Automatic identification of spatial defect patterns for semiconductor manufacturing

This research proposes an on-line diagnosis system based on denoising and clustering techniques to identify spatial defect patterns for semiconductor manufacturing. Today, even with highly automated and precisely monitored facilities used in a near dust-free clean room and operated with well-trained process engineers, the occurrence of spatial signatures on the wafer still cannot be avoided. Typical defect patterns shown on the wafer, including edge ring, linear scratch, zone type and mixed type, usually contain important information for quality engineers to remove their root causes of failures. In this paper, a spatial filter is simultaneously used to judge whether the input data contains any systematic cluster and to extract it from the noisy input. Then, an integrated clustering scheme combining fuzzy C means (FCM) with hierarchical linkage is adopted to separate various types of defect patterns. Furthermore, a decision tree based on two cluster features (convexity and eigenvalue ratio) is applied to a separated pattern to provide decision support for quality engineers. Experimental results show that both real dataset and synthetic dataset have been successfully extracted and classified. More importantly, the proposed method has potential to be further applied to other industries, such as liquid crystal display (LCD) and plasma display panel (PDP).     

Recognition of defect spatial patterns in semiconductor fabrication

This research develops an analytical structure comprised of a filtering scheme and a fuzzy rule-based inference system to help identify defect spatial patterns. These defect spatial patterns include ring, scratch, zone and repeating types. A set of image processing masks is designed to locate defect positions and then the filtering scheme is applied to extract defect clusters on wafers. With clearly identified defect clusters, three features of minimum rectangle area are used to locate and describe the shape of defect clusters. When all possible defect clusters are well represented by minimum rectangle areas, a set of fuzzy rules are established to combine all the defect clusters and therefore defect spatial patterns are identified. The experiments show that the present approach can effectively identify different defect spatial patterns on wafers.     

笔管缺陷自动化检测系统设计与研究

中国已成为笔类产品生产大国与出口大国，而笔管检测是制笔行业的关键工艺技术。针对目前制笔行业中笔管检测的需求，设计了笔管缺陷自动化检测系统，以提高笔管缺陷检测效率及笔管制造企业的生产质量。基于机器视觉及重心分类装置，采取分模块检测系统，对笔管的缺陷形态、类型进行鉴别与统计，高效率、高精度地实现笔管缺陷检测、残次品剔除与自动分拣。采用缺陷自动检测算法，利用计算机视觉检测技术进行缺陷边缘检测，分割出笔管的缺陷区域并定义主要缺陷类型，完成对笔管缺陷的判断与分类。通过构建、训练卷积神经网络，得到了拟合度较高的卷积神经网络模型，用于分析笔管的缺陷情况。实验结果表明，笔管缺陷自动化检测系统可以客观地检测笔管的缺陷，提高笔管生产效率，提升生产线的成品质量，具有较高的工程应用价值。     

Algorithmic Scheme for Concurrent Detection and Classification of Printed Circuit Board Defects

An ideal printed circuit board (PCB) defect inspection system can detect defects and classify PCB defect types. Existing defect inspection technologies can identify defects but fail to classify all PCB defect types. This research thus proposes an algorithmic scheme that can detect and categorize all 14-known PCB defect types. In the proposed algorithmic scheme, fuzzy c-means clustering is used for image segmentation via image subtraction prior to defect detection. Arithmetic and logic operations, the circle hough transform (CHT), morphological reconstruction (MR), and connected component labeling (CCL) are used in defect classification. The algorithmic scheme achieves 100% defect detection and 99.05% defect classification accuracies. The novelty of this research lies in the concurrent use of CHT, MR, and CCL algorithms to accurately detect and classify all 14-known PCB defect types and determine the defect characteristics such as the location, area, and nature of defects. This information is helpful in electronic parts manufacturing for finding the root causes of PCB defects and appropriately adjusting the manufacturing process. Moreover, the algorithmic scheme can be integrated into machine vision to streamline the manufacturing process, improve the PCB quality, and lower the production cost.     

A system for online detection and classification of wafer bin map defect patterns for manufacturing intelligence

Wafer bin maps (WBM) in circuit probe (CP) tests that present specific defect patterns provide crucial information to identifying assignable causes in the semiconductor manufacturing process. However, most semiconductor companies rely on engineers using eyeball analysis to judge defect patterns, which is time-consuming and not reliable. Furthermore, the conventional statistical process control used in CP tests only monitors the mean or standard deviation of yield rates and failure percentages without detecting defect patterns. To fill the gap, this study aims to develop a manufacturing intelligence solution that integrates spatial statistics and neural networks for the detection and classification of WBM patterns to construct a system for online monitoring and visualisation of WBM failure percentages and corresponding spatial patterns with an extended statistical process control chart. An empirical study was conducted in a leading semiconductor company in Taiwan to validate the effectiveness of the proposed system. The results show its practical viability and thus the proposed solution has been implemented in this company.     

