Search

Low-temperature solderless Cu–Cu bonding is an important technology for advanced packaging, such as fine-pitch 3-D packaging and heterogeneous integration. In this study, we prepared an oxidation-free Cu surface using an optimized N2 plasma process in a two-step Ar/N2 plasma treatment based on the design of the experiment method, to improve direct Cu–Cu bonding quality at 260 °C. The N2 plasma treatment process was optimized using the Minitab optimizer with chemical state results obtained by X-ray photoelectron spectroscopy (XPS) analysis. The Cu surface treated under the two-step Ar/N2 plasma with optimized N2 plasma conditions not only formed Cu nitride well, but the surface remained without further oxidation for at least one week at room temperature. The Cu–Cu bonding was performed at the low bonding temperature of 260 °C and low bonding pressure of 0.9 MPa for 1 hour, and the bonded interface was evaluated using scanning acoustic tomography (SAT) and field emission scanning electron microscope (FE-SEM) images. We measured shear strength to estimate the bonding interface quality of the oxidation-free Cu–Cu bonding specimen. A maximum shear strength of 62.6 MPa was obtained. Our bonding results demonstrated remarkably improved Cu–Cu bonding quality compared with other previous Cu–Cu joint studies that used Sn, Cu/Ag nanoparticles, or Cu composites.

Biocidal products are broadly used in homes and industries. However, the safety of biocidal active substances (BASs) is not yet fully understood. In particular, the neurotoxic action of BASs needs to be studied as diverse epidemiological studies have reported associations between exposure to BASs and neural diseases. In this study, we developed in silico models to predict the blood-brain barrier (BBB) permeation of organic and inorganic BASs. Due to a lack of BBB data for BASs, the chemical space of BASs and BBB dataset were compared in order to select BBB data that were structurally similar to BASs. In silico models to predict log-scaled BBB penetration were developed using support vector regression for organic BASs and multiple linear regression for inorganic BASs. The model for organic BASs was developed with 231 compounds (training set: 153 and test set: 78) and achieved good prediction accuracy on an external test set (R2 = 0.64), and the model outperformed the model for pharmaceuticals. The model for inorganic BASs was developed with 11 compounds (R2 = 0.51). Applicability domain (AD) analysis of the models clarified molecular structures reliably predicted by the models. Therefore, the models developed in this study can be used for predicting BBB permeable BASs in human. These models were developed according to the Quantitative Structure-Activity Relationship validation principles proposed by the Organization for Economic Cooperation and Development.