Biblio

Software based scan diagnosis is the de facto method for debugging logic scan failures. Physical analysis success rate is high on dies diagnosed with maximum score, one symptom, one suspect and shorter net. This poses a limitation on maximum utilization of scan diagnosis data for PFA. There have been several attempts to combine dynamic fault isolation techniques with scan diagnosis results to enhance the utilization and success rate. However, it is not a feasible approach for foundry due to limited product design and test knowledge and hardware requirements such as probe card and tester. Suitable for a foundry, an enhanced diagnosis-driven analysis scheme was proposed in [1] that classifies the failures as frontend-of-line (FEOL) and backend-of-line (BEOL) improving the die selection process for PFA. In this paper, static NIR PEM and defect prediction approach are applied on dies that are already classified as FEOL and BEOL failures yet considered unsuitable for PFA due to low score, multiple symptoms, and suspects. Successful case studies are highlighted to showcase the effectiveness of using static NIR PEM as the next level screening process to further maximize the scan diagnosis data utilization.

An increasing number of industries around the world are adopting advance manufacturing technologies for product design, among which additive manufacturing (AM) is gaining attention among aerospace, defense, automotive and health care domains. Products with complicated designs demanding lesser weight, improved performance and conformance are manufactured by companies using AM technologies. Some noticeable examples of ducting, airflow system and vent products in the aerospace domain can be seen being made out of AM techniques. One of the benefits being mentioned is the significant reduction in the number of components going into a finished product, thereby impacting the supply chain as well. However, one of the challenges in AM process is to reduce the process variation which affects the reliability of the product. To realize the true benefits of additively manufactured products, it is imperative to ensure that the reliability of AM products is similar or better than traditionally manufactured products. Current state of art for assessing reliability of traditionally manufactured products is mature. However, the reliability assessment framework for products manufactured by advanced technologies are being studied upon. In this direction, this paper highlights a structured reliability assessment framework for additive manufactured products, which will help in identifying, analyzing and mitigating reliability risks as part of product development life cycle.

GDSII layouts of IP-confidential products are heavily controlled and access is only granted to certain privileged personnel. Failure analysts are generally excluded. Without guidance from GDSII, failure analysis, specifically physical inspection based on fault isolation findings cannot proceed. To overcome this challenge, we develop an automated approach that enables image snapshots relevant to failure analysts to be furnished without compromising the confidentiality of the GDSII content in this paper. Modules built are executed to trace the suspected nets and extract them into multiple images of different pre-defined frame specifications to facilitate failure analysis.

This paper proposes a mathematical model for assessing the trustworthiness of the system condition prognosis. The set of mutually exclusive events at the time of predictive checking are analyzed. Correct and incorrect decisions correspond to events such as true-positive, false-positive, true-negative, and false-negative. General expressions for computing the probabilities of possible decisions when predicting the system condition at discrete times are proposed. The paper introduces the effectiveness indicators of predictive maintenance in the form of average operating costs, total error probability, and a posteriori probability of failure-free operation in the upcoming interval. We illustrate the developed approach by calculating the probabilities of correct and incorrect decisions for a specific stochastic deterioration process.

Testing which is an indispensable part of software engineering is itself an art and science which emerged as a discipline over a period. On testing, if defects are found, testers diminish the risk by providing the awareness of defects and solutions to deal with them before release. If testing does not find any defects, testing assure that under certain conditions the system functions correctly. To guarantee that enough testing has been done, major risk areas need to be tested. We have to identify the risks, analyse and control them. We need to categorize the risk items to decide the extent of testing to be covered. Also, Implementation of structured metrics is lagging in software testing. Efficient metrics are necessary to evaluate, manage the testing process and make testing a part of engineering discipline. This paper proposes the usage of risk based testing using FMEA technique and provides an ideal set of metrics which provides a way to ensure effective testing process.

