A Multimedia Information System for IC Failure Analysis

Purpose Emergency shutdown (ESD) systems play a critical role in ensuring safety and availability of oil and gas production. The systems are operated in on-demand mode, and the detection and prediction of their failures is deemed challenging. The purpose of this paper is to develop a logical data-driven approach to enhance the understanding and detectability of ESD system failures. Design/methodology/approach The study was conducted in close collaboration with the Norwegian oil and gas industry. The study and analyses were supported by industrial data, failure data generated in a test facility in Norway and domain experts. Findings The paper demonstrated that there is a considerable potential to improve the decision process and to reduce the workload related to ESD systems by means of a logical data-driven approach. The results showed that the failure analysis process can be executed with more clarity and efficiency. Common cause failures could also be identified based on the suggested approach. The study further underlined the requirements regarding relevant data, new competence and technical supports in order to improve the current practice. Originality/value The paper leveraged the value of real-time data in identifying failures through mapping of the interrelationships between data, failure mechanisms and decisions. The failure analysis process was re-designed, and the understanding and decision making related to the system was improved as a result. The process developed for ESDs can further be adapted as a common practice for other low-demand systems.

Failure analysis (FA) is a basic and effective method to obtain the physical and failure information of electronic components and circuits. Most of these information can provide many useful evidences for prognostic and system health management (PHM) of electronic products. To obtain these information, many failure analysis techniques are used. This paper focuses on analyzing the relationship between FA techniques and PHM system. To test and verify the availability of the relationship between FA techniques and PHM system, a failure analysis case of an Intelligent Power Module (IPM) is used to study the relationship. This IPM contains six insulated gate bipolar transistors (IGBTs) and there are obvious burnout indications on most of them after failure. With the process of failure analysis, the key information, such as component's structure, sensitive parameters are identified. These results persuasively prove that FA techniques can provide much information to PHM system, including PoF and data-driven methods.

Hard disk failure is a serious problem in term of product quality and credibility to customers. All hard disk drive companies need to be aware and address how to get rid of failure and prevent the repeat of the problem in their products. The quality of failure analysis process depends on the person who has most experience. It would not be so efficient if the company has no experienced person to perform the analysis. A knowledge management system can store the knowledge of experienced engineers. It can help new engineers to learn the craft. It would reduce a knowledge gap issues and bring up efficiency for failure solving process. This paper presents a design and implementation of knowledge management system for failure analysis in hard disk with case-based reasoning. The existing cases are stored and a new case can be compared to the existing one in order to retrieve the relevant existing knowledge to help the analysis. Once the new case is solved, it can be stored to aid the future cases. A prototype of the system has been implemented and the assessment of user satisfaction shows that it can improve the failure analysis process effectively.

This study aims to design, build, and implement a decision support system for selecting Elementary School recipients of face-to-face learning facilities and infrastructure in the New Normal Era with the Weighted Product (WP) method in Jiwan District, Madiun Regency, East Java. The data used is secondary data in the form of public data obtained from the site http://school.data.kemdikbud.go.id/. and the number of elementary school data obtained with the boundaries of Jiwan District, Madiun Regency, amounted to 23 schools. At the stage of designing the system for this research, it is divided into three parts, namely the UML System, User Interface System and Database with ERD. The research findings show that the decision support system for selecting primary schools that receive face-to-face learning facilities and infrastructure in the New Normal Era with the WP method has several user interfaces, namely (1) User interface Login is used for security features of the Elementary School election system. (2) The Home Page user interface contains facilities for criteria data management, criteria weight data management, alternative data management, SPK value data management, user data management, and logout. (3) Criteria Data User Interface can perform criteria data management by adding and saving, changing, and deleting criteria data. (4) The Criteria Weighting Data User Interface can fill in the value of the weight of each criterion. (5) Alternative Data User Interface can add and save, change, and delete alternative data. (6) The Value Data User Interface can view and save by adding values to each alternative according to the criteria. (7) The DSS Management User Interface can make decisions by calculating the weight correction, calculating the S vector value, and calculating the V vector value. (8) User Interface Viewing Results can see the results of decision making after calculating the SPK. (9) User Management Interface Users can add, edit, and delete data from users containing usernames and passwords. Then by using the 57 item test scenarios of the blackbox method, the decision support system that was built had 100% success according to the expectations of the test scenario.

