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Many sparse signal representation (SSR) algorithms have been developed in the past decade. The advantages of SSR such as compact representations and super resolution lead to the state of the art performance of SSR for processing ultrasonic non-destructive evaluation (NDE) signals. Choosing a suitable SSR algorithm and designing an appropriate overcomplete dictionary is a key for success. After a brief review of sparse signal representation methods and the design of overcomplete dictionaries, this paper addresses the recent accomplishments of SSR for processing ultrasonic NDE signals. The advantages and limitations of SSR algorithms and various overcomplete dictionaries widely-used in ultrasonic NDE applications are explored in depth. Their performance improvement compared to conventional signal processing methods in many applications such as ultrasonic flaw detection and noise suppression, echo separation and echo estimation, and ultrasonic imaging is investigated. The challenging issues met in practical ultrasonic NDE applications for example the design of a good dictionary are discussed. Representative experimental results are presented for demonstration., (Copyright © 2011 Elsevier B.V. All rights reserved.)

The axial resolution of conventional acoustic micro imaging is limited by the wavelength of acoustic waves. Acoustic time-frequency domain imaging was recently proposed to overcome the wavelength limit [Zhang et al., J. Acoust. Soc. Am. 118, 3706-3720 (2005)]. A continuous wavelet transform based acoustic time-frequency domain imaging technique is investigated in this paper. Experiments are performed on real 3D data collected from microelectronic packages. Results demonstrate the proposed technique reveals more image details and enhances the image contrast in comparison with conventional time domain imaging.

Sparse signal representations from overcomplete dictionaries are the most recent technique in the signal processing community. Applications of this technique extend into many fields. In this paper, this technique is utilized to cope with ultrasonic flaw detection and noise suppression problem. In particular, a noisy ultrasonic signal is decomposed into sparse representations using a sparse Bayesian learning algorithm and an overcomplete dictionary customized from a Gabor dictionary by incorporating some a priori information of the transducer used. Nonlinear postprocessing including thresholding and pruning is then applied to the decomposed coefficients to reduce the noise contribution and extract the flaw information. Because of the high compact essence of sparse representations, flaw echoes are packed into a few significant coefficients, and noise energy is likely scattered all over the dictionary atoms, generating insignificant coefficients. This property greatly increases the efficiency of the pruning and thresholding operations and is extremely useful for detecting flaw echoes embedded in background noise. The performance of the proposed approach is verified experimentally and compared with the wavelet transform signal processor. Experimental results to detect ultrasonic flaw echoes contaminated by white Gaussian additive noise or correlated noise are presented in the paper.

Abstract: In a highly competitive market, reliable techniques for manufacturing quality control of electronic devices are demanded. Characterisation of modern microelectronic package integrity becomes more difficult due to the continued miniaturisation of electronic device and the complexity of advanced micro-assembling technologies such as chip-scale packages and 3D IC stacks. In this paper, sparse representations of acoustic signals are sought to improve the scanning acoustic microscopy (SAM), a common non-destructive tool for failure analysis of microelectronic packages. Sparse representation of an ultrasonic signal is obtained by decomposing it in an overcomplete dictionary. Detection and location of ultrasonic echoes are then performed on the basis of the resulting redundant representation. The method offers a solution to the deconvolution problem for restoration of the ultrasonic reflectivity function. It can restore closely space overlapping echoes beyond the resolution of the conventional SAM system. It also produces high resolution and accurate estimates for ultrasonic echo parameters, i.e., time-of-flight, amplitude, centre frequency, and bandwidth. These merits of the proposed method are explored in various potential applications for microelectronic package characterisation. [Copyright &y& Elsevier]

Acoustic microimaging (AMI) is a common nondestructive tool for failure analysis of microelectronic packages. Accurate estimation of the reflected ultrasonic echoes is essential for detection and location of defects inside the microelectronic packages. In this paper, an advanced AMI technique based on adaptive sparse representations is proposed to estimate the ultrasonic echoes and recover the reflectivity function. An adapted overcomplete dictionary capable of concise expression of ultrasonic signals is first learned by the focal underdetermined system solver-based column normalized dictionary learning algorithm. The ultrasonic A-scans generated by an AMI system are then decomposed into adaptive sparse representations over the learned dictionary using a sparse basis selection algorithm. Echo selection and echo estimation are further performed from the resulting adaptive sparse representations. The proposed technique offers a solution to the blind source separation problem for restoration of the reflectivity function and can separate closely spaced overlapping echoes beyond the resolution of the AMI system. Experimental verifications are carried out using both synthetic and measured data. The results show the proposed technique produces high resolution and accurate estimates for ultrasonic echoes. [ABSTRACT FROM AUTHOR]

Modern 3D microelectronic packages frequently exhibit a significant overlap of reflected ultrasonic echoes, often exceeding 50 % due to the diminishing thickness of internal structural layers. This overlap results in a marked degradation of ultrasonic image quality during C-scanning. To tackle this issue, a novel multiresolution sparse signal representation algorithm is proposed to achieve multiresolution decomposition of overlapping ultrasonic signals. The algorithm begins with SMP algorithm using a standard Gabor dictionary for initial decomposition. In each subsequent iteration, the dictionary is refined by narrowing the dictionary parameter boundaries while dividing the signal into shorter segments. Through this iterative refinement, the decomposition achieves increased precision without necessitating the enlargement of the dictionary size. Our approach not only ensures a more precise decomposition but also enhances the alignment of dictionary atoms with ultrasonic echoes, especially in instances of echo overlap. The efficacy of this algorithm in accurately separating and estimating ultrasonic echoes has been validated through both simulated and experimental ultrasonic signals. This study strengthens Sparse Signal Representation (SSR) in ultrasonic Non-Destructive Evaluation (NDE) by addressing the challenge of unstable decomposition when the dictionary size surpasses a certain threshold, thereby enhancing the reliability of SSR in the failure analysis of 3D microelectronic packaging. [ABSTRACT FROM AUTHOR]