To ensure power system operational security, it not only requires security incident detection, but also automated intrusion response and recovery mechanisms to tolerate failures and maintain the system's functionalities. In this paper, we present a design procedure for remedial action schemes (RAS) that improves the power systems resiliency against accidental failures or malicious endeavors such as cyber attacks. A resilience-oriented optimal power flow is proposed, which optimizes the system security instead of the generation cost. To improve its speed for online application, a fast greedy algorithm is presented to narrow the search space. The proposed techniques are computationally efficient and are suitable for online RAS applications in large-scale power systems. To demonstrate the effectiveness of the proposed methods, there are two case studies with IEEE 24-bus and IEEE 118-bus systems.

The internet of things (IoT) is the popular wireless network for data collection applications. The IoT networks are deployed in dense or sparse architectures, out of which the dense networks are vastly popular as these are capable of gathering the huge volumes of data. The collected data is analyzed using the historical or continuous analytical systems, which uses the back testing or time-series analytics to observe the desired patterns from the target data. The lost or bad interval data always carries the high probability to misguide the analysis reports. The data is lost due to a variety of reasons, out of which the most popular ones are associated with the node failures and connectivity holes, which occurs due to physical damage, software malfunctioning, blackhole/wormhole attacks, route poisoning, etc. In this paper, the work is carried on the new routing scheme for the IoTs to avoid the connectivity holes, which analyzes the activity of wireless nodes and takes the appropriate actions when required.

Although there has been notable progress in modeling cascading failures in power grids, few works included using machine learning algorithms. In this paper, cascading failures that lead to massive blackouts in power grids are predicted and classified into no, small, and large cascades using machine learning algorithms. Cascading-failure data is generated using a cascading failure simulator framework developed earlier. The data set includes the power grid operating parameters such as loading level, level of load shedding, the capacity of the failed lines, and the topological parameters such as edge betweenness centrality and the average shortest distance for numerous combinations of two transmission line failures as features. Then several machine learning algorithms are used to classify cascading failures. Further, linear regression is used to predict the number of failed transmission lines and the amount of load shedding during a cascade based on initial feature values. This data-driven technique can be used to generate cascading failure data set for any real-world power grids and hence, power-grid engineers can use this approach for cascade data generation and hence predicting vulnerabilities and enhancing robustness of the grid.

Major blackouts are due to cascading failures in power systems. These failures usually occur at vulnerable links of the network. To identify these, indicators have already been defined using complex network theory. However, most of these indicators only depend on the topology of the grid; they fail to detect the weak links. We introduce a new metric to identify the vulnerable lines, based on the load-flow equations and the grid geometry. Contrary to the topological indicators, ours is built from the electrical equations and considers the location and magnitude of the loads and of the power generators. We apply this new metric to the IEEE 118-bus system and compare its prediction of weak links to the ones given by an industrial software. The agreement is very well and shows that using our indicator a simple examination of the network and its generator and load distribution suffices to find the weak lines.

The cyber-physical security of smart grid is of great importance since it directly concerns the normal operating of a system. Recently, researchers found that organized sequential attacks can incur large-scale cascading failure to the smart grid. In this paper, we focus on the line-switching sequential attack, where the attacker aims to trip transmission lines in a designed order to cause significant system failures. Our objective is to identify the critical line-switching attack sequence, which can be instructional for the protection of smart grid. For this purpose, we develop an evolutionary computation based vulnerability analysis framework, which employs particle swarm optimization to search the critical attack sequence. Simulation studies on two benchmark systems, i.e., IEEE 24 bus reliability test system and Washington 30 bus dynamic test system, are implemented to evaluate the performance of our proposed method. Simulation results show that our method can yield a better performance comparing with the reinforcement learning based approach proposed in other prior work.

The smart grid consists of two parts, one is the physical power grid, the other is the information network. In order to study the cascading failure, the vulnerability analysis of the smart grid is done under a kind of community attack style in this paper. Two types of information networks are considered, i.e. topology consistency and scale-free cyber networks, respectively. The concept of control center is presented and the controllable power nodes and observable power lines are defined. Minimum load reduction model(MLRM) is given and described as a linear programming problem. A index is introduced to assess the vulnerability. New England 39 nodes system is applied to simulate the cascading failure process to demonstrate the effectiveness of the proposed MLRM where community the attack methods include attack the power lines among and in power communities.

