Efficient TSV Fault Detection Scheme For High Bandwidth Memory Using Pattern Analysis

The day-by-day increase in digital loads draws attention towards the need for an efficient and compatible distribution network. An LVDC distribution network has the capability to fulfill such digital load demands. However, the major challenge of an LVDC distribution network is its vulnerability during a fault. The need for a high-speed fault detection method is inevitable before it can be widely adopted. This paper proposes a new fault detection method which extracts the features of the current during a fault. The proposed fault detection method uses the merits of overcurrent, the first and second derivative of current, and signal processing techniques. Three different features are extracted from a time domain current signal through a sliding window. The extracted features are based upon the root squared zero, second, and fourth order moments. The features are then set with individual thresholds to discriminate low-, high-, and very high-resistance faults. Furthermore, a fault is located through the superimposed power flow. Moreover, this study proposes a new method based on the vector sum of positive and negative pole currents to identify the faulty pole. The proposed scheme is verified by using a modified IEEE 13 node distribution network, which is implemented in Matlab/Simulink. The simulation results confirm the effectiveness of the proposed fault detection and identification method. The simulation results also confirm that a fault having a resistance of 1 m Ω is detected and interrupted within 250 μ s for the test system used in this study.

This paper reports on a new type of through-silicon via (TSV) defect, silicon fin defect, which was found after TSV deep-reactive-ion-etching (DRIE) process for TSV integration with front-end-of-line (FEOL) devices. One possible root cause for this defect is that the bulk micro defect (BMD) in silicon substrate serves as a micro-mask during etching and results in silicon fin defects at TSV bottom. These defects have to be eliminated as they are killer TSV defects for several reasons: (1) could serve as a weak point for isolation liner deposition; (2) could be a weak point for barrier/seed layer deposition; and (3) may cause mechanical failures during TSV backside reveal. Previously, silicon fin defects were removed by switching to a non-BMD silicon substrate for interposer application. However, for TSV integration with FEOL devices, the BMD layer serves as an intrinsic gettering layer for devices, therefore, it cannot be removed from the silicon substrate, which makes it challenging to get rid of silicon fin defects. In order to establish a non-destructive in-line detection method of the fin defects, scanning electron microscope (SEM) automatic process inspection (API) was set up to image the fin defects at the bottom of the trench. A special working point with high depth of focus (DoF) and contrast was created to obtain good top-down SEM imaging of the defects at the bottom of this high-aspect-ratio (HAR) structure. Three types of silicon substrates (A, B, and C) were used for this study to investigate the potential root cause. SEM API results show defect rates of 20%, 3.3% and 0% for substrates A, B, and C, respectively. This is in good agreement with both BMD simulation results and benchmarking data in which substrates A, B, and C had normalized BMD densities of 11.7, 5.74, and 1 cm-3, respectively, with a comparable BMD size of 80~90 nm and a denuded zone (DNZ) depth of 10~15 μm. The correlation between BMD density in a silicon substrate and silicon fin defect rate indicates that BMD is a key root cause for silicon fin defects. To eliminate silicon fin defects, an optimized DRIE process has been developed. On the same type of substrate, the DRIE process with a typical voltage bias results in a defect rate of 6.7%, while no silicon fin defect was detected out of 200 TSVs with a polynomial bias ramp to relatively higher final voltage bias during the last 15 μm etch. The hypothesis is that higher voltage bias is able to sputter away BMD and shows potential to get rid of the silicon fin defects at the TSV bottom. In summary, a capable inspection method, a preferred silicon substrate with BMD spec range, and a promising way for DRIE process optimization to eliminate the silicon fin defect at the TSV bottom have been identified and developed in this work. Detailed results and analysis, particularly the fin defect images, statistical inspection results, BMD benchmarking data, simulation results, and TSV profile with optimized process will be discussed in the paper.

Three-dimensional (3D) memories using through-silicon vias (TSVs) as vertical buses across memory layers will likely be the first commercial application of 3D integrated circuit technology. The memory dies to stack together in a 3D memory are selected by a die-selection method. The conventional die-selection methods do not result in a high-enough yields of 3D memories because 3D memories are typically composed of known-good-dies (KGDs), which are repaired using self-contained redundancies. In 3D memory, redundancy sharing between neighboring vertical memory dies using TSVs is an effective strategy for yield enhancement. With the redundancy sharing strategy, a known-bad-die (KBD) possibly becomes a KGD after bonding. In this paper, we propose a novel die-selection method using KBDs as well as KGDs for yield enhancement in 3D memory. The proposed die-selection method uses three search-space conditions, which can reduce the search space for selecting memory dies to manufacture 3D memories. Simulation results show that the proposed die-selection method can significantly improve the yield of 3D memories in various fault distributions.

