Understanding, Modeling and Predicting Hidden Solder Joint Shape Using Active Thermography

Characterizing hidden solder joint shapes is essential for electronics reliability. Active thermography is a methodology to identify hidden defects inside an object by means of surface abnormal thermal response after applying a heat flux. This research focused on understanding, modeling, and predicting hidden solder joint shapes. An experimental model based on active thermography was used to understand how the solder joint shapes affect the surface thermal response (grand average cooling rate or GACR) of electronic multi cover PCB assemblies. Next, a numerical model simulated the active thermography technique, investigated technique limitations and extended technique applicability to characterize hidden solder joint shapes. Finally, a prediction model determined the optimum active thermography conditions to achieve an adequate hidden solder joint shape characterization. The experimental model determined that solder joint shape plays a higher role for visible than for hidden solder joints in the GACR; however, a MANOVA analysis proved that hidden solder joint shapes are significantly different when describe by the GACR. An artificial neural networks classifier proved that the distances between experimental solder joint shapes GACR must be larger than 0.12 to achieve 85% of accuracy classifying. The numerical model achieved minimum agreements of 95.27% and 86.64%, with the experimental temperatures and GACRs at the center of the PCB assembly top cover, respectively. The parametric analysis proved that solder joint shape discriminability is directly proportional to heat flux, but inversely proportional to covers number and heating time. In addition, the parametric analysis determined that active thermography is limited to five covers to discriminate among hidden solder joint shapes. A prediction model was developed based on the parametric numerical data to determine the appropriate amount of energy to discriminate among solder joint shapes for up to five covers. The degree of agreement between the prediction model and the experimental model was determined to be within a 90.6% for one and two covers. The prediction model is limited to only three solder joints, but these research principles can be applied to generate more realistic prediction models for large scale electronic assemblies like ball grid array assemblies having as much as 600 solder joints

A Review and Analysis of Automatic Optical Inspection and Quality Monitoring Methods in Electronics Industry

Electronics industry is one of the fastest evolving, innovative, and most competitive industries. In order to meet the high consumption demands on electronics components, quality standards of the products must be well-maintained. Automatic optical inspection (AOI) is one of the non-destructive techniques used in quality inspection of various products. This technique is considered robust and can replace human inspectors who are subjected to dull and fatigue in performing inspection tasks. A fully automated optical inspection system consists of hardware and software setups. Hardware setup include image sensor and illumination settings and is responsible to acquire the digital image, while the software part implements an inspection algorithm to extract the features of the acquired images and classify them into defected and non-defected based on the user requirements. A sorting mechanism can be used to separate the defective products from the good ones. This article provides a comprehensive review of the various AOI systems used in electronics, micro-electronics, and opto-electronics industries. In this review the defects of the commonly inspected electronic components, such as semiconductor wafers, flat panel displays, printed circuit boards and light emitting diodes, are first explained. Hardware setups used in acquiring images are then discussed in terms of the camera and lighting source selection and configuration. The inspection algorithms used for detecting the defects in the electronic components are discussed in terms of the preprocessing, feature extraction and classification tools used for this purpose. Recent articles that used deep learning algorithms are also reviewed. The article concludes by highlighting the current trends and possible future research directions.Framework of the IQONIC Project; European Union’s Horizon 2020 Research and Innovation Program

Remote Sensing

This dual conception of remote sensing brought us to the idea of preparing two different books; in addition to the first book which displays recent advances in remote sensing applications, this book is devoted to new techniques for data processing, sensors and platforms. We do not intend this book to cover all aspects of remote sensing techniques and platforms, since it would be an impossible task for a single volume. Instead, we have collected a number of high-quality, original and representative contributions in those areas

Advanced Computational Methods for Oncological Image Analysis

[Cancer is the second most common cause of death worldwide and encompasses highly variable clinical and biological scenarios. Some of the current clinical challenges are (i) early diagnosis of the disease and (ii) precision medicine, which allows for treatments targeted to specific clinical cases. The ultimate goal is to optimize the clinical workflow by combining accurate diagnosis with the most suitable therapies. Toward this, large-scale machine learning research can define associations among clinical, imaging, and multi-omics studies, making it possible to provide reliable diagnostic and prognostic biomarkers for precision oncology. Such reliable computer-assisted methods (i.e., artificial intelligence) together with clinicians’ unique knowledge can be used to properly handle typical issues in evaluation/quantification procedures (i.e., operator dependence and time-consuming tasks). These technical advances can significantly improve result repeatability in disease diagnosis and guide toward appropriate cancer care. Indeed, the need to apply machine learning and computational intelligence techniques has steadily increased to effectively perform image processing operations—such as segmentation, co-registration, classification, and dimensionality reduction—and multi-omics data integration.

