Machine learning-based novelty detection for faulty wafer detection in semiconductor manufacturing

Since semiconductor manufacturing consists of hundreds of processes, a faulty wafer detection system, which allows for earlier detection of faulty wafers, is required. statistical process control (SPC) and virtual metrology (VM) have been used to detect faulty wafers. However, there are some limitations in that SPC requires linear, unimodal and single variable data and VM underestimates the deviations of predictors. In this paper, seven different machine learning-based novelty detection methods were employed to detect faulty wafers. The models were trained with Fault Detection and Classification (FDC) data to detect wafers having faulty metrology values. The real world semiconductor manufacturing data collected from a semiconductor fab were tested. Since the real world data have more than 150 input variables, we employed three different dimensionality reduction methods. The experimental results showed a high True Positive Rate (TPR). These results are promising enough to warrant further study.     

Anomaly detection of defects on concrete structures with the convolutional autoencoder

This paper reports the application of deep learning for implementing the anomaly detection of defects on concrete structures, so as to facilitate the visual inspection of civil infrastructure. A convolutional autoencoder was trained as a reconstruction-based model, with the defect-free images, to rapidly and reliably detect defects from the large volume of image datasets. This training process was in the unsupervised mode, with no label needed, thereby requiring no prior knowledge and saving an enormous amount of time for label preparation. The built anomaly detector favors minimizing the reconstruction errors of defect-free images, which renders high reconstruction errors of defects, in turn, detecting the location of defects. The assessment shows that the proposed anomaly detection technique is robust and adaptable to defects on wide ranges of scales. Comparison was also made with the segmentation results produced by other automatic classical methods, revealing that the results made by the anomaly map outperform other segmentation methods, in terms of precision, recall, F₁ measure and F₂ measure, without severe under- and over-segmentation. Further, instead of merely being a binary map, each pixel of the anomaly map is represented by the anomaly score, which acts as a risk indicator for alerting inspectors, wherever defects on concrete structures are detected.     

A virtual metrology system for semiconductor manufacturing

Nowadays, the semiconductor manufacturing becomes very complex, consisting of hundreds of individual processes. If a faulty wafer is produced in an early stage but detected at the last moment, unnecessary resource consumption is unavoidable. Measuring every wafer’s quality after each process can save resources, but it is unrealistic and impractical because additional measuring processes put in between each pair of contiguous processes significantly increase the total production time. Metrology, as is employed for product quality monitoring tool today, covers only a small fraction of sampled wafers. Virtual metrology (VM), on the other hand, enables to predict every wafer’s metrology measurements based on production equipment data and preceding metrology results. A well established VM system, therefore, can help improve product quality and reduce production cost and cycle time. In this paper, we develop a VM system for an etching process in semiconductor manufacturing based on various data mining techniques. The experimental results show that our VM system can not only predict the metrology measurement accurately, but also detect possible faulty wafers with a reasonable confidence.     

数据异常的监测技术综述

在目前大数据的环境下,相对于正常数据,异常类数据更难获取,也显得更加重要。异常检测的目的是检测出异于正常主体的活动数据。异常检测适用于机器故障诊断、数据挖掘以及疾病和入侵检测等多个领域。基于目前大量的异常检测方法,主要从异常类数据的有无来阐述,根据这个框架将主要的异常检测方法进行了分类,并评价了这些方法的优劣;最后重点讨论了基于深度学习的大数据异常检测方法,并分别介绍了不同的方法及相关的应用和未来的研究热点。     

A fuzzy hierarchical reinforcement learning based scheduling method for semiconductor wafer manufacturing systems

Scheduling semiconductor wafer manufacturing systems has been viewed as one of the most challenging optimization problems owing to the complicated constraints, and dynamic system environment. This paper proposes a fuzzy hierarchical reinforcement learning (FHRL) approach to schedule a SWFS, which controls the cycle time (CT) of each wafer lot to improve on-time delivery by adjusting the priority of each wafer lot. To cope with the layer correlation and wafer correlation of CT due to the re-entrant process constraint, a hierarchical model is presented with a recurrent reinforcement learning (RL) unit in each layer to control the corresponding sub-CT of each integrated circuit layer. In each RL unit, a fuzzy reward calculator is designed to reduce the impact of uncertainty of expected finishing time caused by the rematching of a lot to a delivery batch. The results demonstrate that the mean deviation (MD) between the actual and expected completion time of wafer lots under the scheduling of the FHRL approach is only about 30 % of the compared methods in the whole SWFS.     

