scholarly journals Correlation-driven machine learning for accelerated reliability assessment of solder joints in electronics

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Vahid Samavatian ◽  
Mahmud Fotuhi-Firuzabad ◽  
Majid Samavatian ◽  
Payman Dehghanian ◽  
Frede Blaabjerg
Keyword(s):  
Machine Learning ◽  
Thermal Cycling ◽  
Solder Joints ◽  
Electronic Materials ◽  
Electronic Devices ◽  
Reliability Assessment ◽  
Electronic Systems ◽  
Learning Framework ◽  
Useful Lifetime

Abstract The quantity and variety of parameters involved in the failure evolutions in solder joints under a thermo-mechanical process directs the reliability assessment of electronic devices to be frustratingly slow and expensive. To tackle this challenge, we develop a novel machine learning framework for reliability assessment of solder joints in electronic systems; we propose a correlation-driven neural network model that predicts the useful lifetime based on the materials properties, device configuration, and thermal cycling variations. The results indicate a high accuracy of the prediction model in the shortest possible time. A case study will evaluate the role of solder material and the joint thickness on the reliability of electronic devices; we will illustrate that the thermal cycling variations strongly determine the type of damage evolution, i.e., the creep or fatigue, during the operation. We will also demonstrate how an optimal selection of the solder thickness balances the damage types and considerably improves the useful lifetime. The established framework will set the stage for further exploration of electronic materials processing and offer a potential roadmap for new developments of such materials.

Role of aging temperature on thermomechanical fatigue lifetime of solder joints in electronic systems

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Waluyo Adi Siswanto ◽  
Kirill Borodin ◽  
Zaid Hamid Mahmoud ◽  
A. Surendar ◽  
Sami Sajjadifar ◽  
...  
Keyword(s):  
Shear Stress ◽  
Prediction Model ◽  
Aging Temperature ◽  
Design Methodology ◽  
Electronic Devices ◽  
Electronic Systems ◽  
Stress Tests ◽  
Fatigue Lifetime ◽  
Content Type

Purpose The purpose of this study is to investigate the effect of aging temperature on the barrel-type solder joint lifetime of electronic devices and to include these effects in the modified prediction model. Design/methodology/approach Several accelerated shear stress tests under different stress amplitudes and aging temperatures were performed. Findings It was found that by aging temperature increasing, the lifetime decreases. Morrow energy model was also found as the best prediction model when the aging temperature is taken into consideration. Originality value It is confirmed.

scholarly journals The role of ligands in coinage-metal nanoparticles for electronics

2017 ◽  
Vol 8 ◽  
pp. 2625-2639 ◽  
Author(s):  
Ioannis Kanelidis ◽  
Tobias Kraus
Keyword(s):  
Metal Nanoparticles ◽  
Self Assembly ◽  
Colloidal Stability ◽  
Chemical Nature ◽  
Electronic Materials ◽  
Electronic Devices ◽  
Coinage Metal ◽  
Conductive Composites ◽  
Particle Shapes

Coinage-metal nanoparticles are key components of many printable electronic inks. They can be combined with polymers to form conductive composites and have been used as the basis of molecular electronic devices. This review summarizes the multidimensional role of surface ligands that cover their metal cores. Ligands not only passivate crystal facets and determine growth rates and shapes; they also affect size and colloidal stability. Particle shapes can be tuned via the ligand choice while ligand length, size, ω-functionalities, and chemical nature influence shelf-life and stability of nanoparticles in dispersions. When particles are deposited, ligands affect the electrical properties of the resulting film, the morphology of particle films, and the nature of the interfaces. The effects of the ligands on sintering, cross-linking, and self-assembly of particles in electronic materials are discussed.

scholarly journals How Policies on Restaurants, Bars, Nightclubs, Masks, Schools, and Travel Influenced Swiss COVID-19 Reproduction Ratios

