Time-varying FMEA method based on interval-valued spherical fuzzy theory

This paper proposes a time-varying failure mode and effect analysis (FMEA) method based on interval-valued spherical fuzzy theory, which not only improves the limitations in evaluating, weighting, and ranking but also considers the effect of time change. The process of distinguishing time changes enables the FMEA to have dynamic recognition capability, enabling it to identify critical failure modes more accurately. The interval-valued spherical fuzzy theory is used to deal with the uncertainty of intuitionistic linguistic evaluations. The advantages of two traditional approaches are combined to improve the weight determined method. Risk factors are divided into subjective and objective types. In the subjective risk factors, which are severity (S) and detection (D), the consistency of judgment is used as the acceptance standard. In the objective risk factors, which are occurrence (O), the time-varying characteristics are considered. The occurrence in a certain period is expressed as the integral of failure intensity in the time period. Interval-valued spherical fuzzy exponential risk priority number is proposed as the criterion for measuring the priority of failure modes. The effectiveness of the proposed method is verified using an example of spindle.     

An alternative evaluation of FMEA: Fuzzy ART algorithm

Failure Mode and Effects Analysis (FMEA) is a technique used in the manufacturing industry to improve production quality and productivity. It is a method that evaluates possible failures in the system, design, process or service. It aims to continuously improve and decrease these kinds of failure modes. Adaptive Resonance Theory (ART) is one of the learning algorithms without consultants, which are developed for clustering problems in artificial neural networks. In the FMEA method, every failure mode in the system is analyzed according to severity, occurrence and detection. Then, risk priority number (RPN) is acquired by multiplication of these three factors and the necessary failures are improved with respect to the determined threshold value. In addition, there exist many shortcomings of the traditional FMEA method, which affect its efficiency and thus limit its realization. To respond to these difficulties, this study introduces the method named Fuzzy Adaptive Resonance Theory (Fuzzy ART), one of the ART networks, to evaluate RPN in FMEA. Copyright © 2008 John Wiley & Sons, Ltd.     

Analysing semiconductor manufacturing big data for root cause detection of excursion for yield enhancement

With the shrinking feature size of integrated circuits driven by continuous technology migrations for wafer fabrication, the control of tightening critical dimensions is critical for yield enhancement, while physical failure analysis is increasingly difficult. In particular, the yield ramp up stage for implementing new technology node involves new production processes, unstable machine configurations, big data with multiple co-linearity and high dimensionality that can hardly rely on previous experience for detecting root causes. This research aims to propose a novel data-driven approach for Analysing semiconductor manufacturing big data for low yield (namely, excursions) diagnosis to detect process root causes for yield enhancement. The proposed approach has shown practical viability to efficiently detect possible root causes of excursion to reduce the trouble shooting time and improve the production yield effectively.     

An interval probability-based FMEA model for risk assessment: A real-world case

Failure mode and effect analysis (FMEA) is an effective quality tool to eliminate the risks and enhance the stability and safety in the fields of manufacturing and service industry. Nevertheless, the conventional FMEA has been criticized for its drawbacks in the evaluation process of risk factors or the determination of risk priority number (RPN), which may lead to inaccurate evaluation results. Therefore, in this paper, we develop a novel FMEA method based on rough set and interval probability theories. The rough set theory is adopted to manipulate the subjectivity and uncertainty of experts' assessment and convert the evaluation values of risk factors into interval numbers. Meanwhile, the interval exponential RPN (ERPN) is used to replace the traditional RPN due to its superior properties, eg, solving the problems of duplicate numbers and discontinuity of RPN values. Furthermore, an interval probability comparison method is proposed to rank the risk priority of each failure mode for avoiding the information loss in the calculation process of RPN. Finally, a real case study is presented, and the comparison analysis among different FMEA methods is conducted to demonstrate the reliability and effectiveness of the proposed FMEA method.     

Data transformation in SPC for semiconductor machinery control: A case of monitoring particles

Yield is an important indicator of productivity in semiconductor manufacturing. In the complex manufacturing process, the particles on wafers inevitably cause defects, which may result in chip failure and thus reduce yield. Semiconductor manufacturers initially use wafer testing to control the machine for the number of particles. This machinery control procedure aims to detect any unusual condition of machines, reduce defects in actual wafer production and thus improve yield. In practice, the distribution of particles does not usually follow a Poisson distribution, which causes an overly high rate of false alarms in applying the c-chart. Consequently, the semiconductor machinery cannot be appropriately controlled by the number of particles on machines. This paper primarily combines data transformation with the control chart based on a Neyman type-A distribution to develop a machinery control procedure applicable to semiconductor machinery. The proposed approach monitors the number of particles on the testing wafer of machines. A semiconductor company in Taiwan in the Hsinchu Science Based Industrial Park demonstrated the feasibility of the proposed method through the implementation of several machines. The implementation results indicated that the occurrence of false alarms declined extensively from 20% to 4%.     

