• Journal of Mechanical Science and Technology
• /
• v.19 no.3
• /
• pp.820-825
• /
• 2005

This paper proposes non-destructive ESPI technique to evaluate inside defects of semiconductor package quantitatively. Inspection system consists of ESPI system, thermal loading system and adiabatic chamber. The technique has high feasibility in non-destructive testing of semiconductor and gives solutions to the drawbacks in previous technique, time-consuming and the difficulty of quantitative evaluation. In result, most of defects are classified in delamination, from which it is inferred to the insufficiency of adhesive strength between layers and nonhomogeneous heat spread. The $90\%$ of tested samples have a delamination defect started at the around of the chip which may be related to heat spread design.

• International Journal of Precision Engineering and Manufacturing
• /
• v.5 no.3
• /
• pp.11-18
• /
• 2004

In this paper, the classification of artificial defects in semiconductor packages is studied by using pattern recognition technology. For this purpose, the pattern recognition algorithm includes the user made MATLAB code. And preprocess is made of the image process and self-organizing map, which is the input of the back-propagation neural network and the dimensionality reduction method, The image process steps are data acquisition, equalization, binary and edge detection. Image process and self-organizing map are compared to the preprocess method. Also the pattern recognition technology is applied to classify two kinds of defects in semiconductor packages: cracks and delaminations.

• Journal of the Korean Society for Nondestructive Testing
• /
• v.21 no.6
• /
• pp.598-606
• /
• 2001

Recently, semiconductor packages trend to be thinner, which makes difficult to detect defects therein. A preconditioning test is generally performed to evaluate the reliability of semiconductor packages. The test procedure includes two scanning acoustic microscope (SAM) tests at the beginning and end of the entire test, in order to help detect physical defects such as delaminations and package cracks. In particular, of primary concern are package cracks and delaminations caused by moisture absorbed under ambient conditions. This paper discusses the failure mechanism associated with the moisture absorbed and encapsulated in semiconductors, and the use SAM to detect failures such as tracks and delaminations grown during the preconditioning test.

• Transactions of the Korean Society of Machine Tool Engineers
• /
• v.15 no.6
• /
• pp.82-87
• /
• 2006

Availability of defect test algorithm that recognizes exact and standardized defect information in order to fundamentally resolve generated defects in industrial sites by giving artificial intelligence to SAT(Scanning Acoustic Tomograph), which previously depended on operator's decision, to find various defect information in a semiconductor package, to decide defect pattern, to reduce personal errors and then to standardize the test process was verified. In order to apply the algorithm to the lately emerging Neural Network theory, various weights were used to derive results for performance advancement plans of the defect test algorithm that promises excellent field applicability.

• Proceedings of the KSME Conference
• /
• 2003.11a
• /
• pp.1442-1447
• /
• 2003

Computer is a very powerful machine which is widely using for data processing, DB construction, peripheral device control, image processing etc. Consequently, many researches and developments have progressed for high performance processing unit, and other devices. Especially, the core units such as semiconductor parts are rapidly growing so that high-integration, high-performance, microminiat turization is possible. The packaging in the semiconductor industry is very important technique to de determine the performance of the system that the semiconductor is used. In this paper, the inspection of the inner defects such as delamination, void, crack, etc. in the semiconductor packages is studied. ESPI which is a non-contact, non-destructive, and full-field inspection method is used for the inner defect inspection and its results are compared with that of C-Scan method.

• Proceedings of the Korean Society of Precision Engineering Conference
• /
• 2002.10a
• /
• pp.345-350
• /
• 2002

In this study, researchers classifying the artificial flaws in semiconductor packages are performed by pattern recognition technology. For this purposes, image pattern recognition package including the user made software was developed and total procedure including ultrasonic image acquisition, equalization filtration, binary process, edge detection and classifier design is treated by Backpropagation Neural Network. Specially, it is compared with various weights of Backpropagation Neural Network and it is compared with threshold level of edge detection in preprocessing method fur entrance into Multi-Layer Perceptron(Backpropagation Neural network). Also, the pattern recognition techniques is applied to the classification problem of defects in semiconductor packages as normal, crack, delamination. According to this results, it is possible to acquire the recognition rate of 100% for Backpropagation Neural Network.

• Proceedings of the Korean Society of Precision Engineering Conference
• /
• 2005.10a
• /
• pp.721-726
• /
• 2005

In this study, researchers classifying the artificial flaws in semiconductor. packages are performed by pattern recognition technology. For this purposes, image pattern recognition package including the user made software was developed and total procedure including ultrasonic image acquisition, equalization filtration, binary process, edge detection and classifier design is treated by Backpropagation Neural Network. Specially, it is compared with various weights of Backpropagation Neural Network and it is compared with threshold level of edge detection in preprocessing method for entrance into Multi-Layer Perceptron(Backpropagation Neural network). Also, the pattern recognition techniques is applied to the classification problem of defects in semiconductor packages as normal, crack, delamination. According to this results, it is possible to acquire the recognition rate of 100% for Backpropagation Neural Network.

• Proceedings of the Korean Society of Machine Tool Engineers Conference
• /
• 2005.05a
• /
• pp.91-96
• /
• 2005

In this study, researchers developed the estimative algorithm for artificial defects in semiconductor packages and performed it by pattern recognition technology. For this purpose, the estimative algorithm was included that researchers made software with MATLAB. The software consists of some procedures including ultrasonic image acquisition, equalization filtering, Self-Organizing Map and Backpropagation Neural Network. Self-Organizing Map and Backpropagation Neural Network are belong to methods of Neural Networks. And the pattern recognition technology has applied to classify three kinds of detective patterns in semiconductor packages: Crack, Delamination and Normal. According to the results, we were confirmed that estimative algorithm was provided the recognition rates of $75.7\%$ (for Crack) and $83_4\%$ (for Delamination) and $87.2\%$ (for Normal).

• The Journal of Korean Institute of Communications and Information Sciences
• /
• v.31 no.12C
• /
• pp.1157-1164
• /
• 2006

In this paper, we proposed a method for measuring accurate positions and sizes of package and balls in a micro BGA. To find defects of BGA accurately, we focused on finding positions of package and balls. After labeling, we detected connected components of package and balls using feature parameters. After the detection of package component, we measured position and size of package by employing rectangular model which was constructed by the package information. After the detection of the ball components, we measured positions and diameters of balls by employing circular models which were constructed by the ball informations. We did calibration based on landmarks to measure real length, and we compared the measured results with the SEM data. Finally, we found that the accuracy of the proposed method is 94% in terms of ball's radius.

• Journal of the Korea Convergence Society
• /
• v.8 no.11
• /
• pp.15-20
• /
• 2017

Recently, with the development of the IT industry, interest in computer convergence technology is increasing in various fields. Especially, in the semiconductor field, a vision system that uses a camera and computer convergence is often used to inspect semiconductor device defects in the production process. Various systems have been studied to remove noise, which is a major cause of degradation in processing of data related to these image processing systems. In this paper, we try to detect defects in BGA (Ball Grid Array) package devices by recognizing defects in advance during mass production. We propose a measurement system using a Gaussian filter, a Median filter, and an Average filter, which are widely used for noise reduction of image data Applying the proposed system to the manufacturing process of the BGA package can be used to judge whether the defect is good or not, and it is expected that productivity will be improved.