In order to protect power grid network, the security assessment techniques which include both cyber side and the physical side should be considered. In this paper, we present a method for evaluating the dynamic vulnerability of cyber-physical power system (CPPS) considering the power grid infrastructures failure. First, according to the functional characteristics of different components, the impact of a single component function failure on CPPS operation is analyzed and quantified, such as information components, communication components and power components; then, the dynamic vulnerability of multiple components synchronization function failure is calculated, and the full probability evaluation formula of CPPS operational dynamic vulnerability is built; Thirdly, from an attacker's perspective to identify the most hazardous component combinations for CPPS multi-node collaborative attack; Finally, a local CPPS model is established based on the IEEE-9 bus system to quantify its operational dynamic vulnerability, and the effectiveness of proposed method is verified.

Supervisory control and data acquisition (SCADA) systems are becoming increasingly susceptible to cyber-physical attacks on both physical and cyber layers of critical information infrastructure. Failure Mode and Effects Analysis (FMEA) have been widely used as a structured method to prioritize all possible vulnerable areas (failure modes) for design review of security of information systems. However, traditional RPN based FMEA has some inherent problems. Besides, there is a lacking of application of FMEA for security in SCADAs under vague and uncertain environment. Thus, the main purpose of this study was to propose a new evaluation model, which not only intends to recover above mentioned problems, but also intends to evaluate, prioritize and correct security risk of SCADA system's threat modes. A numerical case study was also conducted to demonstrate that the proposed new evaluation model is not only capable of addressing FMEA's inherent problems but also is best suited for a semi-quantitative high level analysis of a secure SCADA's failure modes in the early design phases.

Despite the benefits offered by smart grids, energy producers, distributors and consumers are increasingly concerned about possible security and privacy threats. These threats typically manifest themselves at runtime as new usage scenarios arise and vulnerabilities are discovered. Adaptive security and privacy promise to address these threats by increasing awareness and automating prevention, detection and recovery from security and privacy requirements' failures at runtime by re-configuring system controls and perhaps even changing requirements. This paper discusses the need for adaptive security and privacy in smart grids by presenting some motivating scenarios. We then outline some research issues that arise in engineering adaptive security. We particularly scrutinize published reports by NIST on smart grid security and privacy as the basis for our discussions.

The paper describes modification of the ATA (Attack Tree Analysis) technique for assessment of instrumentation and control systems (ICS) dependability (reliability, availability and cyber security) called AvTA (Availability Tree Analysis). The techniques FMEA, FMECA and IMECA applied to carry out preliminary semi-formal and criticality oriented analysis before AvTA based assessment are described. AvTA models combine reliability and cyber security subtrees considering probabilities of ICS recovery in case of hardware (physical) and software (design) failures and attacks on components casing failures. Successful recovery events (SREs) avoid corresponding failures in tree using OR gates if probabilities of SRE for assumed time are more than required. Case for dependability AvTA based assessment (model, availability function and technology of decision-making for choice of component and system parameters) for smart building ICS (Building Automation Systems, BAS) is discussed.

Integrated cyber-physical systems (CPSs), such as the smart grid, are becoming the underpinning technology for major industries. A major concern regarding such systems are the seemingly unexpected large scale failures, which are often attributed to a small initial shock getting escalated due to intricate dependencies within and across the individual counterparts of the system. In this paper, we develop a novel interdependent system model to capture this phenomenon, also known as cascading failures. Our framework consists of two networks that have inherently different characteristics governing their intra-dependency: i) a cyber-network where a node is deemed to be functional as long as it belongs to the largest connected (i.e., giant) component; and ii) a physical network where nodes are given an initial flow and a capacity, and failure of a node results with redistribution of its flow to the remaining nodes, upon which further failures might take place due to overloading. Furthermore, it is assumed that these two networks are inter-dependent. For simplicity, we consider a one-to-one interdependency model where every node in the cyber-network is dependent upon and supports a single node in the physical network, and vice versa. We provide a thorough analysis of the dynamics of cascading failures in this interdependent system initiated with a random attack. The system robustness is quantified as the surviving fraction of nodes at the end of cascading failures, and is derived in terms of all network parameters involved. Analytic results are supported through an extensive numerical study. Among other things, these results demonstrate the ability of our model to capture the unexpected nature of large-scale failures, and provide insights on improving system robustness.

