Measurement of the Hygroscopic Swelling Coefficient of Thin Film Polymers Used in Semiconductor Packaging

Jang, Changheui; Yoon, Sung-Hoon; Han, Bongtae

doi:10.1109/tcapt.2009.2038366

Cited by 26 publications

(4 citation statements)

References 16 publications

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“…2. The lower temperature (,100 C) CHS results of 0.2-0.4 from this study is within similar range of other mold compounds from publications [2,8,9,12,20,21]. For elevated temperatures, 150 to 220 o C, the CHS results of approximately 1.0-1.8 are trending comparatively higher than the few published data of 0.5-1.6 [12,21].…”

Section: Hygroscopic Swellingsupporting

confidence: 84%

A Novel Approach for Characterizing Epoxy Mold Compound High Temperature Swelling

Chin,

Loh,

Bin Abdullah

2023

Journal of Microelectronics and Electronic Packaging

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In this paper, a novel method for measuring hightemperature material swelling of an epoxy mold compound is explored. This work attempts to extend the swelling characterization beyond the capability of typical commercial tools. The approach uses a high-pressure chamber to maintain a saturated liquid environment for the specimen. Digital image correlation is used to measure in-situ strain change in the specimen due to hygro-thermal expansion. The results show moisture-induced swelling increases with temperature and is significant compared with thermal expansion. Results are compared against other measurement methods and published data. This has yielded good fundamental learnings for the novel concept and identified areas for future work.

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Section: Hygroscopic Swellingsupporting

confidence: 84%

A Novel Approach for Characterizing Epoxy Mold Compound High Temperature Swelling

Chin,

Loh,

Bin Abdullah

2023

Journal of Microelectronics and Electronic Packaging

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“…Moisture-related failure, such as the notorious popcorning failure during reflow soldering, is an important failure type for electronic components. Numerous researchers have been engaged in moisture-related research activities, such as measuring the diffusivity, solubility [1,2], and coefficient of hygroscopic swelling (CHS) [3][4][5], and studying the failure mechanisms [6,7]. In recent years, computational methods have been used to determine the amount, distribution, and subsequent effects of moisture.…”

Section: Prior Arts On Moisture Diffusion Simulation With Normalized mentioning

confidence: 99%

“…where S is the solubility and P VP is the ambient vapor pressure of the environment, an alternative way is to define, / ¼ C=S (5) as the normalized concentration, and in this case, the ambient vapor pressure P VP is treated as a boundary condition. By substituting concentration C with S/ in Eq.…”

Section: Prior Arts On Moisture Diffusion Simulation With Normalized mentioning

confidence: 99%

A Note on the Normalized Approach to Simulating Moisture Diffusion in a Multimaterial System Under Transient Thermal Conditions Using ansys 14 and 14.5

Liu

Park

2014

Journal of Electronic Packaging

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Moisture can have significant effects on the performance and reliability of electronic components. Accurately simulating moisture diffusion is important for designers and manufacturers to obtain a realistic reliability evaluation. Beginning with version 14, ansys is capable of simulating diffusion and related behaviors, such as hygroscopic swelling, with newly developed elements. However, a normalized approach is still required to deal with the discontinuity of concentrations at the material boundaries, and normalization of the moisture concentration in transient thermal conditions is tricky. Case studies have shown that normalizing the moisture concentration with respect to a time- or temperature-dependent material property will lead to erroneous results. This paper re-addresses the issues of performing diffusion simulations under transient thermal conditions and more general anisothermal conditions (temporally and spatially), and suggests an easy-to-use approach to cope with the limitations of the current version for users in the electronic packaging industry.

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“…The light-emitting diode (LED) has more and more application fields in recent years. The LED is an attractive alternative to conventional light sources owing to lower power requirement, longer lifetime [1,2]. The basic structure of a LED consists of light-emitting semiconductor chip, a lead frame where the chip is actually placed, and the encapsulation epoxy that surrounds and protects the chip.…”

Section: Introductionmentioning

confidence: 99%

Defect Inspection of LED Chips Using Generalized Regression Neural Network

Pan

Ling

2011

SSP

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The inspection of the defects in LED chip has become a critical task for manufacturers in order to enhance product quality. In this paper, a new approach for the defect inspection of LED chip is presented, which uses both the features of defects and the generalized regression neural networks. The approach consists of following three steps. First of all, preprocess of LED chip image is performed by using the image operations such as image enhancement. Secondly, the chip image is divided into a lot of sub-regions, the features of each sub-region are extracted, the database of features is built. Thirdly, an initial structure of generalized regression neural network is constructed, then the neural network is trained by using the features in database. The generalized regression neural network has the ability to converge to the underlying function of the data with only few training samples available, and the additional knowledge needed to input by the user is relatively small. The experimental results show that the defect inspection approach in this paper can effectively identify the LED chips with defects.

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Measurement of the Hygroscopic Swelling Coefficient of Thin Film Polymers Used in Semiconductor Packaging

Cited by 26 publications

References 16 publications

A Novel Approach for Characterizing Epoxy Mold Compound High Temperature Swelling

A Novel Approach for Characterizing Epoxy Mold Compound High Temperature Swelling

A Note on the Normalized Approach to Simulating Moisture Diffusion in a Multimaterial System Under Transient Thermal Conditions Using ansys 14 and 14.5

Defect Inspection of LED Chips Using Generalized Regression Neural Network

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