Owing to the configuration of cavity up and stacked die packaging and the requirements of backside analysis, both packaging types require similar sample preparation steps. This article describes the failure analysis (FA) process to be applied with cavity up and stack die packages. The FA process flow includes testing to determine the nature of the failure, failure correlation to chip and/or internal circuitry, die preparation for repackaging, die repackaging in a cavity down configuration, automated test equipment (ATE) testing to verify the integrity of the pre-packaging failure mode, backside thinning, global fault isolation, backside reconstruction, and defect identification by front side deprocessing. ATE FA can often be performed using special analysis modes and the modification of the test software to put tester in a halt or a loop during fault isolation. When this is completed, global FA techniques can be used. The article also presents a case study on the successful repackaging efforts of cavity up packages.

Various types of 3D simulation applications benefit from realistic forest models. They range from flight simulators for entertainment to harvester simulators for training and tree growth simulations for research and planning. This paper's 4D forest simulation and information system integrates the necessary methods for data extraction, modelling and management. Using modern methods of semantic world modelling, tree data can efficiently be extracted from remote sensing data. The derived forest models contain position, height, crown volume, type and diameter of each tree. This data is modelled using GML-based data models to assure compatibility and exchangeability. ForestGML is the name of a new schema family developed to provide a common basis for forestry data. A flexible approach for database synchronization is used to manage the data and provide caching, persistence, a central communication hub for change distribution, and a versioning mechanism. Combining various simulation techniques and data versioning, the 4D forest simulation and information system can provide applications with “both directions” of the fourth dimension. This paper outlines the current state, new developments, and integration of tree extraction, data modelling, and data management. It also shows several applications realized with the system.

In an era where the complexity and cost of Failure Analysis tools and techniques is rapidly expanding, it is easy sometimes to lose sight of the basic tool and technique required for successful root-cause analysis. That technique is intellectual curiosity and the tool is the human brain. This paper will describe a simple methodology to insure that this tool and technique are properly engaged either concomitant with or in the absence of state-of-the-art instrumentation and computation. Two simple case studies will be used to illustrate where the Failure Analysis process can easily go awry without proper attention to detail, and, conversely, from too much attention to detail.

The cost of in-service clearance control coating failure analysis can be high to the aerospace OEM’s (Original Equipment Manufacturers). The failure analysis process is labor-intensive and time-consuming. It is for these reasons that OEM’s will be requiring vendors of materials and equipment to have: (1) greater in-process controls on spray process equipment, (2) greater statistical process control (SPC) documentation of key material manufacturing process variables, (3) increased documentation of cause and effect of powder manufacturing and spray process variables and better understanding of the influence of these variables on overall performance, and (4) more detailed coating/powder specifications. This paper will discuss the future needs of aerospace OEM’s in these four areas and specifically discuss how vendors are addressing these needs.

Improving object-oriented user interfaces with constraints

The energy data scope is very broad including oil and gas, coal, minerals, new energy, renewable and conversion energy, electricity, and others. The different volume, variety, veracity, and velocity of data have challenge to address with the energy data model. The works focus on the development of data model for big data storage implementation schemes in the energy data. The data model has produces by analyzed the national energy big data architecture, develop the data mapping and correlation, big data master data management with energy industry standardization, big data management portal for upstream energy and downstream energy. The research has goal to establishing a foundation for building technology and big data management in the energy sector which includes petroleum, coal, geothermal and renewable energy, and in the future can be the basis for predictive analysis and national energy production.

The performance of remote sensing image retrieval (RSIR) systems depends on the capability of the extracted features in characterizing the semantic content of images. Existing RSIR systems describe images by visual descriptors that model the primitives (such as different land-cover classes) present in the images. However, the visual descriptors may not be sufficient to describe the high-level complex content of RS images (e.g., attributes and relationships among different land-cover classes). To address this issue, in this article, we present an RSIR system that aims at generating and exploiting textual descriptions to accurately describe the relationships between the objects and their attributes present in RS images with captions (i.e., sentences). To this end, the proposed retrieval system consists of three main steps. The first step aims to encode the image visual features and then translate the encoded features into a textual description that summarizes the content of the image with captions. This is achieved based on the combination of a convolutional neural network with a recurrent neural network. The second step aims to convert the generated textual descriptions into semantically meaningful feature vectors. This is achieved by using the recent word embedding techniques. Finally, the last step estimates the similarity between the vectors of the textual descriptions of the query image and those of the archive images, and then retrieve the most similar images to the query image. Experimental results obtained on two different datasets show that the description of the image content with captions in the framework of RSIR leads to an accurate retrieval performance.

