Localization of defects in die-attach assembly by continuous wavelet transform using scanning acoustic microscopy

Béchou, Laurent; Angrisiani, L.; Ousten, Yves; Dallet, Dominique; Levi, Hila; Daponte, P.; Danto, Yves

doi:10.1016/s0026-2714(99)00155-9

Cited by 11 publications

(3 citation statements)

References 8 publications

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“…5(a)]. The one that propagates in the transparent layer interacts with the probe optical beam: While propagating, the acoustic strain induces a modulation of the optical refractive index through the photoelastic effect [see (2)]. Due to the difference between light and sound wave velocities (3e8 ms −1 versus typically thousands of meters per second), the medium is considered quasi-static for the probe light.…”

Section: Passivation Influence On the Signalmentioning

confidence: 99%

“…However, due to multiple reflections and scattering of the ultrasonic beam in multilayer samples, ultrasonic images can often appear very confused to be correctly interpreted and the nature of defects difficult to Manuscript be pointed out. Furthermore, for a good understanding of the images revealed by these techniques, signal processing methods are required [2]. Moreover, SAM resolution is limited by the acoustic frequency (tens of megahertz) to a few micrometers.…”

Section: Introductionmentioning

confidence: 99%

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Scanning laser ultrasonics experiments for in situ nondestructive analysis of integrated circuits

Andriamonje

Pouget

Ousten

et al. 2005

IEEE Trans. Device Mater. Relib.

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Scanning experiments on a very large scale integrated (VLSI) circuit are presented using the picosecond ultrasonics technique. This optical nondestructive technique is based on ultrasound generation and detection by ultrashort laser pulses. The experimental setup and methodology are described. The influence of the protection layer for passivated circuits on the experimental results is described and illustrated by experimental data. In order to help in the interpretation of the experimental analysis of microelectronic devices, a simulation tool was developed.

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Section: Passivation Influence On the Signalmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Scanning laser ultrasonics experiments for in situ nondestructive analysis of integrated circuits

Andriamonje

Pouget

Ousten

et al. 2005

IEEE Trans. Device Mater. Relib.

Self Cite

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show abstract

“…After an original signal was decomposed by 5 levels, an approximate coefficient and five detailed coefficients were produced, each of them represent different scale of frequency information embedded in the original signal. The noise signals mainly distributed on the high frequency coefficient level, such as d1 and d2 [12][13][14][15][16], and defect echo appeared on comparatively low frequency coefficient level, d3-d5. Comparing different levels of each flaw signal, it can be find that waveform differences on d5 scale is more obvious than that on the other two high frequency scales, d3 and d4.…”

Section: Ultrasonic Testing and Signal Processingmentioning

confidence: 99%

Ultrasonic Detection of the Metallurgical Defects in the Steel and its Evaluation by Neural Network Based on the Wavelet Transform Noise Suppression

Zhang

Ta³

2004

KEM

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To detect the metallurgical flaws and to realize its automatic classification has long been expected by technicians in the steelmaking shop, among various methods have been tried, ultrasonic is the mostly preferred one. In this work, a method used to realize the steel flaws detection and auto-classification has been tried, which involving the use of the high center frequency ultrasonic detection, transient nonstationary signal processing tools, wavelet transformation, and neural networks. On the base of ultrasonic test results, the different flaw echoes were analyzed by multiresolution signal decomposition using wavelet transform, and a chain of 120 data samples were extracted from the reconstructed wavelet coefficient, which can represents the character of the corresponding flaw. Totally there were 42 chains of data were collected from specimens with different flaws contained, they were divided into a training set and a testing set for the training of the neural networks, which are used for automatic classification of the flaw signals. The results produced from the network architectures investigated shown classification rates in the range from 70 to 100% based on real and artificial defects and with comparatively simple feature extraction method, a performance can be accepted in the light of rough evaluation

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Ultrasonic Inspection of Thin Multilayers

Hinders¹,

Hou²,

McKeon³

2005

AIP Conference Proceedings

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Localization of defects in die-attach assembly by continuous wavelet transform using scanning acoustic microscopy

Cited by 11 publications

References 8 publications

Scanning laser ultrasonics experiments for in situ nondestructive analysis of integrated circuits

Scanning laser ultrasonics experiments for in situ nondestructive analysis of integrated circuits

Ultrasonic Detection of the Metallurgical Defects in the Steel and its Evaluation by Neural Network Based on the Wavelet Transform Noise Suppression

Ultrasonic Inspection of Thin Multilayers

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