Optimization of reflow profile for copper pillar with SAC305 solder cap FCCSP

Zainudin, Wan Zamir Zakwan Wan; Tan, C.Y.; Hui, Tan Cai; Yap, Boon Kar; Wong, Yew Hoong

doi:10.1007/s10854-022-09661-0

Cited by 1 publication

(3 citation statements)

References 32 publications

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“…To calculate the value of ρ, the natural logarithm (ln) function is used, which takes the argument as the result of √𝑦 2 + 𝑥 2 . The natural logarithm (ln) function is mathematically defined as the inverse of the exponential function with the base e. In Eq.…”

Section: Log-polar Transformmentioning

confidence: 99%

“…( 4), the ln function is used to compute the exponent value that utilizes the base e (approximately 2.71828). In other words, the ln function seeks the exponent value that satisfies the equation 𝑒 𝜌 = √𝑦 2 + 𝑥 2 .…”

Section: Log-polar Transformmentioning

confidence: 99%

“…Poor solder joints can result in product damage and even pose risks to human safety. Various defects in solder joints are caused by the difficulty in maintaining accuracy in mechanical and chemical parameters during the soldering process [1,2]. Due to the various risks of defects in electronic circuit manufacturing, solder joint inspection becomes an essential aspect to ensure that the produced products have high-quality solder joints and meet the required standards [3].…”

Section: Introductionmentioning

confidence: 99%

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Enhancing GLCM-Based Feature Extraction for THT Solder Joint Inspection through Log Polar Transformation

2024

IJIES

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Quality inspection of solder connections in electronic circuit manufacturing is commonly performed using automatic optical inspection (AOI) technology. The utilization of deep learning in AOI has demonstrated high accuracy and fast computation, yet it requires expensive graphics processing unit (GPU) equipped computers. To enable broader and more cost-effective utilization, there is a need for an AOI model that can be embedded in simple central processing unit (CPU) based computers. To pursue this objective, several research efforts have been undertaken to develop AOI models based on classical machine learning techniques. However, the accuracy and speed achieved by these models have not yet matched deep learning-based AOI models. This study aims to enhance the computational efficiency of classic machine learning by processing only pixels containing textural information. The effectiveness enhancement is achieved through the application of log-polar transformation in the extraction of texture features using the gray level co-occurrence matrix (GLCM) to detect defects in trough hole technology (THT) solder joint connections. By transforming cartesian coordinates into polar coordinates, the textural areas to be detected assume a square shape, facilitating efficient texture feature extraction. To ensure a significant improvement in performance, a comparative performance evaluation is conducted on classic machine learning-based AOI models with and without the log-polar transformation. The texture features extracted from both models are classified using the support vector machine (SVM) method. Model testing and evaluation reveals that the proposed enhancement effort is capable of increasing accuracy levels to 95% and reach computation time by 120 milliseconds.

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Section: Log-polar Transformmentioning

confidence: 99%

Section: Log-polar Transformmentioning

confidence: 99%