Scheduling of wafer test processes in semiconductor manufacturing

This research focuses on solving a common wafer test scheduling problem in semiconductor manufacturing. During wafer testing, a series of test processes are conducted on wafers using computer-controlled test stations at various temperatures. The test processes are conducted in a specified order on a wafer lot, resulting in precedence constraints for the schedule. Furthermore, the assignment of the wafer lots to test stations and the sequence in which they are processed affects the time required to set up the test operations. Thus, the set-up times are sequence dependent. Four heuristics are developed to solve the test scheduling problem with the aim of minimizing the makespan required to test all wafers on a set of test stations. The heuristics generate a sorted list of wafer lots as a dispatching sequence and then schedule the wafer lots on test stations in order of appearance on the list. An experimental analysis and two case studies are presented to validate the proposed solution approaches. In the case studies, the heuristics are applied to actual data from a semiconductor manufacturing facility. For both case studies, the proposed solution approaches decrease the makespan by 23–45% compared with the makespan of the actual schedule executed in the manufacturing facility.     

SIL analysis of subsea control system components based on a typical OREDA database

Proper performance evaluation of subsea system components is of high significance for reliable operation and remote monitoring or the replacement of the components before the occurrence of any failure. As a part of subsea systems, subsea control system (SCS) plays a key role in accomplishing a reliable performance. Hence, achieving knowledge of the components’ failure rates is highly important in the safety analysis of SCS. To the author's knowledge, limited work is done on the safety analysis of SCS using failure rates for a multitude of components. Also, the number of research papers that are based on industrial works is restricted. Hence, this paper aims to provide a noticeable contribution in fulfilling the referred gap. For this purpose, a safety integrity-level (SIL) analysis is proposed based on a typical OREDA database. In the implementation of the proposed SIL, a failure mode classification table is provided for a selection of SCS components. This is followed by the estimation of several parameters, such as the total time in service, as well as obtaining the values of critical failure rates. The analysis indicates that signal failure is the failure mode occurring more than the other ones. Also, the subsea electronic module yields the highest value of critical failure rates. Besides, a comparison of parameter values is provided for two different versions of the utilized database.     

A machine learning approach to yield management in semiconductor manufacturing

Yield improvement is one of the most important topics in semiconductor manufacturing. Traditional statistical methods are no longer feasible nor efficient, if possible, in analysing the vast amounts of data in a modern semiconductor manufacturing process. For instance, a typical wafer fabrication process has more than 1000 process parameters to record on a single wafer and one manufacturing plant may produce tens of thousands wafers a day. Traditional approaches have limits in extracting the full benefits of the data. Therefore, the manufacturing data is poorly exploited even in the most sophisticated processes. Now it is widely accepted that machine learning techniques can provide powerful tools for continuous quality improvement in a large and complex process such as semiconductor manufacturing. In this work, memory based reasoning (MBR) and neural network (NN) learning are combined for yield improvement and an integrated framework is proposed for a yield management system based on hybrid machine learning techniques. In this hybrid system of NN and MBR, the feature weight set which is calculated from the trained neural network plays the core role in connecting both learning strategies and the explanation on prediction can be given by obtaining and presenting the most similar examples from the case base. The proposed system has advantages in typical semiconductor manufacturing problems such as scalability to large datasets, high dimensions and adaptability to dynamic situations.     

Boosted Stacking Ensemble Machine Learning Method for Wafer Map Pattern Classification

Recently, machine learning-based technologies have been developed to automate the classification of wafer map defect patterns during semiconductor manufacturing. The existing approaches used in the wafer map pattern classification include directly learning the image through a convolution neural network and applying the ensemble method after extracting image features. This study aims to classify wafer map defects more effectively and derive robust algorithms even for datasets with insufficient defect patterns. First, the number of defects during the actual process may be limited. Therefore, insufficient data are generated using convolutional auto-encoder (CAE), and the expanded data are verified using the evaluation technique of structural similarity index measure (SSIM). After extracting handcrafted features, a boosted stacking ensemble model that integrates the four base-level classifiers with the extreme gradient boosting classifier as a meta-level classifier is designed and built for training the model based on the expanded data for final prediction. Since the proposed algorithm shows better performance than those of existing ensemble classifiers even for insufficient defect patterns, the results of this study will contribute to improving the product quality and yield of the actual semiconductor manufacturing process.     