基于宽度自编码器的VSLAM快速回环检测方法

回环检测对于视觉同步定位和建图（visual simultaneous localization and mapping,VSLAM）系统减小累计误差和重定位具有重要意义。为缩短回环检测在线运行时间,同时满足准确率召回率需求,提出了一种基于宽度自编码器的快速回环检测算法（fast loop closure detection-broad autoencoder,FLCD-BA）。该检测算法改进了宽度学习网络,通过无监督的方式从输入数据中自主学习数据特征,进而运用于回环检测任务。与传统的深度学习方法不同,该网络使用伪逆的岭回归算法求解权重矩阵,通过增量学习的方法实现网络的快速重构,从而避免了整个网络的重复训练。所提算法在三个公开数据集上进行了实验,无须使用GPU设备,且网络的训练时间相比词袋模型以及深度学习的方法有较大缩短。实验结果表明该算法在检测回环时具有较高的准确率和召回率,测试中每帧的平均运行时间仅需21 ms,为视觉SLAM系统的回环检测提供了一种新算法。     

Knowledge augmented broad learning system for computer vision based mixed-type defect detection in semiconductor manufacturing

Defect detection is a critical measurement process for intelligent manufacturing systems to provide insights for product quality improvement. For complex products such as integrated circuit wafers, several types of defects are usually coupled in a piece of wafer to form a mixed-type defect, which poses a challenge to current defect detection methods. This paper proposed a knowledge augmented broad learning system with a knowledge module and broad selective sampling module, which provides a multichannel selective sampling network to decouple the mixed-type defects. In this model, each channel is equipped with a pre-trained deformable convolution model to extract the feature of a fixed single-type defect. The knowledge module is designed to activate the candidate network channel by pre-detection of wafer maps. The experiment results indicated that the proposed model outperforms conventional models and other deep learning models, which demonstrated that the knowledge augmented broad selective sampling mechanism is effective for mixed-type defect detection.     

Deep learning based online metallic surface defect detection method for wire and arc additive manufacturing

Wire and arc additive manufacturing (WAAM) is an emerging manufacturing technology that is widely used in different manufacturing industries. To achieve fully automated production, WAAM requires a dependable, efficient, and automatic defect detection system. Although machine learning is dominant in the object detection domain, classic algorithms have defect detection difficulty in WAAM due to complex defect types and noisy detection environments. This paper presents a deep learning-based novel automatic defect detection solution, you only look once (YOLO)-attention, based on YOLOv4, which achieves both fast and accurate defect detection for WAAM. YOLO-attention makes improvements on three existing object detection models: the channel-wise attention mechanism, multiple spatial pyramid pooling, and exponential moving average. The evaluation on the WAAM defect dataset shows that our model obtains a 94.5 mean average precision (mAP) with at least 42 frames per second. This method has been applied to additive manufacturing of single-pass, multi-pass deposition and parts. It demonstrates its feasibility in practical industrial applications and has potential as a vision-based methodology that can be implemented in real-time defect detection systems.     

Modeling manufacturing processes using a genetic programming-based fuzzy regression with detection of outliers

Fuzzy regression (FR) been demonstrated as a promising technique for modeling manufacturing processes where availability of data is limited. FR can only yield linear type FR models which have a higher degree of fuzziness, but FR ignores higher order or interaction terms and the influence of outliers, all of which usually exist in the manufacturing process data. Genetic programming (GP), on the other hand, can be used to generate models with higher order and interaction terms but it cannot address the fuzziness of the manufacturing process data. In this paper, genetic programming-based fuzzy regression (GP-FR), which combines the advantages of the two approaches to overcome the deficiencies of the commonly used existing modeling methods, is proposed in order to model manufacturing processes. GP-FR uses GP to generate model structures based on tree representation which can represent interaction and higher order terms of models, and it uses an FR generator based on fuzzy regression to determine outliers in experimental data sets. It determines the contribution and fuzziness of each term in the model by using experimental data excluding the outliers. To evaluate the effectiveness of GP-FR in modeling manufacturing processes, it was used to model a non-linear system and an epoxy dispensing process. The results were compared with those based on two commonly used FR methods, Tanka’s FR and Peters’ FR. The prediction accuracy of the models developed based on GP-FR was shown to be better than that of models based on the other two FR methods.     

