Built-in Self-Test and Fault Localization for Inter-Layer Vias in Monolithic 3D ICs

Monolithic 3D (M3D) integration provides massive vertical integration through the use of nanoscale inter-layer vias (ILVs). However, high integration density and aggressive scaling of the inter-layer dielectric make ILVs especially prone to defects. We present a low-cost built-in self-test (BIST) method that requires only two test patterns to detect opens, stuck-at faults, and bridging faults (shorts) in ILVs. We also propose an extended BIST architecture for fault detection, called Dual-BIST, to guarantee zero ILV fault masking due to single BIST faults and negligible ILV fault masking due to multiple BIST faults. We analyze the impact of coupling between adjacent ILVs arranged in a 1D array in block-level partitioned designs. Based on this analysis, we present a novel test architecture called Shared-BIST with the added functionality of localizing single and multiple faults, including coupling-induced faults. We introduce a systematic clustering-based method for designing and integrating a delay bank with the Shared-BIST architecture for testing small-delay defects in ILVs with minimal yield loss. Simulation results for four two-tier M3D benchmark designs highlight the effectiveness of the proposed BIST framework.

A Hierarchical Restoration Mechanism for Distribution Networks Considering Multiple Faults

Service restoration of distribution networks in contingency situations is one of the highly investigated and challenging problems. In the conventional service restoration method, utilities reconfigure the topological structure of the distribution networks to supply the consumer load demands. However, the advancements in renewable distributed generations define a new dimension for developing service restoration methodologies. This paper proposes a hierarchical service restoration mechanism for distribution networks in the presence of distributed generations and multiple faults. The service restoration problem is modeled as a complicated and hierarchical program. The objectives are to achieve the maximization of loads restored with minimization of switch operations while simultaneously satisfying grid operational constraints and ensuring a radial operation configuration. We present the service restoration mechanism, which includes the dynamic topology analysis, matching isolated islands with renewable distributed generations, network reconfiguration, and network optimization. A new code scheme that avoids feasible solutions is applied to generate candidate solutions to reduce the computational burden. We evaluate the proposed mechanism on the IEEE 33 and 69 systems and report on the collected results under multitype fault cases. The results demonstrate the importance of the available renewable distributed generations in the proposed mechanism. Moreover, simulation results verify that the proposed mechanism can obtain reasonable service restoration plans to achieve the maximization of loads restored and minimization of switching operations under different faults.

Assessment of Dynamic Bayesian Models for Gas Turbine Diagnostics, Part 2: Discrimination of Gradual Degradation and Rapid Faults

There are many challenges that an effective diagnostic system must overcome for successful fault diagnosis in gas turbines. Among others, it has to be robust to engine-to-engine variations in the fleet, it has to discriminate between gradual deterioration and abrupt faults, and it has to identify sensor faults correctly and be robust in case of such faults. To combine their benefits and overcome their limitations, two diagnostic methods were integrated in this work to form a multi-layer system. An adaptive performance model was used to track gradual deterioration and detect rapid or abrupt anomalies, while a series of static and dynamic Bayesian networks were integrated to identify component degradation, component abrupt faults, and sensor faults. The proposed approach was tested on synthetic data and field data from a single-shaft gas turbine of 50 MW class. The results showed that the approach could give acceptable accuracy in the isolation and identification of multiple faults, with 99% detection and isolation accuracy and 1% maximum error in the identified fault magnitude. The approach was also proven robust to sensor faults, by replacing the faulty signal with an estimated value that had only 3% error compared to the real measurement.

The 2019–2020 Southwest Puerto Rico Earthquake Sequence: Seismicity and Faulting

The 2019–2020 Southwest Puerto Rico earthquake sequence ruptured multiple faults with several moderate magnitude earthquakes. Here, we investigate the seismotectonics of this fault system using high-precision hypocenter relocation and inversion of the near-field strong motions of the five largest events in the sequence (5.6≤Mw≤6.4) for kinematic rupture models. The Mw 6.4 mainshock occurred on a northeast-striking, southeast-dipping normal fault. The rupture nucleated offshore ∼15 km southeast of Indios at the depth of 8.6 km and extended southwest–northeast and up-dip with an average speed of 1.55 km/s, reaching the seafloor and shoreline after about 8 s. The 6 January 2020 (10:32:23) Mw 5.7 and the 7 January 2020 (11:18:46) Mw 5.8 events occurred on two east–southeast-striking, near-vertical, left-lateral strike-slip faults. However, the 7 January 2020 (08:34:05) Mw 5.6 normal-faulting aftershock, which occurred only 10 min after the Mw 6.4 normal-faulting mainshock, ruptured on a fault with almost the same strike as the mainshock but situated ∼8 km farther east, forming a set of parallel faults in the fault system. On 11 January 2020, an Mw 6.0 earthquake occurred on a north–northeast-striking, westing-dipping fault, orthogonal to the faults hosting the strike-slip earthquakes. We apply template matching for the detection of missed, small-magnitude earthquakes to study the spatial evolution of the main part of the sequence. Using the template-matching results along with Global Positioning System analysis, we image the temporal evolution of a foreshock sequence (Caja swarm). We propose that the swarm and the main sequence were a response to a tectonic transient that most affected the whole Puerto Rico Island.

