A Study on the Thermal Fatigue Behavior of Solder Joints Under Power Cycling Conditions

Failure mechanisms exposed by environmental accelerating testing methods such as thermal cycling or thermal shock test, may differ from those at service operating conditions. While the device is heated up or cooled down evenly on its external surface during environmental testing, real operating powered devices experience temperature gradients caused by internal local heating, components' different heat dissipation capability, and ambient temperature variation, etc. In this study, a power cycling technique is introduced to better approximate the field operating conditions so as to activate the field failure modes. Power cycling thermal fatigue test is performed with different ball grid array solder joints, that is, lead contained [Sn/37 Pb (SP)] and lead free [Sn/4.0Ag/0.5 Cu (SAC)], and the result is compared. In order to account for the thermal fatigue life behavior discrepancy for different solder joint composition, real time Moire interferometry is applied to measure the global/local thermo-mechanical behavior during power cycling excursion. Effective damage parameter, the total average shear strain, is extracted from the experiment and applied to account for the difference in fatigue life result of two different solders. In addition, amount of experimentally measured total average shear strain is mutually verified with finite element method analysis. It is clear that total average shear strain of a solder joint can be an effective damage parameter to predict thermo-mechanical fatigue life. A physical mechanism in terms of thermal material property of solder joints' is proposed to offer some thoughts to abnormal shear strain behavior that leads to discrepancies in fatigue life of two solders. An importance of power cycling testing method is emphasized for certain package designs.     

Fatigue and fracture assessment for reliability in electronics packaging

Modern electronics products relentlessly become more complex, higher in density and speed, and thinner and lighter for greater portability. The package of these products is therefore critical. The reliability of the interconnection of electronics packaging has become a critical issue. In this study, the novel testing methods for electronic packaging are introduced and failure mechanisms of electronic packaging are explained. Electronics packaging is subjected to mechanical vibration and thermal cyclic loads which lead to fatigue crack initiation, propagation and the ultimate fracture of the packaging. A small-sized electromagnetic-type bending cycling tester, a micro-mechanical testing machine, and thermal fatigue testing apparatus were specially developed for the reliability assessment of electronics packaging. The long-term reliability of an electronic component under cyclic bending induced high-cycle fatigue was assessed. The high-cycle bending-fatigue test was performed using an electromagnetic-type testing machine. The time to failure was determined by measuring the changes in resistance. Using the micro-mechanical tester, low cycle fatigues were performed and compared with the results of a finite element analysis to investigate the optimal shape of solder bumps in electronic packaging. Fatigue tests on various lead-free solder materials are discussed. To assess the resistance against thermal loads, pseudo-power cycling method is developed. Thermal fatigue tests of lead-containing and lead-free solder joints of electronic packaging were performed using the pseudo-power cycling tester. The results from the thermal fatigue tests are compared with the mechanical fatigue data in terms of the inelastic energy dissipation per cycle. It was found that the mechanical load has a longer fatigue life than the thermal load at the same inelastic energy dissipation per cycle.     

Long-term stagnation monitoring using machine learning: comparison of artificial neural network model and convolution neural network model

In this study, a device to diffuse the flow of water in a horizontal direction was installed over a small river connected to Nakdonggang River and the dissolved oxygen (DO) concentration within the range of its influence was monitored. A DO probe was installed and operated at three depths of water; the surface layer, middle layer and deep layer. In order to judge stagnant water by operating and controlling the device automatically, an artificial neural network model that worked through profiling by logics and expert learning was applied. For expert learning, the number of all cases generated from DO data was labeled based on expert judgment. In other words, when DO concentration was divided into 7 levels, the number of cases was 343, the experts were requested to determine whether each case was a stagnant water. Machine learning was carried out targeting labelling by experts with the artificial neural network (ANN) and the convolution neural network (CNN). The target datasets for learning were 3?×?1 based on numbers from 1 to 7 and 7?×?7 based on the dot graph. The correct ratio for the ANN model learning result based on the graph was only 29.2%, so it was excluded. The correct ratio for the ANN model learning result based on numbers was 87.2%. The correct ratio for the CNN based on the graph was 94.2%. When machine learning was carried out with 30 to 300 randomly selected targeted graphs, the ANN model showed 74.6% as the correct ratio for up to 150 graphs, which was somewhat low, while the CNN showed 84.3% for 30 graphs and 94.2% for 200 graphs, a gradual increase with results comparable to the total number of graphs. By applying the relevant control logics to actual monitoring results, 91.5% and 87.4% was judged to be stagnant water from points directly and indirectly affected by the device, respectively.

     

Ozone treatment process for the removal of pharmaceuticals and personal care products in wastewater

This study investigated the degradability of pharmaceuticals and personal care products (PPCPs) by ozonation for the treatment of secondary effluent of a municipal wastewater treatment plant. A set of experiments were conducted in a laboratory using a pilot-scale process consisting of three flow-through reactors in series, by varying the ozone dose (1–9 mg L^?1), the hydraulic retention time (5–15 min), and the concentration of ozone injected into the reactors (14–42 mg L^?1). Thirty-seven PPCPs were detected in the secondary effluent, which belongs to the use categories of antibiotics, analgesics, antiarrhythmic agents, anticonvulsants, vasodilators, lipid modifying agents, anti-itch drugs, anti-psychotic drugs, insect repellents, bronchodilators, diuretics, peptic ulcer drugs, NMDA receptor antagonists, antifungal drugs, antimicrobial drugs, and antineoplastic agents. These PPCPs were broadly classified into five groups ranging from “sensitive” to ozone (O₃) or “unstable” in the ozonation process, to the group of “insensitive” to O₃ or “very stable” in the ozonation process. These groups are based on the PPCP concentrations after the ozone treatment and their limit of detection (LOD). Furthermore, this study examined comparatively the effects of the ozone dose, the retention (reaction) time, and the concentration of O₃ supplied to the reactors on the degradability of the PPCPs.     