The explosive increase of image data on Internet has made it an important, yet very challenging task to index and automatically annotate image data. To achieve that end, sophisticated algorithms and models have been proposed to study the correlation between image content and corresponding text description. Despite the success of previous works, however, researchers are still facing two major difficulties that may undermine their effort of providing reliable and accurate annotations for images. The first difficulty is lacking of comprehensive benchmark image dataset with high quality text descriptions. The second difficulty is lacking of effective way to represent the image content and make it associate with the text descriptions. In our paper, we aim to deal with both problems. To deal with the first problem, we utilize Wikipedia as external knowledge source and enrich the ontology structure of ImageNet database with comprehensive and highly-reliable text descriptions from Wikipedia articles. To address the second problem, we develop a Probabilistic Topic-Connection (PTC) model to represent the connection between latent semantic topic in text description and latent patterns from image feature space. We compare the performance of our model with the currently popular Correspondence LDA (Corr-LDA) model under the same automatic image annotation scenario using cross-validation. Experimental results demonstrate that our model is able to well represent the connection between latent semantic topics and latent patterns in image feature space, thus facilitates knowledge organization and understanding of both image and text descriptions.

Nowadays, touchable displays are widely used in most embedded systems, including mobile phones and other hand-held devices. Although interactive user interfaces are intuitively required for these systems, most of interactive graphical user interface implementations usually need lots of system resources. For low-tier embedded systems, we have presented that the text-based user interfaces are more suitable, as shown in our previous works. In this paper, we present an object-oriented approach to provide a full-scale text-based user interface scheme, especially on the low-tier embedded systems. Our application user interface implementation is very simple and intuitive, with few amount of computing resource. It naturally supports multiple overlapping windows, pop-up menus, pull-down menus, dialog boxes, and other user interface components, with graceful object-oriented application programming interfaces. We also provide large size text output for emphasis purposes. All the systems are now available as a software library, and can be used for wide range of applications.

In today’s World sensor networks offer various opportunities for data management applications because of their low cost, reliability, scalability, high-speed data processing, and other versatile advantageous purposes. It is a great challenge to organize data effectively and to retrieve the appropriate data from the large volume of various data sets in ad-hoc network databases, mobile databases, etc. The sensor network is necessary for routing of data, performance analysis of data management activities, and data incorporation for the right application. Data management involves intranet and extranet query handling, data access mechanism, modeling of data, different data movement algorithm, data warehousing, and data mining of network database. Additionally, connectivity, design, and lifetime are important issues for sensor networks to perform all data management activities smoothly. In this paper, we are trying to give a cognitive research tendency of Sensor network data management in the last two decades considering all the challenges and issues of both sensor network database and data management functions using Scopus and Web of Science database. To analyze data, different assessments are done considering various parameters like the author, time, publication and citation number, place, source, document separately for Web of Science and Scopus database in global perspective. It is noticed that there is a significant growth of research in data management for sensor networks because of the popularity of this topic.

In this study, a hierarchical multi-label classification method called HFFNet-2d is proposed for the automatic classification of scanning electron microscope (SEM) images of metal failure. The method combines the advantages of convolutional neural networks (CNN) and Vision Transformers (ViT) to effectively realize hierarchical feature extraction and classification of SEM images of fracture morphologies, enabling accurate identification of metal failure mechanisms at different scales. The dataset of high-quality SEM images in this work is sourced from reputable materials science publications for its comprehensive coverage of various failure modes and its suitability for training and validating the hierarchical multi-label classification model. The HFFNet-2d model can achieve a high accuracy of 97.71% in the first-level classification and 92.62% in the second-level sub-category identification. This performance surpasses the human experts on the same task. To ensure that the model predictions are sufficiently reliable, a multi-level gradcam algorithm is also introduced for checking the regions of interest of the Hierarchical model at two levels and the comparisons are made with human experts. It is anticipated that the optimization and extension of HFFNet-2d are conducive in diverse material systems and application scenarios to accelerate the intelligent process of material development and failure analysis, ultimately supporting the design of reliable and high-performance engineering materials.