The increasing importance of power electronic converters in supplying electrical energy to utility grids places a higher priority to detect and protect against fault conditions. Fault detection and isolation are particularly important for inverters that provide black-start recovery for microgrids since these converters provide the energy source for restoration after a power outage. This paper presents a new fault detection and location method for Cascaded B-Bridge (CHB) multilevel inverters. The new fault detection method is based on monitoring the output voltage of each cell and output current directions along with each switch's state. By monitoring each cell's output voltage and current direction, the faulty cell can be detected and isolated. After the faulty cell is detected, the faulty switch can be located by comparing the current direction with the switching states. This technique is implemented with Level-Shifted Pulse Width Modulation (LS-PWM) in order to maintain acceptable total harmonic distortion (THD) levels at the converter. The proposed method can be implemented for a CHB with any number of cells, can operate with nonlinear loads, and offers very fast detection times. Simulation and experimental results verify the performance of this method.

The failure of a rolling bearing may cause the shutdown of mechanical equipment and even induce catastrophic accidents, resulting in tremendous economic losses and a severely negative impact on society. Fault diagnosis of rolling bearings becomes an important topic with much attention from researchers and industrial pioneers. There are an increasing number of publications on this topic. However, there is a lack of a comprehensive survey of existing works from the perspectives of fault detection and fault type recognition in rolling bearings using vibration signals. Therefore, this paper reviews recent fault detection and fault type recognition methods using vibration signals. First, it provides an overview of fault diagnosis of rolling bearings and typical fault types. Then, existing fault diagnosis methods are categorized into fault detection methods and fault type recognition methods, which are separately revised and discussed. Finally, a summary of existing datasets, limitations/challenges of existing methods, and future directions are presented to provide more guidance for researchers who are interested in this field. Overall, this survey paper conducts a review and analysis of the methods used to diagnose rolling bearing faults and provide comprehensive guidance for researchers in this field.

This chapter deals with the recent advancements in IGBT fault detection and protection methods for modular multilevel converters (MMCs). First, the IGBT short-circuit fault and open-circuit fault are briefly introduced. Then, the detection and protection methods are present for the IGBT short-circuit faults. Following the introduction of the features of the submodule's IGBT open-circuit faults, several fault detection methods are presented for the MMC, including a Kalman filter based method, an integrator-based method, a sliding-time window based method, and an isolation forest based method. Finally, a conclusion is given for the chapter.

In the field of industrial maintenance, early detection of gearbox faults is crucial, yet challenging, especially under varying and unseen operating conditions and types of damage. Traditional gear fault detection methods are typically designed for known operating conditions. However, in real industrial scenarios, gear fault detection is always an open-set detection, in which working conditions are commonly unseen. To overcome these limitations, this study introduces the ResNetAutoencoder Fault Detection (RAFD) method. RAFD integrates a short-time Fourier transform with a pre-trained ResNet-18 model for feature extraction and an autoencoder for feature reconstruction, trained on normal operational data. Moreover, a similarity loss is introduced to minimize the distribution gap of different working conditions. The effectiveness of the method is evaluated against traditional autoencoder and one-class support vector machine across various operational conditions, with performance metrics including AUROC, precision, recall, and F1 -score. The results demonstrate RAFD's superior performance in both in-distribution and out-of-distribution tasks, underscoring its robustness and reliability in detecting normal and faulty gearbox states.

Data-driven fault detection and identification methods are important in large-scale chemical processes. However, some traditional methods often fail to show superior performance owing to the self-limitations and the characteristics of process data, such as nonlinearity, non-Gaussian distribution, and multi-operating mode. To cope with these issues, the k-NN (k-Nearest Neighbor) fault detection method and extensions have been developed in recent years. Nevertheless, these methods are primarily used for fault detection, and few papers can be found that examine fault identification. In this paper, in order to extract effective fault information, the relationship between various faults and abnormal variables is studied, and an accurate “fault–symptom” table is presented. Then, a novel fault identification method based on k-NN variable contribution and CNN data reconstruction theories is proposed. When there is an abnormality, a variable contribution plot method based on k-NN is used to calculate the contribution index of each variable, and the feasibility of this method is verified by contribution decomposition theory, which includes a feasibility analysis of a single abnormal variable and multiple abnormal variables. Furthermore, to identify all the faulty variables, a CNN (Center-based Nearest Neighbor) data reconstruction method is proposed; the variables that have the larger contribution indices can be reconstructed using the CNN reconstruction method in turn. The proposed search strategy can guarantee that all faulty variables are found in each sample. The reliability and validity of the proposed method are verified by a numerical example and the Continuous Stirred Tank Reactor system.