2020 ◽  
Author(s):  
C. K. Sruthi ◽  
Malay Ranjan Biswal ◽  
Brijesh Saraswat ◽  
Himanshu Joshi ◽  
Meher K. Prakash
Keyword(s):  
Machine Learning ◽  
Secondary Schools ◽  
Public Transport ◽  
Scientific Basis ◽  
Data Driven ◽  
Economic Activities ◽  
Model Framework ◽  
Fine Grained ◽  
Learning Framework

SummaryThe role of complete lockdowns in reducing the reproduction ratios (Rt) of COVID-19 is now established. However, the persisting reality in many countries is no longer a complete lockdown, but restrictions of varying degrees using different choices of Non-pharmaceutical interaction (NPI) policies. A scientific basis for understanding the effectiveness of these graded NPI policies in reducing the Rt is urgently needed to address the concerns on personal liberties and economic activities. In this work, we develop a systematic relation between the degrees of NPIs implemented by the 26 cantons in Switzerland during March 9 – September 13 and their respective contributions to the Rt. Using a machine learning framework, we find that Rt which should ideally be lower than 1.0, has significant contributions in the post-lockdown scenario from the different activities - restaurants (0.0523 (CI. 0.0517-0.0528)), bars (0.030 (CI. 0.029-0.030)), and nightclubs (0.154 (CI. 0.154-0.156)). Activities which keep the land-borders open (0.177 (CI. 0.175-0.178)), and tourism related activities contributed comparably 0.177 (CI. 0.175-0.178). However, international flights with a quarantine did not add further to the Rt of the cantons. The requirement of masks in public transport and secondary schools contributed to an overall 0.025 (CI. 0.018-0.030) reduction in Rt, compared to the baseline usage even when there are no mandates. Although causal relations are not guaranteed by the model framework, it nevertheless provides a fine-grained justification for the relative merits of choice and the degree of the NPIs and a data-driven strategy for mitigating Rt.

The Role of Elastic and Plastic Anisotropy of Sn in Recrystallization and Damage Evolution During Thermal Cycling in SAC305 Solder Joints

2011 ◽  
Vol 41 (2) ◽  
pp. 283-301 ◽  
Author(s):  
Thomas R. Bieler ◽  
Bite Zhou ◽  
Lauren Blair ◽  
Amir Zamiri ◽  
Payam Darbandi ◽  
...  
Keyword(s):  
Thermal Cycling ◽  
Damage Evolution ◽  
Solder Joints ◽  
Plastic Anisotropy ◽  
Sac305 Solder

Machine learning framework for predicting reliability of solder joints

2019 ◽  
Vol 32 (2) ◽  
pp. 82-92 ◽  
Author(s):  
Sung Yi ◽  
Robert Jones
Keyword(s):  
Machine Learning ◽  
Big Data ◽  
Data Analytics ◽  
Failure Modes ◽  
Solder Joints ◽  
Big Data Analytics ◽  
Content Type ◽  
Learning Framework ◽  
Surface Finishes ◽  
Reliability Of Solder Joints

Purpose This paper aims to present a machine learning framework for using big data analytics to predict the reliability of solder joints. The purpose of this study is to accurately predict the reliability of solder joints by using big data analytics. Design/methodology/approach A machine learning framework for using big data analytics is proposed to predict the reliability of solder joints accurately. Findings A machine learning framework for predicting the life of solder joints accurately has been developed in this study. To validate its accuracy and efficiency, it is applied to predict the long-term reliability of lead-free Sn96.5Ag3.0Cu0.5 (SAC305) for three commonly used surface finishes such OSP, ENIG and IAg. The obtained results show that the predicted failure based on the machine learning method is much more accurate than the Weibull method. In addition, solder ball/bump joint failure modes are identified based on various solder joint failures reported in the literature. Originality/value The ability to predict thermal fatigue life accurately is extremely valuable to the industry because it saves time and cost for product development and optimization.

scholarly journals Diving beetle–like miniaturized plungers with reversible, rapid biofluid capturing for machine learning–based care of skin disease

