Guang-Ming Zhang

Sparse signal representation and its applications in ultrasonic NDE.

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.

Application of adaptive time-frequency decomposition in ultrasonic NDE of highly-scattering materials

In the paper, adaptive time-frequency decomposition by basis pursuit (BP) is utilized to improve ultrasonic flaw detection in highly-scattering materials as an alternative to the Wavelet Transform technique. The detection of ultrasonic pulses using the BP is described. Computer simulation was performed to verify the signal detection improvements for an ultrasonic wave embodied in white noise, and numerical results show good detection even for signal-noise ratio (SNR) of -18 dB. The improvement in detection is experimentally verified using cast steel samples with artificial flaws.

Advanced acoustic microimaging using sparse signal representation for the evaluation of microelectronic packages

Acoustic microimaging (AMI) has been widely used to nondestructively evaluate microelectronic packages for the presence of internal defects. To detect defects in small devices such as /spl mu/BGA, flip-chip, and chip-scale packages, high acoustic frequencies are required for the conventional AMI systems. The acoustic frequency used in practice, however, is limited by its penetration through materials. In this paper, a novel acoustic microimaging technique, which utilizes nonlinear signal processing techniques to improve the resolution and robustness of conventional AMI, is proposed and investigated. The technique is based on the concept of sparse signal representations in overcomplete time-frequency dictionaries. Simulation and experimental results show its super resolution and high robustness.

Optimal frequency-to-bandwidth ratio of wavelet in ultrasonic non-destructive evaluation

The wavelet transform is the most recent technique for processing signals with time-varying spectra. The frequency-to-bandwidth ratio of wavelet is an important factor for getting success in the practical applications of the technique. In this paper, a theoretical model is presented to estimate the optimal frequency-to-bandwidth of the Gaussian wavelet in ultrasonic non-destructive evaluation. Then the experimental verification is performed, and the experimental results confirm the availability of the theoretical model.

Signal denoising and ultrasonic flaw detection via overcomplete and sparse representations

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.

Contemporary ultrasonic signal processing approaches for nondestructive evaluation of multilayered structures

Various signal processing techniques have been used for the enhancement of defect detection and defect characterisation. Cross-correlation, filtering, autoregressive analysis, deconvolution, neural network, wavelet transform and sparse signal representations have all been applied in attempts to analyse ultrasonic signals. In ultrasonic nondestructive evaluation (NDE) applications, a large number of materials have multilayered structures. NDE of multilayered structures leads to some specific problems, such as penetration, echo overlap, high attenuation and low signal-to-noise ratio. The signals recorded from a multilayered structure are a class of very special signals comprised of limited echoes. Such signals can be assumed to have a sparse representation in a proper signal dictionary. Recently, a number of digital signal processing techniques have been developed by exploiting the sparse constraint. This paper presents a review of research to date, showing the up-to-date developments of signal processing techniques made in ultrasonic NDE. A few typical ultrasonic signal processing techniques used for NDE of multilayered structures are elaborated. The practical applications and limitations of different signal processing methods in ultrasonic NDE of multilayered structures are analysed.

Through lifetime monitoring of solder joints using acoustic micro imaging

Purpose – The purpose of this paper is to evaluate the application of an acoustic micro‐imaging (AMI) inspection technique in monitoring solder joints through lifetime performance and demonstrate the robustness of the monitoring through analysis of AMI data.Design/methodology/approach – Accelerated thermal cycling (ATC) test data on a flip chip test board were collected through AMI imaging. Subsequently, informative features and parameters of solder joints in acoustic images were measured and analysed. Through analysing histogram distance, mean intensity and grey area of the solder joints in acoustic images, cracks between the solder bump and chip interface were tracked and monitored. The results are in accord with associated Finite Element (FE) prediction.Findings – At defective bumps, the formation of a crack causes a larger acoustic impedance mismatch which provides a stronger ultrasound reflection. The intensity of solder joints in the acoustic image increase according to the level of damage during th...

Resolution improvement of acoustic microimaging by continuous wavelet transform for semiconductor inspection

Abstract Acoustic microimaging (AMI) is used as an important non-destructive tool in semiconductor reliability evaluation and failure analysis. As advanced microelectronic packages are being produced smaller and thinner, detection of the internal features and defects in the packages is approaching the resolution limits for conventional AMI. To meet this challenge, an acoustic time–frequency domain imaging technique is proposed in this paper, which utilizes the excellent time–frequency localization characteristics of the continuous wavelet transform (CWT) to improve the axial resolution of AMI, without increasing acoustic frequencies. The proposed technique is compared to time domain AMI, frequency domain AMI and sparse signal representation based AMI (SSRAMI) with respect to both axial resolution and robustness. Simulation results show that the proposed technique has superior performance compared to time domain and frequency domain AMI techniques, and has close performance to SSRAMI but with less computation load.

Adaptive sparse representations of ultrasonic signals for acoustic microimaging

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 reflectiv...

An improved acoustic microimaging technique with learning overcomplete representation

Advancements in integrated circuit (IC) package technology are increasingly leading to size shrinkage of modern microelectronic packages. This size reduction presents a challenge for the detection and location of the internal features/defects in the packages, which have approached the resolution limit of conventional acoustic microimaging, an important nondestructive inspection technique in the semiconductor industry. In this paper, to meet the challenge the learning overcomplete representation technique is pursued to decompose an ultrasonic A-scan signal into overcomplete representations over a learned overcomplete dictionary. Ultrasonic echo separation and reflectivity function estimation are then performed by exploiting the sparse representability of ultrasonic pulses. An improved acoustic microimaging technique is proposed by integrating these operations into the conventional acoustic microimaging technique. Its performance is quantitatively evaluated by elaborated experiments on ultrasonic A-scan sig...