Development of Ultrasonic Devices for Non-destructive Testing: Ultrasonic Vibro-tactile Sensor and FPGA-Based Research Platform

This thesis is focused on the development of ultrasonic devices for industrial non-destructive testing (NDT). Ultrasound is generated from mechanical vibrations and then propagates through the medium. Ultrasonic devices can make use of the ultrasound in both aspects, vibrations and propagations, to perform inspections of the objects. To this end, two devices were developed in this research, each pertaining to NDT of the objects. The first device is the vibro-tactile sensor which aims to estimate the elastic modules of soft materials with minimally invasive technique. Inspired by load sensitivity studies in the high-power ultrasonic applications, vibration characteristics in resonance were utilized to perform the inspection. Only a minimal force to ensure contact with the object surface needs to be applied for a vibro-tactile sensor to perform inspection of the object; hence, it can be used for in-vivo measurement of the soft materials’ elastic moduli without causing severe surface deformation. The design and analysis of the device were carried out using the electro-mechanical analogy to address the electro-mechanical nature of piezoelectric devices. The designed vibro-tactile sensor resonates at ~40 kHz and can be applied to differentiate the elastic modulus of isotropic soft samples with a range from 10 kPa to 70 kPa. The second device developed is a field-programmable development platform for ultrasonic pulse-echo testing. Ultrasonic testing, utilizing sound wave propagation, is a widely used technique in the industry. The commercially available equipment for industrial NDT is highly dependent on the competence of the inspector and rarely provides the access to raw data. For successful transition from traditional labor-intensive manufacturing to the next generation “smart factory” where intelligent machines replace human labor, inspection equipment with automated in-line data collection and processing capability is highly needed. To this end, a flexible platform which provides the access to raw data for algorithm development and implementation should be established. Therefore, an affordable, versatile, and researcher-friendly development platform based on field-programmable gate array (FPGA) was developed in the research. Both hardware and software development tools and procedures were discussed. In the lab experiment, the developed prototype exhibited its competence in NDT applications and successfully carried out hardware-based auto-detection algorithm for mm-level defects on steel and aluminum specimens. Comparisons with commercial systems were provided to guide future development

Velocity-independent thermal conductivity and volumetric heat capacity measurement of binary gas mixtures

In this paper, we present a single hot wire suspended over a V-groove cavity that is used to measure the thermal conductivity ($k$) and volumetric heat capacity ($\rho c_p$) for both pure gases and binary gas mixtures through DC and AC excitation, respectively. The working principle and measurement results are discussed

Microfabrication Technology for Isolated Silicon Sidewall Electrodes and Heaters

This paper presents a novel microfabricationtechnology for highly doped silicon sidewall electrodesparallel to – and isolated from – the microchannel. Thesidewall electrodes can be utilised for both electricaland thermal actuation of sensor systems. Thetechnology is scalable to a wide range of channelgeometries, simplifies the release etch, and allows forfurther integration with other Surface ChannelTechnology-based systems. Furthermore, thefabrication technology is demonstrated through thefabrication of a relative permittivity sensor. The sensormeasures relative permittivity values ranging from 1 to80, within 3% accuracy of full scale, including waterand water-containing mixtures

Bio-Inspired Robotics

Modern robotic technologies have enabled robots to operate in a variety of unstructured and dynamically-changing environments, in addition to traditional structured environments. Robots have, thus, become an important element in our everyday lives. One key approach to develop such intelligent and autonomous robots is to draw inspiration from biological systems. Biological structure, mechanisms, and underlying principles have the potential to provide new ideas to support the improvement of conventional robotic designs and control. Such biological principles usually originate from animal or even plant models, for robots, which can sense, think, walk, swim, crawl, jump or even fly. Thus, it is believed that these bio-inspired methods are becoming increasingly important in the face of complex applications. Bio-inspired robotics is leading to the study of innovative structures and computing with sensory–motor coordination and learning to achieve intelligence, flexibility, stability, and adaptation for emergent robotic applications, such as manipulation, learning, and control. This Special Issue invites original papers of innovative ideas and concepts, new discoveries and improvements, and novel applications and business models relevant to the selected topics of ``Bio-Inspired Robotics''. Bio-Inspired Robotics is a broad topic and an ongoing expanding field. This Special Issue collates 30 papers that address some of the important challenges and opportunities in this broad and expanding field

Using MapReduce Streaming for Distributed Life Simulation on the Cloud

Distributed software simulations are indispensable in the study of large-scale life models but often require the use of technically complex lower-level distributed computing frameworks, such as MPI. We propose to overcome the complexity challenge by applying the emerging MapReduce (MR) model to distributed life simulations and by running such simulations on the cloud. Technically, we design optimized MR streaming algorithms for discrete and continuous versions of Conway’s life according to a general MR streaming pattern. We chose life because it is simple enough as a testbed for MR’s applicability to a-life simulations and general enough to make our results applicable to various lattice-based a-life models. We implement and empirically evaluate our algorithms’ performance on Amazon’s Elastic MR cloud. Our experiments demonstrate that a single MR optimization technique called strip partitioning can reduce the execution time of continuous life simulations by 64%. To the best of our knowledge, we are the first to propose and evaluate MR streaming algorithms for lattice-based simulations. Our algorithms can serve as prototypes in the development of novel MR simulation algorithms for large-scale lattice-based a-life models.https://digitalcommons.chapman.edu/scs_books/1014/thumbnail.jp