This paper discusses gate open-circuit fault detection and diagnosis method in SPWM modulated symmetric cascaded H-bridge multilevel inverter. First, an output voltage modelling under fault condition in cascaded H-bridge multilevel inverter is presented. Further, simulations of fault conditions have been carried out for the development of the fault detection and diagnosis method. Based on fault modelling and simulations, a fault detection and diagnosis method have been proposed based on the mean values of the bridge voltages as the diagnostic feature. Further, the proposed method has been validated experimentally on a 5-level symmetric sine pulse width modulated cascaded H-bridge multilevel inverter.

3-D integration of integrated circuits based on through silicon via (TSV) could achieve high-performance, multifunctional and heterogeneous microsystem. However, various defects inside TSV may degrade system performance, it is essential to detect these defects for system design and process improvement. In this paper, we propose a defect detection method using artificial neural network (ANN) to detect the volume density of voids in the TSV. Different ANNs are designed and trained, their performance is compared. The results demonstrate the detection method could predict void density accurately.

In this paper, a simple and fast open-phase fault (OPF) detection and fault tolerant control method is proposed for three-phase induction machine. Instead of using integrators or low pass filter, this method utilizes the phase current magnitude and trajectory to detect open phase fault in three-phase motor drive. This significantly increases the speed of fault detection, allowing an OPF to be detected and compensated within several sampling cycles, which is advantageous for safety-critical applications. In addition, a double synchronous reference frame PI (DSRF-PI) controller method is adopted as the fault tolerant controller. Compared to previous methods that utilizes feedforward voltage compensation, the DSRF-PI does not require prior knowledge on the machine parameters and operating condition, and therefore is easier to implement. The proposed fault detection and fault tolerant control method are simulated using MATLAB Simulink simulation where the results confirms the high speed and robustness of the proposed methods.

A Data-Driven Fault Detection and Diagnosis by NSGAII-t-SNE and Clustering Methods in the Chemical Process Industry

Three-dimensional (3D) memory products based on through silicon via (TSV) are widely developed to fulfill the ever-increasing demands of per unit area storage capacity. The yield is still one of the critical challenges for 3D memory. Redundancy technique is now widely used in industry to improve yield. How to reduce the overhead of redundancy by improving the utilization of redundancy is important to 3D memory. In this paper, we propose a row/column block-based mapping technique for 3D memory built-in self-repair scheme to improve the utilization of redundancy and low hardware overhead. Each row/column is divided into row/column block and the mapping can be performed at row/column-block level instead of the original row/column level. Therefore, more faulty cells can be clustered into the same row/column. Based on the proposed technology, a 3D-essential spare pivoting (ESP) algorithm is also proposed for the allocation of redundant rows and columns, and the area overhead of this algorithm is particularly low. The experimental results show that on an average the repair ratio of our proposed scheme is much better than the fault clustering technique by 12% and the redundancy-cost can reduce 23%.

This paper investigates the fault detection problem of instruments in process industry. Considering the difficulty of fault identification and the problems of multivariable and large computation complexity based on traditional kernel principal component analysis (KPCA), this paper presents a new method for fault detection and identification, which combines the coupling analysis with kernel principal component for multivariable fault detection and employed the local outlier factor (LOF) for multivariable fault identification. The new method consists of three parts. Firstly, according to nonlinear correlation of multivariable, coupling analysis and module division of variables based on detrended cross-correlation analysis (DCCA) are considered to reduce false alarm rate (FAR) and missed detection rate (MDR) in fault detection and identification. Secondly, KPCA is employed to detect fault in each sub-module of variables. Finally, for the sub-module which has the fault detected in second step, the LOF is adopted to calculate abnormal contribution of each variable in sub-modules to realize fault identification. To prove that the new method has the better capability of processing multivariable fault detection and the more accuracy rate on fault detection and identification than the conventional methods of KPCA, a case study on Tennessee process is carried out at the end.

The scarcity of rolling bearing fault data and the difficulty in annotating fault types prompt us to employ unsupervised fault detection methods. However, existing unsupervised fault detection methods rely on manual feature extraction or use indirect approaches such as reconstruction error or density approximation for fault detection. To address this issue, this paper proposes an unsupervised rolling bearing fault detection method based on a Normalizing flow model, namely the Real-Valued Non-Volume Preserving (Real NVP) model. Compared to existing methods, this approach utilizes vibration data from normal bearings for model training. It transforms the complex distribution of normal bearing data into a simple prior distribution through the Real NVP transformation. Subsequently, fault detection indicators are constructed within this simple prior distribution to achieve rolling bearing fault detection. Experiments on the benchmark bearing dataset demonstrate that the proposed method can effectively identify the fault states of rolling bearings and achieve fault detection results superior to other fault detection methods.