Modelling of faults in LoopStructural 1.0

Without properly accounting for both fault kinematics and observations of a faulted surface, it is challenging to create 3D geological models of faulted geological units. Geometries where multiple faults interact, where the faulted surface geometry significantly deviate from a flat plane and where the geological interfaces are poorly characterised by sparse datasets are particular challenges. There are two existing approaches for incorporating faults into geological surface modelling. One approach incorporates the fault displacement into the surface description but does not incorporate fault kinematics and in most cases will produce geologically unexpected results such as shrinking intrusions, fold hinges without offset and layer thickness growth in flat oblique faults. The second approach builds a continuous surface without faulting and then applies a kinematic fault operator to the continuous surface to create the displacement. Both approaches have their strengths; however, neither approach can capture the interaction of faults within complicated fault networks, e.g. fault duplexes, flower structures and listric faults because they either (1) impose an incorrect (not defined by data) fault slip direction or (2) require an over-sampled dataset that describes the faulted surface location. In this study, we integrate the fault kinematics into the implicit surface, by using the fault kinematics to restore observations, and the model domain prior to interpolating the faulted surface. This new approach can build models that are consistent with observations of the faulted surface and fault kinematics. Integrating fault kinematics directly into the implicit surface description allows for complexly faulted stratigraphy and fault–fault interactions to be modelled. Our approach shows significant improvement in capturing faulted surface geometries, especially where the intersection angle between the faulted surface and the fault surface varies (e.g. intrusions, fold series) and when modelling interacting faults (fault duplex).

Reexamination of Tsunami Source Models For the 20th Century Earthquakes Off Hokkaido and Tohoku Along the Eastern Margin of the Sea of Japan

Large earthquakes around Japan occur not only in the Pacific Ocean but also in the Sea of Japan, and cause both damage from the earthquake itself and from the ensuing tsunami to the coastal areas. Recently, offshore active fault surveys were conducted in the Sea of Japan by the Integrated Research Project on Seismic and Tsunami Hazards around the Sea of Japan (JSPJ), and their fault models (length, width, strike, dip, and slip angles) have been obtained. We examined the causative faults of M7 or larger earthquakes in the Sea of Japan during the 20th century using seismic and tsunami data. The 1940 off Shakotan Peninsula earthquake (MJMA 7.5) appears to have been caused by the offshore active faults MS01, MS02, ST01, and ST02 as modelled by the JSPJ. The 1993 off the southwest coast of Hokkaido earthquake (MJMA 7.8) likely occurred on the offshore active faults OK03a, OK03b, and OK05, while the 1983 Central Sea of Japan earthquake (MJMA 7.7) probably related to MMS01, MMS04, and MGM01. For these earthquakes, the observed tsunami waveforms were basically reproduced by tsunami numerical simulation from the offshore active faults with the slip amounts obtained by the scaling relation with three stages between seismic moment and source area for inland earthquakes. However, the observed tsunami runup heights along the coast were not reproduced at certain locations, possibly because of the coarse bathymetry data used for the simulation. The 1983 west off Aomori (MJMA 7.1) and the 1964 off Oga Peninsula (MJMA 6.9) earthquakes showed multiple faults near the source area that could be used to reproduce the observed tsunami waveforms; therefore, we could not identify the causative faults. Further analysis using near-field seismic waveforms is required for their identification of their causative faults and their parameters. The scaling relation for inland earthquakes can be used to obtain the slip amounts for offshore active faults in the Sea of Japan and to estimate the coastal tsunami heights and inundation area which can be useful for disaster prevention and mitigation of future earthquakes and tsunamis in the Sea of Japan.

Interval fuzzy probability method for power transformer multiple fault diagnosis

Accurately diagnosing power transformer faults is critical to improving the operational reliability of power systems. Although some researchers have made great efforts to improve the accuracy of transformer fault diagnosis, accurate diagnosis of multiple faults is still a difficult problem. In order to improve the accuracy of transformer multiple faults diagnosis, a multiple fault diagnosis method based on interval fuzzy probability is proposed. Different from the previous methods which provide single-value probability, this method use probability interval to represent the occurrence degree of various possible faults, which can objectively predict the potential faults that occurring in a transformer and provide a more reasonable explanation for the diagnosis results. In the proposed method, the interval fuzzy set is used to describe the evaluation of state variables and the interval fuzzy probability model based on interval weighted average is applied to integrate the fault information. The representative matrix of fault types based on fuzzy preference relationship is established to estimate the relative importance of each gas in the dissolved gases. The proposed method can provide the probability of probable faults in transformer, help engineers quickly determine the type and location of faults, and improve the accuracy of diagnosis and maintenance efficiency of transformer. The effectiveness of the method is verified with case studies.

Fault Detection and Diagnosis for Plasticizing Process of Single-Base Gun Propellant Using Mutual Information Weighted MPCA under Limited Batch Samples Modelling

Aiming at the lack of reliable gradual fault detection and abnormal condition alarm and evaluation ability in the plasticizing process of single-base gun propellant, a fault detection and diagnosis method based on normalized mutual information weighted multiway principal component analysis (NMI-WMPCA) under limited batch samples modelling was proposed. In this method, the differences of coupling correlation among multi-dimensional process variables and the coupling characteristics of linear and nonlinear relationships in the process are considered. NMI-WMPCA utilizes the generalization ability of a multi-model to establish an accurate fault detection model in limited batch samples, and adopts fault diagnosis methods based on a multi-model SPE statistic contribution plot to identify the fault source. The experimental results demonstrate that the proposed method is effective, which can realize the rapid detection and diagnosis of multiple faults in the plasticizing process.