packaging based on built-in integrated sensor

Huang

Yin

Liu

et al. 2022

Advances in Mechanical Engineering

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TSV (Through Silicon Via) is a key technology for three-dimensional (3D) packaging due to its unique vertical interconnection method. However, its particular manufacturing process of-ten leads to internal defects, such as gaps, bottom voids, filling missing, which are usually difficult to be detected by common means. In order to discover the internal defect of TSV packaging effectively, a novel non-destructive inspection method based on built-in integrated temperature sensor array is proposed. The relationship between temperature distribution and internal defect is dis-covered and then corresponding sensor array layout is designed. The simulation analysis shows that this kind of sensor array can recognize the internal TSV defect. And supervised machine learning is used to construct the classification model by which different defects can be found and classified with relatively high accuracy, and the classification accuracy rate can reach 95.625%. Experiments were conducted and the rationality of this built-in sensing array was verified. The research provides a non-destructive testing method for TSV internal defects based on bulit-in-integrated sensors, and verifies the feasibility of sensor arrangement through simulation, laying a foundation for the realization of later TSV design optimization.

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