Modeling and implementation of a neurofuzzy system for surface mount assembly defect prediction and control

A high-speed surface mount assembly can reduce both production cost and time; however, it could allow an enormous number of boards to be built before a problem is detected. Therefore, early detection and assessment of a surface mount assembly problem is critical for cost-effective manufacturing. This paper proposes a neurofuzzy system for surface mount assembly defect prediction and control. Hybrid data from both in-process quality control database and from a fractional factorial experimental design are collected for neurofuzzy learning and modeling. Customized programming codes are generated for rule retrieval and for graphical user interface modeling. The proposed system is successfully implemented at a surface mount assembly plant, ll significantly improves plant throughput by the downtime reduction that is a result of a better defect prediction and control.     

A structured approach to deadlock detection,avoidance and resolution in flexible manufacturing systems

Production scheduling models that determine part mix ratios and detailed schedules do not usually account for deadlocks that can be caused by part flow. Deadlocks must be prevented for operational control (especially in automated systems). The major thrust of this paper is in developing a structured model for deadlock detection, avoidance and resolution caused by part flow in manufacturing systems. A system status graph can be constructed for the parts currently in the system. Deadlock detection amounts to determining deadlocks in the system status graph. On the other hand, deadlock avoidance amounts to restricting parts movement so that deadlocks are completely avoided in the future. While deadlock detection is a one-step look ahead procedure, deadlock avoidance is a complete look ahead procedure. Deadlock resolution or recovery amounts to judiciously using a limited queue to recover from deadlocks. Deadlock detection and avoidance are absolutely crucial to uninterrupted operation of automated manufacturing systems. A model based in graph theory has been formulated to detect and avoid deadlocks in automated manufacturing systems.     

A model-based clustering approach to the recognition of the spatial defect patterns produced during semiconductor fabrication

Defects on semiconductor wafers tend to cluster and the spatial defect patterns of these defect clusters contain valuable information about potential problems in the manufacturing processes. This study proposes a model-based clustering algorithm for automatic spatial defect recognition on semiconductor wafers. A mixture model is proposed to model the distributions of defects on wafer surfaces. The proposed algorithm can find the number of defect clusters and identify the pattern of each cluster automatically. It is capable of detecting defect clusters with linear patterns, curvilinear patterns and ellipsoidal patterns. Promising results have been obtained from simulation studies.     

Fuzzy-based system reliability of a labour-intensive manufacturing network with repair

This paper presents a fuzzy-based assessment model to evaluate system reliability of a labour-intensive manufacturing system with repair actions. Due to the uncertainty in human performance, labour-intensive manufacturing systems must determine the capacity of each labourer in order to accurately characterise the performance of the systems. Therefore, we model such a manufacturing system as a fuzzy multi-state network in order to characterise the labourers’ influence on workstation performance. First, the workstation reliability is defined according to the loading state by three fuzzy membership functions, namely ‘under loading’, ‘normal loading’ and ‘over loading’, respectively. The system reliability is subsequently evaluated with fuzzy intersection operations in terms of these workstation reliabilities. Thus, the system reliability is defined as a fuzzy membership function to assess whether the manufacturing system performance is sufficient to satisfy the demand reliably. A case study of a footwear manufacturing system is illustrated to explain the proposed model. Furthermore, we apply the proposed model to a non-labour-intensive manufacturing network in order to validate the applicability to this class of systems.     

Fuzzy logic based leanness assessment and its decision support system

Manufacturing organisations have been witnessing a transition from mass manufacturing to lean manufacturing. Lean manufacturing is focused on the elimination of obvious wastes occurring in the manufacturing process, thereby enabling cost reduction. The quantification of leanness is one of the contemporary research agendas of lean manufacturing. This paper reports a study which is carried out to assess the leanness level of a manufacturing organisation. During this research study, a leanness measurement model has been designed. Then the leanness index has been computed. Since the manual computation is time consuming and error-prone, a computerised decision support system has been developed. This decision support system has been designated as FLBLA-DSS (decision support system for fuzzy logic based leanness assessment). FLBLA-DSS computes the fuzzy leanness index, Euclidean distance and identifies the weaker areas which need improvement. The developed DSS has been test implemented in an Indian modular switches manufacturing organisation.     

