Characterization of oxidized copper leadframes and copper/epoxy molding compound interface adhesion in plastic package

Kang, Teck-Gyu; Park, I.-S.; Kim, J.-H.; Choi, Kwang‐Seong

doi:10.1109/adhes.1998.742011

Cited by 8 publications

(2 citation statements)

References 9 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…For example, there are some sources in the literature, which found a critical oxide layer thickness for adhesion of mould compounds that could be set as a scope for production. [42][43][44][45][46] By implementing the HSI system to a production line, this parameter could be tracked for all parts. In the end the use of a HSI system leads to an extreme reduction of time.…”

Section: Prediction Based On Plsrmentioning

confidence: 99%

Characterisation of oxide layers on technical copper based on visible hyperspectral imaging

Stiedl

Azemtsop

Boldrini

et al. 2019

J. Spectral Imaging

View full text Add to dashboard Cite

The detection and characterisation of oxide layers on metallic copper samples plays an important role for power electronic modules in the automotive industry. However, since precise identification of oxide layers by visual inspection is difficult and time consuming due to inhomogeneous colour distribution, a reliable and efficient method for estimating their thickness is needed. In this study, hyperspectral imaging in the visible wavelength range (425–725 nm) is proposed as an in-line inspection method for analysing oxide layers in real-time during processing of copper components such as printed circuit boards in the automotive industry. For implementation in the production line a partial least square regression (PLSR) model was developed with a calibration set of n = 12 with about 13,000 spectra per sample to determine the oxide layer thickness on top of the technical copper surfaces. The model shows a good prediction performance in the range of 0–30 nm compared to Auger electron spectroscopy depth profiles as a reference method. The root mean square error (RMSE) is 1.75 nm for calibration and 2.70 nm for full cross-validation. Applied to an external dataset of four new samples with about 13,000 spectra per sample the model provides an RMSE of 1.84 nm for prediction and demonstrates the robustness of the model during real-time processing. The results of this study prove the ability and usefulness of the proposed method to estimate the thickness of oxide layers on technical copper. Hence, the application of hyperspectral imaging for the industrial process control of electronic devices is very promising.

show abstract

Section: Prediction Based On Plsrmentioning

confidence: 99%

Characterisation of oxide layers on technical copper based on visible hyperspectral imaging

Stiedl

Azemtsop

Boldrini

et al. 2019

J. Spectral Imaging

View full text Add to dashboard Cite

show abstract

“…It was shown that oxides and contaminations on the surface were linked to an increase of or a decrease in the adhesion of processes such as wire bonding, gluing, or molding. For oxides, the adhesion increases until the oxide reaches a critical thickness [4][5][6][7][8]. Lower organic contamination leads to the improved adhesion of these processes.…”

Section: Introductionmentioning

confidence: 99%

Use of Hyperspectral Imaging for the Quantification of Organic Contaminants on Copper Surfaces for Electronic Applications

Englert

Gruber

Stiedl

et al. 2021

Sensors

View full text Add to dashboard Cite

To correctly assess the cleanliness of technical surfaces in a production process, corresponding online monitoring systems must provide sufficient data. A promising method for fast, large-area, and non-contact monitoring is hyperspectral imaging (HSI), which was used in this paper for the detection and quantification of organic surface contaminations. Depending on the cleaning parameter constellation, different levels of organic residues remained on the surface. Afterwards, the cleanliness was determined by the carbon content in the atom percent on the sample surfaces, characterized by XPS and AES. The HSI data and the XPS measurements were correlated, using machine learning methods, to generate a predictive model for the carbon content of the surface. The regression algorithms elastic net, random forest regression, and support vector machine regression were used. Overall, the developed method was able to quantify organic contaminations on technical surfaces. The best regression model found was a random forest model, which achieved an R2 of 0.7 and an RMSE of 7.65 At.-% C. Due to the easy-to-use measurement and the fast evaluation by machine learning, the method seems suitable for an online monitoring system. However, the results also show that further experiments are necessary to improve the quality of the prediction models.

show abstract

Oxide adhesion characteristic of lead frame copper alloys

Tomioka¹,

Miyake²

1999 Proceedings. 49th Electronic Components and Technology Conference (Cat. No.99CH36299)

View full text Add to dashboard Cite

Characterization of oxidized copper leadframes and copper/epoxy molding compound interface adhesion in plastic package

Cited by 8 publications

References 9 publications

Characterisation of oxide layers on technical copper based on visible hyperspectral imaging

Characterisation of oxide layers on technical copper based on visible hyperspectral imaging

Use of Hyperspectral Imaging for the Quantification of Organic Contaminants on Copper Surfaces for Electronic Applications

Oxide adhesion characteristic of lead frame copper alloys

Contact Info

Product

Resources

About