Bioprinting of Multiscaled Hepatic Lobules within a Highly Vascularized Construct

Highly vascularized complex liver tissue is generally divided into lobes, lobules, hepatocytes, and sinusoids, which can be viewed under different types of lens from the micro‐ to macro‐scale. To engineer multiscaled heterogeneous tissues, a sophisticated and rapid tissue engineering approach is required, such as advanced 3D bioprinting. In this study, a preset extrusion bioprinting technique, which can create heterogeneous, multicellular, and multimaterial structures simultaneously, is utilized for creating a hepatic lobule (≈1 mm) array. The fabricated hepatic lobules include hepatic cells, endothelial cells, and a lumen. The endothelial cells surround the hepatic cells, the exterior of the lobules, the lumen, and finally, become interconnected with each other. Compared to hepatic cell/endothelial cell mixtures, the fabricated hepatic lobule shows higher albumin secretion, urea production, and albumin, MRP2, and CD31 protein levels, as well as, cytochrome P450 enzyme activity. It is found that each cell type with spatial cell patterning in bioink accelerates cellular organization, which could preserve structural integrity and improve cellular functions. In conclusion, preset extruded hepatic lobules within a highly vascularized construct are successfully constructed, enabling both micro‐ and macro‐scale tissue fabrication, which can support the creation of large 3D tissue constructs for multiscale tissue engineering.     

Preparation of Cu/ZnO catalyst using a polyol method for alcohol-assisted low temperature methanol synthesis from syngas

A polyol method was used to prepare Cu/ZnO catalysts for alcohol-assisted low temperature methanol synthesis from syngas. Unlike conventional low temperature methanol synthesis, ethanol was employed both as a solvent and a reaction intermediate. Catalyst characterization revealed that Cu/ZnO catalysts were successfully and efficiently prepared using the polyol method. Various preparation conditions such as PVP concentration and identity of ZnO precursor strongly influenced the catalytic activity of Cu/ZnO catalysts. Copper dispersion and catalyst morphology played key roles in determining the catalytic performance of the Cu/ZnO catalyst in alcohol-assisted low temperature methanol synthesis. A high copper dispersion and platelike Cu/ZnO structure led to high catalytic activity. Among the catalysts tested, 5_Cu/ZnO_Zn(Ac)₂ had the best catalytic performance due to its high copper dispersion.     

Navigation-oriented design for in-pipe robot in recursively divided sampling space with rapidly exploring random tree

Gas pipelines are subject to periodic inspection and maintenance for safety and longevity. Many robotic inspection systems have been developed for in-pipe applications, but systematic geometric design methodology that is suitable for in-pipe navigation has not been well studied so far due to difficulties in predicting the capability of maneuvering through the obstacles inside of pipelines such as bend, miter, and T-branch joint. The geometric design of the robot is critical to the performance of such in-pipe robots because the actuation and the measurement are constrained by the shape and the size of the robot. In this paper, we propose a design methodology that finds the maximum value of geometric design parameters of the robot with recursive evaluation of the parameter values in the design parameter space. The role of the design space division is to reduce the search region and to increase the number of parametric samples to near optimal values. As a parameter evaluation method, we adapt Rapidly exploring random tree (RRT) because it is known to be suitable for solving narrow passage problems for high-dimensional systems. Our design method makes it possible to find an optimal parameter set without computing complex cost functions. The design result of the in-pipe robot is 8 % larger than that of a heuristic geometry-based approach in three-parameter design problem.

     

Efficient multiquality super‐resolution using a deep convolutional neural network for an FPGA implementation

We propose an efficient deep convolutional neural network for a super‐resolution which is capable of multiple‐quality input, by analyzing the input quality and choosing appropriate features automatically. To implement the network in an FPGA and an ASIC, we employ a network trimming technique to compress the neural network.     

Development of a Novel Microimpact-Fatigue Tester and Its Application to Impact Reliability of Lead-Free Solder Joints

Drop-impact forces cause portable electronic devices to fail. Assessment of resistance against drop-impact force is required to predict life of an electronic package. Several methods have been used to evaluate impact-fatigue life and other advanced methods have been proposed. However, such conventional impact tests require excessive time and cost. In this paper, a novel micro-impact-fatigue tester is developed to overcome such drawbacks of conventional methods. A newly developed impact-fatigue apparatus directly applies impact force to solder joints and measures deformation of the solder joints. The impact-fatigue test apparatus consists of an electromagnetic actuator, an impact-pin, a load-cell, a displacement sensor, and a main frame. Electromagnetic actuator produces a repeatable impact force with a changeable amplitude and pulse duration. Impact-fatigue apparatus was used to test reliability of a lead-free solder (96.5Sn4.0Ag0.5Cu). An evaluation of impact-fatigue life was performed over a wide range of 1-10⁴ cycles with various applied forces ranging from 40 N to 110 N. Two failure modes were observed in a section inspection. First type of failure was a mixed mode failure, where a bulk solder failure and interface failure coincide and the relation between the impact load and fatigue life is almost linear. Stress-based life-prediction model is proposed for the mixed mode failure. The second type of failure, interface failure between the Ni(P) layer and the solder, occurs under a high-load condition. Fatigue life is shorter in the second type of failure than in the mixed mode failure. Brittleness of the interface reduces the impact-fatigue life.