2021 ◽  
Vol 7 (25) ◽  
pp. eabf5695
Author(s):  
Sangyul Baik ◽  
Jihyun Lee ◽  
Eun Je Jeon ◽  
Bo-yong Park ◽  
Da Wan Kim ◽  
...  
Keyword(s):  
Machine Learning ◽  
Learning Framework ◽  
Biomedical Technologies ◽  
Visual Indication ◽  
Personalized Health ◽  
Personalized Health Care ◽  
Smart Skin ◽  
Diving Beetle

Recent advances in bioinspired nano/microstructures have received attention as promising approaches with which to implement smart skin-interfacial devices for personalized health care. In situ skin diagnosis requires adaptable skin adherence and rapid capture of clinical biofluids. Here, we report a simple, all-in-one device consisting of microplungers and hydrogels that can rapidly capture biofluids and conformally attach to skin for stable, real-time monitoring of health. Inspired by the male diving beetle, the microplungers achieve repeatable, enhanced, and multidirectional adhesion to human skin in dry/wet environments, revealing the role of the cavities in these architectures. The hydrogels within the microplungers instantaneously absorb liquids from the epidermis for enhanced adhesiveness and reversibly change color for visual indication of skin pH levels. To realize advanced biomedical technologies for the diagnosis and treatment of skin, our suction-mediated device is integrated with a machine learning framework for accurate and automated colorimetric analysis of pH levels.

Exchange Spin Coupling from Gaussian Process Regression

2020 ◽  
Author(s):  
Marc Philipp Bahlke ◽  
Natnael Mogos ◽  
Jonny Proppe ◽  
Carmen Herrmann
Keyword(s):  
Machine Learning ◽  
Gaussian Process ◽  
Gaussian Process Regression ◽  
Molecular Magnets ◽  
Molecular Structures ◽  
Spin Coupling ◽  
Structure Property ◽  
Data Set ◽  
Uncertainty Estimates

Heisenberg exchange spin coupling between metal centers is essential for describing and understanding the electronic structure of many molecular catalysts, metalloenzymes, and molecular magnets for potential application in information technology. We explore the machine-learnability of exchange spin coupling, which has not been studied yet. We employ Gaussian process regression since it can potentially deal with small training sets (as likely associated with the rather complex molecular structures required for exploring spin coupling) and since it provides uncertainty estimates (“error bars”) along with predicted values. We compare a range of descriptors and kernels for 257 small dicopper complexes and find that a simple descriptor based on chemical intuition, consisting only of copper-bridge angles and copper-copper distances, clearly outperforms several more sophisticated descriptors when it comes to extrapolating towards larger experimentally relevant complexes. Exchange spin coupling is similarly easy to learn as the polarizability, while learning dipole moments is much harder. The strength of the sophisticated descriptors lies in their ability to linearize structure-property relationships, to the point that a simple linear ridge regression performs just as well as the kernel-based machine-learning model for our small dicopper data set. The superior extrapolation performance of the simple descriptor is unique to exchange spin coupling, reinforcing the crucial role of choosing a suitable descriptor, and highlighting the interesting question of the role of chemical intuition vs. systematic or automated selection of features for machine learning in chemistry and material science.

Failure Analysis and Defect Inspection of Electronic Devices by High-Resolution Cathodoluminescence

2017 ◽  
Author(s):  
C. Monachon ◽  
M.S. Zielinski ◽  
D. Gachet ◽  
S. Sonderegger ◽  
S. Muckenhirn ◽  
...  
Keyword(s):  
Failure Analysis ◽  
Electronic Materials ◽  
Electronic Devices ◽  
Optical Emission ◽  
Large Field ◽  
Nanometer Scale ◽  
Defect Analysis ◽  
Defect Inspection ◽  
Spectrally Resolved ◽  
Non Destructive

Abstract Quantitative cathodoluminescence (CL) microscopy is a new optical spectroscopy technique that measures electron beam-induced optical emission over large field of view with a spatial resolution close to that of a scanning electron microscope (SEM). Correlation of surface morphology (SE contrast) with spectrally resolved and highly material composition sensitive CL emission opens a new pathway in non-destructive failure and defect analysis at the nanometer scale. Here we present application of a modern CL microscope in defect and homogeneity metrology, as well as failure analysis in semiconducting electronic materials

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