Intrinsic stress analysis of tungsten-lined open TSVs

Filipovic, Lado; Singulani, Anderson; Roger, F.; Carniello, S.; Selberherr, S.

doi:10.1016/j.microrel.2015.06.014

Cited by 10 publications

(2 citation statements)

References 16 publications

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“…We apply scanning acoustic microscopy to scan the TSV arrays. Here, we characterise an open TSV technology 46 , see also method section for further sample details and the schematic in Fig. 1 A.…”

Section: Resultsmentioning

confidence: 99%

“…Even though closed TSV design has very low contact resistance, they suffer from a high degree of mechanical stress due to the mismatch of coefficient of thermal expansion between silicon and the filling material 45 . Therefore, tungsten-lined open TSV technology replaces the closed TSV one when thermal expansion is of particular concern 46 . In this work, we use an open TSV technology with a 100 µm diameter and 250 µm depth.…”

Section: Methodsmentioning

confidence: 99%

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An end-to-end convolutional neural network for automated failure localisation and characterisation of 3D interconnects

Paulachan

Siegert

Wiesler

et al. 2023

Sci Rep

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The advancement in the field of 3D integration circuit technology leads to new challenges for quality assessment of interconnects such as through silicon vias (TSVs) in terms of automated and time-efficient analysis. In this paper, we develop a fully automated high-efficient End-to-End Convolutional Neural Network (CNN) model, utilizing two sequentially linked CNN architectures, suitable to classify and locate thousands of TSVs as well as provide statistical information. In particular, we generate interference patterns of the TSVs by conducting a unique concept of Scanning Acoustic Microscopy (SAM) imaging. Scanning Electron Microscopy (SEM) is used to validate and also disclose the characteristic pattern in the SAM C-scan images. By comparing the model with semi-automated machine learning approaches its outstanding performance is illustrated, indicating a localisation and classification accuracy of 100% and greater than 96%, respectively. The approach is not limited to SAM-image data and presents an important step towards zero defect strategies.

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Section: Resultsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%