Solder paste reflow modeling for flip chip assembly

Mannan, Samjid H.; Wheeler, Daniel; Hutt, David A.; Whalley, D.C.; Conway, Paul; Bailey, Christopher

doi:10.1109/eptc.2000.906357

Cited by 6 publications

(2 citation statements)

References 4 publications

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“…Using a value of 100 Pas for μ , σ = 0.4 N/m and 10 μm for l , we obtain t = 2.5 ms which represents the time‐scale for two solder spheres to coalesce. The axisymmetric models reproduce this result well (Mannan et al , 2000). If we now consider, a chain of n particles, we obtain a coalescence time‐scale of 2.5 n ms, which agrees well with the results in Figure 5 (time‐scale = 34 ms).…”

Section: ‐D Models Of Solder Paste Coalescence Without Oxidesmentioning

confidence: 65%

Solder paste reflow modeling

Mannan

2002

Soldering &Amp; Surface Mount Technology

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Solder paste printing and reflow are well established processes for producing solder joints in electronic assemblies. Solder paste consists of a dense suspension of solder particles in a liquid medium (vehicle) that acts as an oxide reducing agent (flux) during reflow, cleaning the metal surfaces of oxides. This paper reports on attempts to model the physical and chemical processes occurring during solder paste reflow using computational fluid dynamics (CRD). Axisymmetric, 2 dimensional and 3‐dimensional models are described, and a method of reproducing oxide‐like behaviour in these models in introduced.

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Section: ‐D Models Of Solder Paste Coalescence Without Oxidesmentioning

confidence: 65%

Solder paste reflow modeling

Mannan

2002

Soldering &Amp; Surface Mount Technology

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“…Gao provided a convenient method to develop reflow recipes for a specific reflow profile (Gao et al , 2008). Mannan studied the behavior of solder paste using computational fluid dynamics (CFD) modeling (Mannan et al , 2000). Abdul Aziz presents an effective numerical method to study pin shape during wave soldering (Abdul Aziz et al , 2014).…”

Section: Introductionmentioning

confidence: 99%

Reflow profiling with the aid of machine learning models

Lai

Park

2023

SSMT

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Purpose This paper aims to propose a method to quickly set the heating zone temperatures and conveyor speed of the reflow oven. This novel approach intensely eases the trial and error in reflow profiling and is especially helpful when reflowing thick printed circuit boards (PCBs) with bulky components. Machine learning (ML) models can reduce the time required for profiling from at least half a day of trial and error to just 1 h. Design/methodology/approach A highly compact computational fluid dynamics (CFD) model was used to simulate the reflow process, exhibiting an error rate of less than 1.5%. Validated models were used to generate data for training regression models. By leveraging a set of experiment results, the unknown input factors (i.e. the heat capacities of the bulkiest component and PCB) can be determined inversely. The trained Gaussian process regression models are then used to perform virtual reflow optimization while allowing a 4°C tolerance for peak temperatures. Upon ensuring that the profiles are inside the safe zone, the corresponding reflow recipes can be implemented to set up the reflow oven. Findings ML algorithms can be used to interpolate sparse data and provide speedy responses to simulate the reflow profile. This proposed approach can effectively address optimization problems involving multiple factors. Practical implications The methodology used in this study can considerably reduce labor costs and time consumption associated with reflow profiling, which presently relies heavily on individual experience and skill. With the user interface and regression models used in this approach, reflow profiles can be swiftly simulated, facilitating iterative experiments and numerical modeling with great effectiveness. Smart reflow profiling has the potential to enhance quality control and increase throughput. Originality/value In this study, the employment of the ultimate compact CFD model eliminates the constraint of components’ configuration, as effective heat capacities are able to determine the temperature profiles of the component and PCB. The temperature profiles generated by the regression models are time-sequenced and in the same format as the CFD results. This approach considerably reduces the cost associated with training data, which is often a major challenge in the development of ML models.

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Power Package Typical Assembly Process

Liu

2011

Power Electronic Packaging

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Solder paste reflow modeling for flip chip assembly

Cited by 6 publications

References 4 publications

Solder paste reflow modeling

Solder paste reflow modeling

Reflow profiling with the aid of machine learning models

Power Package Typical Assembly Process

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