A manufacturing resource allocation method with knowledge-based fuzzy comprehensive evaluation for aircraft structural parts

Manufacturing of aircraft structural parts has the characteristics of multiple varieties, complex structures and small batches, which make the manufacturing resource allocation highly difficult. This paper proposes a manufacturing resource allocation method with knowledge-based fuzzy comprehensive evaluation, considering multiple manufacturing resources including process planners, machine tools and cutting tools, as well as manufacturing process schemes of aircraft structural parts. Knowledge in terms of experts’ experience and historical data is used for fuzzy comprehensive evaluation. A manufacturing resource allocation model is proposed based on the analysis of manufacturing processes of aircraft structural parts. The capability of planners, the complexity of structural parts, the reliability of machine tools, the reliability of cutting tools and the correlations between manufacturing resources and structural parts are evaluated using the fuzzy comprehensive evaluation method. Multiple manufacturing resources are allocated based on the fuzzy comprehensive evaluation results. A prototype system has been implemented and a case study is used to validate the proposed approach.     

Development of a surface defect inspection system using radiantlight from steel products in a hot rolling line

The steel industry is constantly trying to reduce production cost and improve quality by making the steel manufacturing processes continuous and faster. Currently, the rolling process of steel production is largely automated, while the finishing process is not yet appreciably automated. The finishing processes involve many tasks difficult to automate, such as defect inspection and repairing the detected defects. In recent years, however, many automated and labor-saving systems have been developed for use in the finishing processes. The surface defect inspection of steel products is the largest bottleneck in the finishing process. This paper describes an inspection system of steel surface defects for large sections, such as wide flange beams and I-beams. This system is based on applied radiant light and it senses the temperature deviation caused by defects. The wavelength of the detector is optimized to improve the signal-to-noise ratio. An optical attenuator was developed to compensate for the known temperature distribution across the product immediately after rolling. The image processor takes only 50 ms per image frame. Each time frame has the necessary image information to detect defects     

A generalised uncertain decision tree for defect classification of multiple wafer maps

Classification of defect chip patterns is one of the most important tasks in semiconductor manufacturing process. During the final stage of the process just before release, engineers must manually classify and summarise information of defect chips from a number of wafers that can aid in diagnosing the root causes of failures. Traditionally, several learning algorithms have been developed to classify defect patterns on wafer maps. However, most of them focused on a single wafer bin map based on certain features. The objective of this study is to propose a novel approach to classify defect patterns on multiple wafer maps based on uncertain features. To classify distinct defect patterns described by uncertain features on multiple wafer maps, we propose a generalised uncertain decision tree model considering correlations between uncertain features. In addition, we propose an approach to extract uncertain features of multiple wafer maps from the critical fail bit test (FBT) map, defect shape, and location based on a spatial autocorrelation method. Experiments were conducted using real-life DRAM wafers provided by the semiconductor industry. Results show that the proposed approach is much better than any existing methods reported in the literature.     

Development of decision support system for sustainability evaluation: a case study

The industries have created threat to the present environment through their manufacturing methods. Moreover, the excessive utilization of natural resources have led to scarcity and triggered danger for the future generations. So there exists a vital need for the modern companies to renovate their manufacturing technologies. Thus, a new concept of manufacturing process known as sustainable manufacturing has been introduced and it gained great importance in the present scenario. Sustainable manufacturing means the production of goods in such a way that it utilizes minimum natural resources and produces safer, cleaner, and environment-friendly products at an affordable cost. The purpose of this article is to assess the sustainability level of a manufacturing organization taking into consideration various factors needed for insuring sustainability. During the course of this research, a sustainability model was developed using fuzzy logic and the sustainability index was calculated. Manual calculation of sustainability index consumes more time and it is mistake prone. So, in order to avoid such inadequacies, a computer-based decision support system was developed designated as fuzzy-logic-based sustainability evaluation decision support system. The system calculates the fuzzy logic sustainability index, Euclidean distance, and fuzzy performance importance index. This model will help the companies to analyze various aspects of sustainability within their organization and work toward further improvement of it.     