The interconnection of networks between several techno-socio-economic sectors such as energy, transport, and communication, questions the manageability and resilience of the digital society. System interdependencies alter the fundamental dynamics that govern isolated systems, which can unexpectedly trigger catastrophic instabilities such as cascading failures. This paper envisions a general-purpose, yet simple prototyping of self-management software systems that can turn system interdependencies from a cause of instability to an opportunity for higher resilience. Such prototyping proves to be challenging given the highly interdisciplinary scope of interdependent networks. Different system dynamics and organizational constraints such as the distributed nature of interdependent networks or the autonomy and authority of system operators over their controlled infrastructure perplex the design for a general prototyping approach, which earlier work has not yet addressed. This paper contributes such a modular design solution implemented as an open source software extension of SFINA, the Simulation Framework for Intelligent Network Adaptations. The applicability of the software artifact is demonstrated with the introduction of a novel self-healing mechanism for interdependent power networks, which optimizes power flow exchanges between a damaged and a healer network to mitigate power cascading failures. Results show a significant decrease in the damage spread by self-healing synergies, while the degree of interconnectivity between the power networks indicates a tradeoff between links survivability and load served. The contributions of this paper aspire to bring closer several research communities working on modeling and simulation of different domains with an economic and societal impact on the resilience of real-world interdependent networks.

Power grid infrastructures have been exposed to several terrorists and cyber attacks from different perspectives and have resulted in critical system failures. Among different attack strategies, simultaneous attack is feasible for the attacker if enough resources are available at the moment. In this paper, vulnerability analysis for simultaneous attack is investigated, using a modified cascading failure simulator with reduced calculation time than the existing methods. A new damage measurement matrix is proposed with the loss of generation power and time to reach the steady-state condition. The combination of attacks that can result in a total blackout in the shortest time are considered as the strongest simultaneous attack for the system from attacker's viewpoint. The proposed approach can be used for general power system test cases. In this paper, we conducted the experiments on W&W 6 bus system and IEEE 30 bus system for demonstration of the result. The modified simulator can automatically find the strongest attack combinations for reaching maximum damage in terms of generation power loss and time to reach black-out.

The previous consideration of power grid focuses on the power system itself, however, the recent work is aiming at both power grid and communication network, this coupling networks are firstly called as interdependent networks. Prior study on modeling interdependent networks always extracts main features from real networks, the model of network A and network B are completely symmetrical, both degree distribution in intranetwork and support pattern in inter-network, but in reality this circumstance is hard to attain. In this paper, we deliberately set both networks with same topology in order to specialized research the support pattern between networks. In terms of initial failure from power grid or communication network, we find the remaining survival fraction is greatly disparate, and the failure initially from power grid is more harmful than failure initially from communication network, which all show the vulnerability of interdependency and meantime guide us to pay more attention to the protection measures for power grid.

This paper combines FMEA and n2 approaches in order to create a methodology to determine risks associated with the components of an underwater system. This methodology is based on defining the risk level related to each one of the components and interfaces that belong to a complex underwater system. As far as the authors know, this approach has not been reported before. The resulting information from the mentioned procedures is combined to find the system's critical elements and interfaces that are most affected by each failure mode. Finally, a calculation is performed to determine the severity level of each failure mode based on the system's critical elements.

In this paper, we present an algorithm for estimating the state of the power grid following a cyber-physical attack. We assume that an adversary attacks an area by: (i) disconnecting some lines within that area (failed lines), and (ii) obstructing the information from within the area to reach the control center. Given the phase angles of the buses outside the attacked area under the AC power flow model (before and after the attack), the algorithm estimates the phase angles of the buses and detects the failed lines inside the attacked area. The novelty of our approach is the transformation of the line failures detection problem, which is combinatorial in nature, to a convex optimization problem. As a result, our algorithm can detect any number of line failures in a running time that is independent of the number of failures and is solely dependent on the size of the network. To the best of our knowledge, this is the first convex relaxation for the problem of line failures detection using phase angle measurements under the AC power flow model. We evaluate the performance of our algorithm in the IEEE 118- and 300-bus systems, and show that it estimates the phase angles of the buses with less that 1% error, and can detect the line failures with 80% accuracy for single, double, and triple line failures.

