Effect of stearic acid coating on anti-oxidation property of Sn-Ag-Cu solder powder

Peng, Fen; Liu, Wensheng; Huang, Yufeng; Tang, Shibo; Liang, Chaoping; Ma, Yunzhu

doi:10.1108/ssmt-08-2018-0021

Cited by 4 publications

(1 citation statement)

References 16 publications

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“…Similar soldering problems can arise if the particles in the solder paste are oxidised, that is why a monolayer surface coating of stearic acid was developed by Peng et al to increase the anti-oxidation property of solder pastes [6]. Further improvements and process optimisation in this field can be obtained, e.g.…”

Section: Introductionmentioning

confidence: 97%

Predicting the Transfer Efficiency of Stencil Printing by Machine Learning Technique

Krammer

Al-Ma'aiteh

Martinek

et al. 2020

2020 43rd International Spring Seminar on Electronics Technology (ISSE)

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Experiment was carried out for acquiring data regarding the transfer efficiency of stencil printing, and a machine learning-based technique (artificial neural network) was trained for predicting that parameter. The input parameters space in the experiment included the printing speed at five different levels (between 20 and120 mm/s) and the area ratio of stencil apertures from 0.34 to1.69. Three types of lead-free solder paste were also investigated as follows: Type-3 (particle size range is 20-45 µm), Type-4 (20-38 µm), Type-5 (10-25 µm). The output parameter space included the height and the area of the print deposits and the respective transfer efficiency, which is the ratio of the deposited paste volume to the aperture volume. Finally, an artificial neural network was trained with the empirical data using the Levenberg-Marquardt training algorithm. The optimal tuning factor for the fine-tuning of the network size was found to be approximately 9, resulting in a hidden neuron number of 160. The trained network was able to predict the output parameters with a mean average percentage error (MAPE) lower than 3%. Though, the prediction error depended on the values of the input parameters, which is elaborated in the paper in details. The research proved the applicability of machine learning techniques in the yield prediction of the process of stencil printing.

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Section: Introductionmentioning

confidence: 97%