Multi-step ART1 algorithm for recognition of defect patterns on semiconductor wafers

The integrated circuits (ICs) on wafers are highly vulnerable to defects generated during the semiconductor manufacturing process. The spatial patterns of locally clustered defects are likely to contain information related to the defect generating mechanism. For the purpose of yield management, we propose a multi-step adaptive resonance theory (ART1) algorithm in order to accurately recognise the defect patterns scattered over a wafer. The proposed algorithm consists of a new similarity measure, based on the p-norm ratio and run-length encoding technique and pre-processing procedure: the variable resolution array and zooming strategy. The performance of the algorithm is evaluated based on the statistical models for four types of simulated defect patterns, each of which typically occurs during fabrication of ICs: random patterns by a spatial homogeneous Poisson process, ellipsoid patterns by a multivariate normal, curvilinear patterns by a principal curve, and ring patterns by a spherical shell. Computational testing results show that the proposed algorithm provides high accuracy and robustness in detecting IC defects, regardless of the types of defect patterns residing on the wafer.     

Statistical monitoring and control of a low defect process

Advanced technologies today are such that it is possible to keep the occurrence of defects in manufactured products at very low levels. The use of the conventional c-chart for statistical control of defects in such products would encounter serious practical difficulties because the low defect counts would render invalid the theoretical assumptions used in the construction of the chart. Based on reasoning with fundamental probability distributions, this paper offers a simple and reliable solution that is particularly suited to on-line inspection and testing operations such as those found in an automated manufacturing environment.     

基于模糊综合评判的MIE需求诊断方法

研究了制造业信息化工程需求分析的现状，提出并设计了基于模糊综合评判的制造业信息化工程需求诊断方法，实现了由定性分析向定量分析的转变，指出了该方法的研究重点在于建立需求评价指标体系和模糊关系矩阵，并围绕这一过程建立了最终判断制造企业信息化工程能力的逻辑关系模型。     

Fuzzy adaptive preventive maintenance in a manufacturing control system: a step towards self-maintenance

In this paper, a framework of integrating preventive maintenance (PM) and manufacturing control system is proposed. Fuzzy-logic control is used to enable an intelligent approach of integrating PM and a manufacturing control system. This will contribute to the novel development of an integrated and intelligent framework in those two fields that are sometimes difficult to achieve. This idea is based on combining work on an intelligent real-time controller for a failure-prone manufacturing system using a fuzzy-logic approach (Yuniarto, M.N. and Labib, A.W., Optimal control system of an unreliable machine using fuzzy logic control: from design to implementation. Int. J. Prod. Res. (in press a); Yuniarto, M.N. and Labib, A.W., Intelligent real time control of disturbances in manufacturing systems. Integr. Manuf. Syst.: Int. J. Manuf. Technol. Manage. (in press b) and the work on PM proposed by Labib et al. (Labib, A.W., Williams, G.B. and O’Connor, R.F., An intelligent maintenance model (system): an application of analytic hierarchy process and a fuzzy logic rule-based controller. J. Oper. Res. Soc., 1998, 49, 745–757)). The aim of the research is to control a failure-prone manufacturing system and at the same time propose which PM method is applicable to a specific failure-prone manufacturing system. The mean time to repair and mean time between failures of the system are used as integrator agents, by using them to couple the two areas to be integrated (i.e. a maintenance system and manufacturing system).     

供应商选择模糊决策方法

制造资源的全球化以及各种先进制造模式的应用使得供应商选择问题变得越来越复杂，加之供应商选择问题中包含大量的不确定因素和模糊因素。在分析供应商评价指标体系所应遵循的原则的基础上，建立了一套供应商评价一般指标体系，然后，将模糊集合论的思想和方法引入其中，提出供应商模糊综合决策方法并给出实例予以验证。     

Feasibility study of the tactical design justification for reconfigurable manufacturing systems using the fuzzy analytical hierarchical process

Reconfigurable manufacturing systems (RMSs) are designed based on the current and future requirements of the market and the manufacturing system (MS). The first stage of designing an RMS at the tactical level is the evaluation of economic and manufacturing/operational feasibility. Because of risk and uncertainty in an RMS environment, this major task must be performed precisely before investment in the detailed design. The present paper highlights the importance of manufacturing capacity and functionality for the feasibility of an RMS design during reconfiguration processes. Due to uncertain demands of product families, the RMS key-design factors, i.e. capacity value, functionality degree and reconfiguration time, are characterized by the identified fuzzy sets. Consequently, an integrated structure of the analytical hierarchical process and fuzzy set theory is presented. The proposed model provides additional insights into a feasibility study of an RMS design by considering both technical and economical aspects. The fuzzy analytical hierarchical process model is examined in an industrial case study by means of Expert Choice software. Finally, the fuzzy multicriteria model is sensitively analysed within the fuzzy domains of those attributes, which are considered to be critical for the case study.