&lt;title&gt;Thermal inspection of solder quality of electronic components&lt;/title&gt;

Varis, J.; Lehtiniemi, R.; Vuohelainen, R.

doi:10.1117/12.342279

Cited by 1 publication

(2 citation statements)

References 2 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Breitenstein et al [40] detected current leakage in integrated circuits, the current leakage were heat sources that appeared as high temperature regions in infrared images. Artificial solder defects in ball grid array electronic components were detected by Varis et al [53]; however the abnormal thermal signature was detected only after removing a plastic cover from the electronic components. Finally, Hsieh et al [54] characterized thermal profiles of chips under vibration stress; spots more susceptible to vibration stress presented a noticeable increase in temperature that could be monitored using infrared thermography.…”

Section: Introductionmentioning

confidence: 98%

“…Thermal profiles have been obtained and used to characterize interconnects in electronics by mean of infrared imaging [40,[50][51][52][53][54]. Boguslaw et al [50] used a thermographic camera to characterize solder thickness on printed circuit boards based on thermal transient response; thermal transient response was found to vary with solder thickness as a consequence of thermal conductivity and capacity.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Finite element analysis and neural network model for electronic hidden solder joint geometry prediction

Palomares

Hsieh

2010

SPIE Proceedings

View full text Add to dashboard Cite

This paper investigates an active thermography approach to probing hidden solder joint geometry. Ten boards were fabricated with the same number of solder joints and amount of solder paste (0.061 g), but using three solder joint geometries (60°, 90°, and 120°). The 90° angle solder pin represented a normal joint, and the 60° and 120° angle pins represented abnormal solder joints. Each board was covered with another board that had three openings just big enough to allow the pin terminals to protrude. A semi-automated system was built to heat and then transfer each board set to a chamber where an infrared camera was used to scan the board as it was cooling down. Each board set underwent the heating, cooling, and scanning process for five trials. Two-thirds of the data set was used for model development and one-third for model evaluation. An artificial neural network (ANN) was constructed to predict abnormal joints given thermal data. Results suggest that solder joints with more surface area cool much faster than those with less surface area. A Finite Element Analysis (FEA) of the heating up and cooling down process consistently predicted solder geometry using the ANN with 86% accuracy. This approach can be used not only to inspect bad solder joints (i.e., low reliability) but also to mass screen for cold solder joints during BGA assembly, since the air gaps in cold solder joints may cause them to cool more slowly than normal joints.

show abstract

Section: Introductionmentioning

confidence: 98%