With Software Defined Networking (SDN) the control plane logic of forwarding devices, switches and routers, is extracted and moved to an entity called SDN controller, which acts as a broker between the network applications and physical network infrastructure. Failures of the SDN controller inhibit the network ability to respond to new application requests and react to events coming from the physical network. Despite of the huge impact that a controller has on the network performance as a whole, a comprehensive study on its failure dynamics is still missing in the state of the art literature. The goal of this paper is to analyse, model and evaluate the impact that different controller failure modes have on its availability. A model in the formalism of Stochastic Activity Networks (SAN) is proposed and applied to a case study of a hypothetical controller based on commercial controller implementations. In case study we show how the proposed model can be used to estimate the controller steady state availability, quantify the impact of different failure modes on controller outages, as well as the effects of software ageing, and impact of software reliability growth on the transient behaviour.

Interconnect opens are known to be one of the predominant defects in nanoscale technologies. Automatic test pattern generation for open faults is challenging, because of their rather unstable behavior and the numerous electrical parameters which need to be considered. Thus, most approaches try to avoid accurate modeling of all constraints like the influence of the aggressors on the open net and use simplified fault models in order to detect as many faults as possible or make assumptions which decrease both complexity and accuracy. Yet, this leads to the problem that not only generated tests may be invalidated but also the localization of a specific fault may fail - in case such a model is used as basis for diagnosis. Furthermore, most of the models do not consider the problem of oscillating behavior, caused by feedback introduced by coupling capacitances, which occurs in almost all designs. In [1], the Robust Enhanced Aggressor Victim Model (REAV) and in [2] an extension to address the problem of oscillating behavior were introduced. The resulting model does not only consider the influence of all aggressors accurately but also guarantees robustness against oscillating behavior as well as process variations affecting the thresholds of gates driven by an open interconnect. In this work we present the first diagnostic classification algorithm for this model. This algorithm considers all constraints enforced by the REAV model accurately - and hence handles unknown values as well as oscillating behavior. In addition, it allows to distinguish faults at the same interconnect and thus reducing the area that has to be considered for physical failure analysis. Experimental results show the high efficiency of the new method handling circuits with up to 500,000 non-equivalent faults and considerably increasing the diagnostic resolution.

Power networks can be modeled as networked structures with nodes representing the bus bars (connected to generator, loads and transformers) and links representing the transmission lines. In this manuscript we study cascaded failures in power networks. As network structures we consider IEEE 118 bus network and a random spatial model network with similar properties to IEEE 118 bus network. A maximum flow based model is used to find the central edges. We study cascaded failures triggered by both random and targeted attacks to the edges. In the targeted attack the edge with the maximum centrality value is disconnected from the network. A number of metrics including the size of the largest connected component, the number of failed edges, the average maximum flow and the global efficiency are studied as a function of capacity parameter (edge critical load is proportional to its capacity parameter and nominal centrality value). For each case we identify the critical capacity parameter by which the network shows resilient behavior against failures. The experiments show that one should further protect the network for a targeted attack as compared to a random failure.

Cascading failure is an intrinsic threat of power grid to cause enormous cost of society, and it is very challenging to be analyzed. The risk of cascading failure depends both on its probability and the severity of consequence. It is impossible to analyze all of the intrinsic attacks, only the critical and high probability initial events should be found to estimate the risk of cascading failure efficiently. To recognize the critical and high probability events, a cascading failure analysis model for power transmission grid is established based on complex network theory (CNT) in this paper. The risk coefficient of transmission line considering the betweenness, load rate and changeable outage probability is proposed to determine the initial events of power grid. The development tendency of cascading failure is determined by the network topology, the power flow and boundary conditions. The indicators of expected percentage of load loss and line cut are used to estimate the risk of cascading failure caused by the given initial malfunction of power grid. Simulation results from the IEEE RTS-79 test system show that the risk of cascading failure has close relations with the risk coefficient of transmission lines. The value of risk coefficient could be useful to make vulnerability assessment and to design specific action to reduce the topological weakness and the risk of cascading failure of power grid.