Hybrid Diagnosis Model To Determine Fault Isolation For Scan Chain Failure Analysis On 22nm Fabrication Process

With the rapid growth of Very Large Scale Integration (VLSI) in complex designs, there is high demand for Design for Testability (DFT). Vast study has proven that Scan based testing is achieving good test coverage with lower cost and smaller die area and is widely used in the industry. Scan chain fault diagnosis plays an important role as with the implementation of Scan based testing, it is reported that 10%-30% of defects in a Scan based design occurs within the Scan chain itself. Currently, there are three main types of stand-alone diagnosis models available, which are: software-based diagnosis, tester-based diagnosis and hardware-based diagnosis, where each has its disadvantages and limitations. In this project, the author proposed a hybrid Scan chain failure analysis technique that uses the proposed software-based diagnosis to obtain a list of possible failing suspect Scan cells, followed by the proposed tester-based diagnosis to further isolate the fault to a single failing device suspect. This proposed hybrid diagnosis algorithm ensures that Scan chain faults such as stuck-at and transition faults can be root-caused with lesser time and low complexity for both solid and marginal failures. Four case studies were successfully carried out to evaluate the proposed hybrid diagnosis algorithm on a 22nm fabrication process technology Device under Test (DUT) System-on-Chip (SOC) product, where the fault isolation was able to isolate a single failing device suspect for all four case studies, indicating a 100% fault isolation success rate

Sensors Fault Diagnosis Trends and Applications

Fault diagnosis has always been a concern for industry. In general, diagnosis in complex systems requires the acquisition of information from sensors and the processing and extracting of required features for the classification or identification of faults. Therefore, fault diagnosis of sensors is clearly important as faulty information from a sensor may lead to misleading conclusions about the whole system. As engineering systems grow in size and complexity, it becomes more and more important to diagnose faulty behavior before it can lead to total failure. In the light of above issues, this book is dedicated to trends and applications in modern-sensor fault diagnosis

VirtualScan: a new compressed scan technology for test cost reduction

This work describes the VirtualScan technology for scan test cost reduction. Scan chains in a VirtualScan circuit are split into shorter ones and the gap between external scan ports and internal scan chains are bridged with a broadcaster and a compactor. Test patterns for a VirtualScan circuit are generated directly by one-pass VirtualScan ATPG, in which multi-capture clocking and maximum test compaction are supported. In addition, VirtualScan ATPG avoids unknown-value and aliasing effects algorithmically without adding any additional circuitry. The VirtualScan technology has achieved successful tape-outs of industrial chips and has been proven to be an efficient and easy-to-implement solution for scan test cost reduction.2004 International Conference on Test, 26-28 October 2004, Charlotte, NC, USA, US

When Things Matter: A Data-Centric View of the Internet of Things

With the recent advances in radio-frequency identification (RFID), low-cost wireless sensor devices, and Web technologies, the Internet of Things (IoT) approach has gained momentum in connecting everyday objects to the Internet and facilitating machine-to-human and machine-to-machine communication with the physical world. While IoT offers the capability to connect and integrate both digital and physical entities, enabling a whole new class of applications and services, several significant challenges need to be addressed before these applications and services can be fully realized. A fundamental challenge centers around managing IoT data, typically produced in dynamic and volatile environments, which is not only extremely large in scale and volume, but also noisy, and continuous. This article surveys the main techniques and state-of-the-art research efforts in IoT from data-centric perspectives, including data stream processing, data storage models, complex event processing, and searching in IoT. Open research issues for IoT data management are also discussed

Continuous maintenance and the future – Foundations and technological challenges

High value and long life products require continuous maintenance throughout their life cycle to achieve required performance with optimum through-life cost. This paper presents foundations and technologies required to offer the maintenance service. Component and system level degradation science, assessment and modelling along with life cycle ‘big data’ analytics are the two most important knowledge and skill base required for the continuous maintenance. Advanced computing and visualisation technologies will improve efficiency of the maintenance and reduce through-life cost of the product. Future of continuous maintenance within the Industry 4.0 context also identifies the role of IoT, standards and cyber security

Self-aware COVID-19 AI Approach (SIntL-CoV19) by Integrating Infected Scans with Internal Behavioral Analysis

In the Artificial intelligence (AI) field, intelligent social awareness is a quantifiable analysis that interacts with humans socially with other infected or non-infected COVID-19 (CoV19) humans. However, less importance is given in this direction. Clinically, there is a need for a social-awareness automated model design to quantify the self-awareness of infected patients and develop a social learning system. In this research paper, a new model of self-aware internal learning coronavirus 19 (SIntL-CoV19) model technique is presented with quantification measures to represent model requirements as an individual self-aware automated detection. Through this model, a human can communicate with the social environment and other humans with an accurate CoV19 infection diagnosis. SIntL-CoV19 model framework for implementation of self-aware architecture with this model is proposed making the diagnosis process compared with the existing architecture. The proposed model achieves improved accuracy Feature Classifier, which outperforms other learning algorithms for CoV19 and normal scans. The data from the investigation show that the proposed SIntL-CoV19 model method might be more effective than other methods

