A new strategy for defect inspection by the virtual inspection in semiconductor wafer fabrication

The defect of process equipments is a major factor that impairs the yields in the mass production of semiconductor wafer fabrication and it is a main supervision means to use high-resolution defect inspection tools to detect and monitor the defect damage. Due to the high investment costs of these inspection tools and the resulting decrease in the throughput, how to improve the sampling rate is an important issue for the associated inspection strategy. This paper proposes a new concept and implementation of virtual inspection (VI) to enhance the detection and monitoring of defect in semiconductor production process. The underlying theory of the VI concept is that the state variables identifications (SVIDs) of process equipments can reflect the process quality effectively and loyally. The approach of VI is to combine the application of the fault detection and classification (FDC), and the defect library and the re-engineering of inspection procedure to reach the full-scope of strategic objective. VI enables the defect monitoring to enter a new era by promoting the monitoring level of defect inspection from the previous lot-sampling basis to the wafer-sampling level, and hence upgrades the sampling strategy from random-sampling to full and right-sampling. In this study, various typical defect cases are utilized to illustrate how to create VI models and verify the reliability of the proposed approach. Furthermore, a feasible architecture of the VI implementation for mass production in semiconductor factory is presented in the paper.     

PIRF-Nav 2.0: Fast and online incremental appearance-based loop-closure detection in an indoor environment

This paper presents a fast and online incremental solution for an appearance-based loop-closure detection problem in a dynamic indoor environment. Closing the loop in a dynamic environment has been an important topic in robotics for decades. Recently, PIRF-Nav has been reported as being successful in achieving high recall rate at precision 1. However, PIRF-Nav has three main disadvantages: (i) the computational expense of PIRF-Nav is beyond real-time, (ii) it utilizes a large amount of memory in the redundant process of keeping signatures of places, and (iii) it is ill-suited to an indoor environment. These factors hinder the use of PIRF-Nav in a general environment for long-term, high-speed mobile robotic applications. Therefore, this paper proposes two techniques: (i) new modified PIRF extraction that makes the system more suitable for an indoor environment and (ii) new dictionary management that can eliminate redundant searching and conserve memory consumption. The results show that our proposed method can complete tasks up to 12 times faster than PIRF-Nav with only a slight percentage decline in recall. In addition, we collected additional data from a university canteen crowded during lunch time. Even in this crowded indoor environment, our proposed method has better real-time processing performance compared with other methods.     

Automatic detection of breast cancers in mammograms using structured support vector machines

Breast cancer is one of the most common cancers diagnosed in women. Large margin classifiers like the support vector machine (SVM) have been reported effective in computer-assisted diagnosis systems for breast cancers. However, since the separating hyperplane determination exclusively relies on support vectors, the SVM is essentially a local classifier and its performance can be further improved. In this work, we introduce a structured SVM model to determine if each mammographic region is normal or cancerous by considering the cluster structures in the training set. The optimization problem in this new model can be solved efficiently by being formulated as one second order cone programming problem. Experimental evaluation is performed on the Digital Database for Screening Mammography (DDSM) dataset. Various types of features, including curvilinear features, texture features, Gabor features, and multi-resolution features, are extracted from the sample images. We then select the salient features using the recursive feature elimination algorithm. The structured SVM achieves better detection performance compared with a well-tested SVM classifier in terms of the area under the ROC curve.     

A novel, electrically protein-manipulated microcantilever biosensor for enhancement of capture antibody immobilization

This study demonstrated a microcantilever biosensor for enhancement of capture antibody immobilization. The electrically protein-manipulated, microcantilever biosensor is featured with enhanced capture antibody immobilization, miniaturization, and high sensitivity. Thanks to the electric property of biological substances in a real environment, given charged proteins can be manipulated with attraction in solution under an electric field. It is evident that higher amount of capture antibody molecules immobilized onto sensing surfaces captures or detects specific molecules, indicating greater deflection and stresses as well. This however leads to significant cost in biosensors. With the merit of MEMS technique that allows highly fabrication-compatible integration into microcantilever biosensors, sparsely distributed antibody molecules in solution are attracted in focus onto a sensing solid surface under electric fields. As the sensing element of the gold-coated, V-shaped silicon nitride microcantilever also serves as an electrode, the electric fields are applied in a channel of flowing microfluidics by locally in-plane electrodes or by a top electrode arranged for three-dimensional fields. As expected, most charged proteins distributed in solution are effectively attracted onto the sensing area within the electric fields. This improves the efficiency of capture antibody immobilization and achieves an eight-fold reduction over the necessary amount of capture antibodies without applying electric fields. With such a successful manipulation of charged proteins, the novel microcantilever biosensor exhibits efficient use of capture antibodies in solution.     