A novel directional object detection method for piled objects using a hybrid region-based convolutional neural network

Digital transformation is an information technology (IT) process that integrates digital information with operating processes. Its introduction to the workplace can promote the development of progressively efficient manufacturing processes, accelerating competition in terms of speed and production capacity. Equipment combined with computer vision has begun to replace manpower in certain industries including manufacturing. However, current object detection methods are unable to identify the actual rotation angle of a specific grasped target while objects are piled. Hence this study proposes a framework based on deep learning that integrates two object detection models. Faster R-CNN (region based convolutional neural network) is utilized to search for the direction reference point of the target, and Mask R-CNN is adopted to obtain the segmentation that not only forms the basis of an area filter but also generates a rotated bounding box by minAreaRect function. After integrating the output from two models, the location and actual rotated angle of target can be obtained. The purpose of this research is to provide the robot arm with the position and angle information of the object located on the top for grasping. An empirical dataset of piled footwear insoles was employed to test the proposed method during the assembly process. Results show that the accuracy of the detection reached 96.26%. The implementation of proposed method in the manufacturing process not only can save man power who responsible for sorting out products but also reduce process time to enlarge production capacity. The proposed method can serve as a part of smart manufacturing system to enhance the enterprise’s competitiveness in the future.     

Anomaly detection for construction vibration signals using unsupervised deep learning and cloud computing

In-operation construction vibration monitoring records inevitably contain various anomalies caused by sensor faults, system errors, or environmental influence. An accurate and efficient anomaly detection technique is essential for vibration impact assessment. Identifying anomalies using visualization tools is computationally expensive, time-consuming, and labor-intensive. In this study, an unsupervised approach for detecting anomalies in construction vibration monitoring data was proposed based on a temporal convolutional network and autoencoder. The anomalies were autonomously detected on the basis of the reconstruction errors between the original and reconstructed signals. Considering the false and missed detections caused by great variability in vibration signals, an adaptive threshold method was applied to achieve the best identification performance. This method used the log-likelihood of the reconstruction errors to search for an optimal coefficient for anomalies. A distributed training strategy was implemented on a cloud platform to speed up training and perform anomaly detection without significant time delay. Construction-induced accelerations measured by a real vibration monitoring system were used to evaluate the proposed method. Experimental results show that the proposed approach can successfully detect anomalies with high accuracy; and the distributed training can remarkably save training time, thereby realizing anomaly detection for online monitoring systems with accumulated massive data.     

Deep-learning-based anomaly detection for lace defect inspection employing videos in production line

Defect inspection plays an essential role in ensuring quality of industrial products. The most widely used human visual inspection method has some drawbacks such as high cost and low efficiency, which bring an eager demand for the application of automatic defect inspection algorithm in actual production. However, few industrial production lines use automatic detection devices due to the gap between data collected in the actual production environment and ready-made datasets. Lace is one of the industrial products which completely depends on manual defect inspection. The complex and fine texture of lace makes it difficult to extract regular patterns using the existing image-based defect inspection methods. In this paper, we propose to collect lace videos in the weaving stage and design a deep-learning-based anomaly detection framework to detect lace defects. The framework contains three stages, namely video pre-processing stage, pixel reconstruction stage and pixel classification stage. In the offline phase, only defect-free lace videos are needed to train the pixel reconstruction model and calculate the detection threshold by our adaptive thresholding method. In the online phase, the proposed framework reconstructs lace videos and performs defect inspection using reconstruction error and the pre-set threshold. As far as we know, this paper the first to detect fabric defects by videos. Experimental results on artificial defect videos demonstrate the effectiveness of the proposed framework.     

