Statistical modeling of the electrical characteristics for HfO2 thin films grown by MOMBE for high-k dielectric applications

Lee, Jung Hwan; Kweon, Kyoung E.; Ko, Young-Hwan; Moon, Tae Hyoung; Myoung, Jae Min; Yun, Ilgu

doi:10.1016/j.jmatprotec.2007.10.045

Cited by 2 publications

(1 citation statement)

References 17 publications

(15 reference statements)

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“…Early-stage optimisations can be realised for example by statistical methods (Lee et al, 2008), by genetic algorithms (Zhang et al, 2008;Tsai, 2012) or by decision trees (Teixidor et al, 2015). For the soldering of through-hole components, Mach et al (2010) performed the optimisation of the wave soldering process using Taguchi orthogonal arrays; however, traditional wave soldering technology is mostly excluded from the soldering methods of through-hole components today.…”

Section: Introductionmentioning

confidence: 99%

Optimising pin-in-paste technology using gradient boosted decision trees

Martinek

Krammer

2018

SSMT

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Purpose This paper aims to present a robust prediction method for estimating the quality of electronic products assembled with pin-in-paste soldering technology. A specific board quality factor was also defined which describes the expected yield of the board assembly. Design/methodology/approach Experiments were performed to obtain the required input data for developing a prediction method based on decision tree learning techniques. A Type 4 lead-free solder paste (particle size 20–38 µm) was deposited by stencil printing with different printing speeds (from 20 mm/s to 70 mm/s) into the through-holes (0.8 mm, 1 mm, 1.1 mm, 1.4 mm) of an FR4 board. Hole-filling was investigated with X-ray analyses. Three test cases were evaluated. Findings The optimal parameters of the algorithm were determined as: subsample is 0.5, learning rate is 0.001, maximum tree depth is 6 and boosting iteration is 10,000. The mean absolute error, root mean square error and mean absolute percentage error resulted in 0.024, 0.03 and 3.5, respectively, on average for the prediction of the hole-filling value, based on the printing speed and hole-diameter after optimisation. Our method is able to predict the hole-filling in pin-in-paste technology for different through-hole diameters. Originality/value No research works are available in current literature regarding machine learning techniques for pin-in-paste technology. Therefore, we decided to develop a method using decision tree learning techniques for supporting the design of the stencil printing process for through-hole components and pin-in-paste technology. The first pass yield of the assembly can be enhanced, and the reflow soldering failures of pin-in-paste technology can be significantly reduced.

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