分布式光纤振动传感系统的多级零频点定位研究

为了减少直线结构Sagnac分布式光纤传感系统的定位误差,提出一种多级零频点的优化方法,通过分析直线结构Sagnac分布式光纤传感定位原理,确定固定的采样速率下离散化是造成定位误差大的原因.并分析得出振动信号频域曲线会周期出现多个零频点,进而对振动定位的多级零频点优化方法进行了理论推导,阐明高倍零频点下的定位误差会有效减小.通过实验验证表明,在总长为11.769 km的传感光纤上,优化后的定位方法在不改变采样速率下可保证定位误差提升到±20 m范围内,可以基本满足系统对外界振动信号进行精确定位的要求.     

基于C—V模型的目标检测与提取

在目标检测与提取中,传统的蛇模型和基于活动轮廓的局部区域检测方法受到初始条件或者自身的收敛性约束的影响,不仅时间花费多,而且不具备鲁棒性。本文提出一种基于C V模型的变分水平集的目标检测与提取方法,通过大量实验验证,在花费时间和鲁棒性上得到了显著的改善。     

基于灰色关联分析法的软件缺陷类型预测

灰色关联分析法是一种描述元素之间影响程度的分析法,适合于小项目数据集。小项目数据集制约着传统的软件缺陷类型的预测方法,使得预测的结果往往不够准确和可靠。因此在灰色关联分析法的基础上提出了特征子集选择、异常工程检测以及软件缺陷类型预测3种方法。通过实验的分析对比,验证了在灰色关联分析法的基础上,提出的软件缺陷类型预测方法的准确性和可靠性。     

On detecting active worms with varying scan rate

Active worms have posed a major security threat to the Internet and many research efforts have focused on them. However, defending against them remains challenging due to their continuous evolution. In this paper, we study a new class of defense-oriented evolved worms, the Varying Scan Rate worm (the VSR worm in short). In order to circumvent detection by existing worm detection schemes, the VSR worm deliberately varies its scan rate according to these schemes’ weaknesses. To counteract the VSR worm, we design a new worm-detection scheme, the attack-target Distribution Entropy-based Dynamic detection scheme (DED detection for short). DED detection utilizes the attack-target distribution and robust statistical feature in conjunction with dynamic decision adaptation to distinguish worm-scan traffic from non-worm-scan traffic. We present a comparatively complete space of detection schemes and conduct extensive performance evaluations on the DED detection scheme compared with other schemes, using real-world Internet traces as background scan traffic. Our data clearly demonstrate the effectiveness of the DED detection scheme in detecting both the VSR worm and the traditional worm.     

On the estimation of adequate test set size using fault failure rates

Test set size in terms of the number of test cases is an important consideration when testing software systems. Using too few test cases might result in poor fault detection and using too many might be very expensive and suffer from redundancy. We define the failure rate of a program as the fraction of test cases in an available test pool that result in execution failure on that program. This paper investigates the relationship between failure rates and the number of test cases required to detect the faults. Our experiments based on 11 sets of C programs suggest that an accurate estimation of failure rates of potential fault(s) in a program can provide a reliable estimate of adequate test set size with respect to fault detection and should therefore be one of the factors kept in mind during test set construction. Furthermore, the model proposed herein is fairly robust to incorrect estimations in failure rates and can still provide good predictive quality. Experiments are also performed to observe the relationship between multiple faults present in the same program using the concept of a failure rate. When predicting the effectiveness against a program with multiple faults, results indicate that not knowing the number of faults in the program is not a significant concern, as the predictive quality is typically not affected adversely.     

Collision and intersection detection of two ruled surfaces using bracket method

Collision and intersection detection of surfaces is an important problem in computer graphics and robotic engineering. A key idea of our paper is to use the bracket method to derive the necessary and sufficient conditions for the collision of two ruled surfaces. Then the numerical intersection curve can be characterized. The cases for two bounded ruled surfaces are also discussed.