基于改进的TOPSIS法与RSR法的质量改善项目综合评估

针对失效模式与影响分析中的风险优先数（RPN）方法在质量改善项目排序问题上的局限性,提出一种新的排序方法。首先,借助模糊集理论和熵权法,确定决策指标的模糊指标值和主、客观权重;随后采用改进的理想解逼近法（TOPSIS）与秩和比法（RSR）获得优先级排序和分档;同时,提出并定义了相对应的关键术语：改善主题、难易程度、发生度和改善潜力,最后,结合企业实例验证了新排序方法的有效性和稳定性。     

模糊综合评价的PFMEA在ABS阀体制造中的应用

为了克服应用传统的过程实效模式与影响分析(PFMEA)时对失效模式的风险顺序识别度不高的问题,在防抱死制动系统(ABS)阀体制造中提出了具有模糊综合评价的PFMEA方法.通过将专家评估与系数变异相结合的方法,分别获得了评价因素集、因素权集、评价等级集.通过模糊综合评价可以计算等级值和最终的风险顺序.这一方法在ABS阀体制造中得到了有效的应用.     

基于FMEA和FTA的老年人汽车人机界面交互设计研究

目的 提升老年人汽车界面满意度,设计适合老年人使用的汽车界面.方法 提出基于失效模式与效应分析(FMEA)、故障树分析(FTA)的研究方法,首先成立会议小组进行SHERPA分析,绘制汽车界面层次任务分析图;随后运用FMEA找出汽车界面中的潜在失效模式,计算风险优先数值;接着选取风险优先数值高的失效模式,运用FTA寻找其发生的原因,对其以图形化表示;再对故障树进行定性定量分析,计算故障树的最小切集和失效事件发生概率,找到失效发生的核心因素;最后对失效事件进行改良并对改良后的结果进行验证.结论 以老年人汽车界面为例进行研究,该方法可以有效发现老年人操作汽车界面发生错误的原因,不仅可以帮助设计师找到改良的重点,还可以使老年人获得良好的驾驶体验.     

Multiple comparisons with the best for supplier selection with linear profiles

In some manufacturing processes, the quality of a process or product is characterised by a linear profile. Comparing the process yield of multiple suppliers with linear profiles is an important task in supplier evaluation. In this study, we consider linear profiles with two-sided specifications and present the multiple comparisons with the best method based on the process yield index to select the best supplier. A subset contains the best supplier determined from the confidence interval of the difference between the process yield indices of the unknown best supplier and all of the suppliers. A simulation study is used to conduct the statistical power analysis. The results confirm that the larger the number of levels or the number of profiles, the larger the power of test. The simulation results indicate that our proposed method can effectively identify the best supplier. Two real examples are used to illustrate the applications of our proposed method.     

Test sample allocation method for testability verification test

An extended failure mode effect and criticality analysis (FMECA)-based sample allocation method for testability verification is presented in this study to deal with the poor representativeness of test sample sets and the randomness of the testability evaluation results caused by unreasonable selection of failure samples. First, the fault propagation intensity is introduced as part of the extended information of FMECA, and the sample allocation impact factors of component units and failure modes are determined under this framework. Then, the failure mode similarity and impact factor support are defined, and the game decision method for weighing the relationship between similarity and support is proposed to obtain the weight of failure mode impact factor. Finally, a two-step allocation framework of test samples is formulated to realize the sample allocation of component units and failure modes. This method is applied to the testability verification test of a launch control system. Results show that this method can obtain more representative test samples compared with the traditional sample allocation method while effectively reducing randomness of single testability evaluation result.     

An interactive scheduler for a wafer probe centre in semiconductor manufacturing

This paper analyses production operations and scheduling constraints on a wafer probe centre in semiconductor manufacturing. Such actions as lot split and hot lot preemption are discussed. Due to the combinatorial difficulties of scheduling problems with sequence-dependent setup costs and multiple criteria, a humancomputer interactive scheduler named the 'interactive computer aided scheduling system' (ICASS) is proposed. This ICASS can search for schedules to achieve a specific performance level set by a human. An experiment was conducted on a wafer probe centre to compare the performance of ICASS with that of a manual approach and six priority rules. The results indicate that ICASS spent less scheduling time and provided better schedules than the manual approach. All the six priority rules yielded only poor schedules with unbalanced performance in three kinds of criteria, although they were beneficial in computation time.     