A Kalman filter-based R2R control system with parallel stochastic disturbance models for semiconductor manufacturing processes

A Kalman filter-based run-to-run control system has been proposed for minimum variance control of semiconductor manufacturing process. In the proposed control system, both gain- and bias-varying process models combined with different stochastic disturbance models were considered and identified in parallel. The best-fit model is selected and used for the R2R controller design. Sub-models of the ARIMA(1,1,1) process were considered for stochastic modeling of the bias and gain variation, and the Kalman filters are used to find the optimum model parameter estimation. The control performance was analyzed for each case of the disturbance model to investigate the expected benefit from the control system in comparison with the EWMA filter-based controller.     

Automatic defect detection of metro tunnel surfaces using a vision-based inspection system

Due to the impact of the surrounding environment changes, train-induced vibration, and human interference, damage to metro tunnel surfaces frequently occurs. Therefore, accidents caused by the tunnel surface damage may happen at any time, since the lack of adequate and efficient maintenance. To our knowledge, effective maintenance heavily depends on the all-round and accurate defect inspection, which is a challenging task, due to the harsh environment (e.g., insufficient illumination, the limited time window for inspection, etc.). To address these problems, we design an automatic Metro Tunnel Surface Inspection System (MTSIS) for the efficient and accurate defect detection, which covers the design of hardware and software parts. For the hardware component, we devise a data collection system to capture tunnel surface images with high resolution at high speed. For the software part, we present a tunnel surface image pre-processing approach and a defect detection method to recognize defects with high accuracy. The image pre-processing approach includes image contrast enhancement and image stitching in a coarse-to-fine manner, which are employed to improve the quality of raw images and to avoid repeating detection for overlapped regions of the captured tunnel images respectively. To achieve automatic tunnel surface defect detection with high precision, we propose a multi-layer feature fusion network, based on the Faster Region-based Convolutional Neural Network (Faster RCNN). Our image pre-processing and the defect detection methods also promising performance in terms of recall and precision, which is demonstrated through a series of practical experimental results. Moreover, our MTSIS has been successfully applied on several metro lines.     

Real-time traffic incident detection using a probabilistic topic model

Traffic congestion occurs frequently in urban settings, and is not always caused by traffic incidents. In this paper, we propose a simple method for detecting traffic incidents from probe-car data by identifying unusual events that distinguish incidents from spontaneous congestion. First, we introduce a traffic state model based on a probabilistic topic model to describe the traffic states for a variety of roads. Formulas for estimating the model parameters are derived, so that the model of usual traffic can be learned using an expectation–maximization algorithm. Next, we propose several divergence functions to evaluate differences between the current and usual traffic states and streaming algorithms that detect high-divergence segments in real time. We conducted an experiment with data collected for the entire Shuto Expressway system in Tokyo during 2010 and 2011. The results showed that our method discriminates successfully between anomalous car trajectories and the more usual, slowly moving traffic patterns.     

Real-time anomaly detection framework using a support vector regression for the safety monitoring of commercial aircraft

The development of an automated health monitoring framework is critical for aviation system safety, especially considering the expected increase in air traffic over the next decade. Conventional approaches such as model-based and exceedance methods have a low detection accuracy and are limited to specific applications. This paper proposes a robust real-time health monitoring framework for detecting performance anomalies, which may impact system safety during flight operations, with high accuracy and generalized applicability. The proposed monitoring framework utilizes sensor data from commercial flight data recorders to predict possible flight performance anomalies. Decimation, a signal processing technique, in conjunction with Savitzky-Golay filtering is utilized to preprocess the dataset and mitigate sampling rate and noise issues that prevent direct usage of historical flight data. Correlation-based feature subset selection is subsequently performed, and these features are used to train a support vector machine that predicts flight performance. With this model, performance anomalies in the test data are automatically detected based on deviations from the predicted flight behavior. The proposed monitoring framework was demonstrated to detect performance anomalies in real-time and exhibited accurate detection capabilities with high computational efficiency.