SoC Test: Trends and Recent Standards

The well-known approaching test cost crisis, where semiconductor test costs begin to approach or exceed manufacturing costs has led test engineers to apply new solutions to the problem of testing System-On-Chip (SoC) designs containing multiple IP (Intellectual Property) cores. While it is not yet possible to apply generic test architectures to an IP core within a SoC, the emergence of a number of similar approaches, and the release of new industry standards, such as IEEE 1500 and IEEE 1450.6, may begin to change this situation. This paper looks at these standards and at some techniques currently used by SoC test engineers. An extensive reference list is included, reflecting the purpose of this publication as a review paper

AI Solutions for MDS: Artificial Intelligence Techniques for Misuse Detection and Localisation in Telecommunication Environments

This report considers the application of Articial Intelligence (AI) techniques to the problem of misuse detection and misuse localisation within telecommunications environments. A broad survey of techniques is provided, that covers inter alia rule based systems, model-based systems, case based reasoning, pattern matching, clustering and feature extraction, articial neural networks, genetic algorithms, arti cial immune systems, agent based systems, data mining and a variety of hybrid approaches. The report then considers the central issue of event correlation, that is at the heart of many misuse detection and localisation systems. The notion of being able to infer misuse by the correlation of individual temporally distributed events within a multiple data stream environment is explored, and a range of techniques, covering model based approaches, `programmed' AI and machine learning paradigms. It is found that, in general, correlation is best achieved via rule based approaches, but that these suffer from a number of drawbacks, such as the difculty of developing and maintaining an appropriate knowledge base, and the lack of ability to generalise from known misuses to new unseen misuses. Two distinct approaches are evident. One attempts to encode knowledge of known misuses, typically within rules, and use this to screen events. This approach cannot generally detect misuses for which it has not been programmed, i.e. it is prone to issuing false negatives. The other attempts to `learn' the features of event patterns that constitute normal behaviour, and, by observing patterns that do not match expected behaviour, detect when a misuse has occurred. This approach is prone to issuing false positives, i.e. inferring misuse from innocent patterns of behaviour that the system was not trained to recognise. Contemporary approaches are seen to favour hybridisation, often combining detection or localisation mechanisms for both abnormal and normal behaviour, the former to capture known cases of misuse, the latter to capture unknown cases. In some systems, these mechanisms even work together to update each other to increase detection rates and lower false positive rates. It is concluded that hybridisation offers the most promising future direction, but that a rule or state based component is likely to remain, being the most natural approach to the correlation of complex events. The challenge, then, is to mitigate the weaknesses of canonical programmed systems such that learning, generalisation and adaptation are more readily facilitated

Objective analysis of neck muscle boundaries for cervical dystonia using ultrasound imaging and deep learning

Objective: To provide objective visualization and pattern analysis of neck muscle boundaries to inform and monitor treatment of cervical dystonia. Methods: We recorded transverse cervical ultrasound (US) images and whole-body motion analysis of sixty-one standing participants (35 cervical dystonia, 26 age matched controls). We manually annotated 3,272 US images sampling posture and the functional range of pitch, yaw, and roll head movements. Using previously validated methods, we used 60-fold cross validation to train, validate and test a deep neural network (U-net) to classify pixels to 13 categories (five paired neck muscles, skin, ligamentum nuchae, vertebra). For all participants for their normal standing posture, we segmented US images and classified condition (Dystonia/Control), sex and age (higher/lower) from segment boundaries. We performed an explanatory, visualization analysis of dystonia muscle-boundaries. Results: For all segments, agreement with manual labels was Dice Coefficient (64±21%) and Hausdorff Distance (5.7±4 mm). For deep muscle layers, boundaries predicted central injection sites with average precision 94±3%. Using leave-one-out cross-validation, a support-vector-machine classified condition, sex, and age from predicted muscle boundaries at accuracy 70.5%, 67.2%, 52.4% respectively, exceeding classification by manual labels. From muscle boundaries, Dystonia clustered optimally into three sub-groups. These sub-groups are visualized and explained by three eigen-patterns which correlate significantly with truncal and head posture. Conclusion: Using US, neck muscle shape alone discriminates dystonia from healthy controls. Significance: Using deep learning, US imaging allows online, automated visualization, and diagnostic analysis of cervical dystonia and segmentation of individual muscles for targeted injection. The dataset is available (DOI: 10.23634/MMUDR.00624643)