钢筋套筒灌浆连接性能试验研究及筒壁应力分析

完成了12个钢制套筒灌浆连接件的拉拔试验,系统研究了其破坏过程与破坏形态、荷载-位移曲线、套筒应变等,重点分析了锚固长度、钢筋直径对筒壁应力的影响。运用厚壁圆筒理论,明确套筒灌浆连接件中钢筋与灌浆料以及套筒之间的传力过程,并基于轴向拉力建立了筒壁纵、横向应力的计算模型。研究表明:套筒灌浆连接件破坏过程分为弹性、屈服、强化、颈缩四个阶段,屈服强度和极限强度与钢筋材性相近。随钢筋锚固长度增加,筒壁应力略有减小,且随着钢筋直径增加,筒壁的应力明显增大。同时,建议筒壁应力计算值与试验值吻合较好,可为钢筋套筒灌浆连接设计与研究提供借鉴。     

结合解释结构模型与网络分析法于FMEA的应用

引入解释结构模型法,分析出控制措施之间的层次、顺序关系,再用网络分析法考虑控制措施间的相依关系,进行客观权重的计算,作为FMEA中的新探测度,再进行新RPN的计算。以回炉焊接制程提升为研究案例,比较新方法与传统方法的差异,发现优先改善项目与改善顺序的不同产生不同结果,提升了改善的效果。     

基于改进FMEA的适老化智能药盒交互设计研究

目的 基于适老化智能药盒交互失效模式的数据,对智能药盒的交互模式进行优化设计,提升交互体验,有效降低交互差错率。方法 首先,成立改进FMEA专家小组进行系统性人因错误减少和预测方法分析（Systematic Human Error Reduction and Prediction Approach, SHERPA）;其次,构建智能药盒交互失误预防模型,然后对适老化智能药盒交互设计进行改进失效模式与效应分析（FailureModeand Effects Analysis, FMEA）;最后,针对需要改进的失效模式对智能药盒进行设计优化。结果 实验结果显示“实证对象”存在诸多交互失效模式,对相应交互失效模式进行优化,可有效提升老年人操作智能药盒的交互体验。结论 运用改进FMEA的方法,可以具体量化适老化智能药盒交互失效模式数据,对应失效模式数据的智能药盒交互优化设计,是可行有效的,能够有效降低老年人的用药风险,此方法可为产品交互设计研究提供新的方法路径。     

Failure mode and effects analysis based on D numbers and TOPSIS

Failure mode and effects analysis (FMEA) is a widely used technique for assessing the risk of potential failure modes in designs, products, processes, system, and services. One of the main problems with FMEA is the need to address a variety of assessments given by FMEA team members and the sequence of the failure modes according to the degree of risk factors. Many different methods have been proposed to improve the traditional FMEA, which is impractical when the risk assessments given by multiple experts to one failure mode are imprecise, incomplete, or inconsistent. However, the existing methods cannot adequately handle these types of uncertainties. In this paper, a new risk priority model based on D numbers and technique for the order of preference by similarity to ideal solution (TOPSIS) is proposed to evaluate the risk in FMEA. In the proposed model, the assessments given by the FMEA team members are represented by D numbers, where a new feasible and effective method can effectively represent the uncertain information. The TOPSIS method, a multicriteria decision‐making method is presented to rank the preference of failure modes with respect to risk factors. Finally, an application of the failure modes of the rotor blades of an aircraft turbine is provided to illustrate the efficiency of the proposed method.     

Variations in properties of atomic force microscope cantilevers fashioned from the same wafer

Variations in the mechanical properties of nominally identical V-shaped atomic force microscope (AFM) cantilevers sourced from the same silicon nitride wafer have been quantified by measuring the spring constants, resonant frequencies and quality factors of 101 specimens as received from the manufacturer using the thermal spectrum method of Hutter and Bechhoefer. The addition of thin gold coatings always lowers the resonant frequency but the corresponding spring constant can either increase or decrease as a result. The observed broad spread of spring constant values and the lack of correlations between the resonant frequency and spring constant can be attributed in part to the non-uniformity of composition and material properties in the thinnest dimension of such cantilevers which arise from the manufacturing process. The effects of coatings are dictated by the competing influence of differences in mass density and Young's modulus between the silicon nitride and the gold coating. An implication of this study is that cantilever calibration methods based on the assumption of uniformity of material properties of the cantilever in the thinnest dimension are unlikely to be applicable for such cantilevers.