Requirement estimation for indirect workforce allocation in semiconductor manufacturing

This work, based on a real case, presents a model to estimate indirect workforce requirements of semiconductor fabrication facilities (fabs) so that the workforce can be fairly allocated. There is a concern in a real setting to fairly allocate the overall corporate workforce among the fabs, particularly when they compete in performance, and properly determining the actual requirement is the most critical in the decision process. The actual requirements of the workforce, especially the indirect workforce, for fabs may be indeterminate due to the lack of a well-defined workforce-output relationship. This paper presents a non-parametric frontier approach for estimating the indirect workforce, and the estimate is based on the best past performance adjusted to reflect the expected productivity growth. An empirical study was conducted in a leading foundry in Taiwan that has a number of 8-inch fabs. The proposed (re)allocation approach can provide an explicit decision support mechanism to balance the workloads in light of various production environments to enable an equitable basis for performance evaluation to foster constructive competition among the fabs.     

面向制造企业战略目标的精益评价

针对精益水平无法直接反映制造企业战略目标的问题,建立制造企业战略目标与精益水平指标间的映射规则。利用解释结构模型方法建立精益水平指标体系的网络模型,并采用模糊网络分析法及灰色关联度法进行评价。案例应用实现了企业战略目标到关键精益指标的转化,其结果与直接对企业目标评价结果最大偏差不超过5%,证明所提评价模式和映射规则的可行性和有效性。     

Agricultural farming planning and water resources management under fuzzy uncertainty

In this study, a rank-based fuzzy optimization (RBFO) approach was provided for supporting agricultural farming planning under complex uncertainties. RBFO was built by linking feasibility-degree-based fuzzy programming with fuzzy ranking. First, the model was solved under different levels of violation risks to environmental or capacity constraints, leading to a group of cost-effective solutions; then, the ranking technique was used to help identify the most desirable solution from different alternatives, based on the trade-off analysis between the system benefit and risk. A farm planning case with water resources management consideration was used to demonstrate the applicability of RBFO. The study results indicate that the provided systems analysis approach could help decision makers to gain a deep insight into the complex interrelationships among agricultural economic development, pollution control and water resources conservation in a farm planning system, and to seek cost-effective production patterns with favourable deliberation of various factors.     

Fuzzy multiple-goal programming for analysing outsourcing cost-effectiveness in hi-tech manufacturing

Given high-tech industry trends such as limited production, diverse model range, short product life cycle, and high customer responsiveness, issues related to recognitive core vs. non-core operations in internal vs. external environments are crucial in hi-tech production systems planning. This work elucidates the relationship between capacity planning and suppliers with a focus on quantity allocation, manufacturing quantity, capacity limit, warehouse space, and time period for hi-tech production planning. A fuzzy multiple-goal programming approach is adopted to model total cost, holding cost, and rework cost in order to analyse the relative cost-effectiveness of different factors. The proposed model was examined via the fuzzy analysis of its application to an actual building-integrated photovoltaic (BIPV) manufacturer with variable characteristics. The proposed model of cost-effectiveness accommodates variables such as multiple components, quality, and responsiveness, it integrates multi-stage functions, and a specific implementation is demonstrated. Various scenarios are designed to analyse the transaction options for outsourcing under combined schemes. The analytical results in this study can help decision makers to systematically analyse the cost effectiveness of outsourcing during capacity planning in practical applications.     

Incorporating engineering process improvement activities into production planning formulations using a large-scale wafer fab model

In most semiconductor wafer fabrication facilities (wafer fabs), both production and engineering lots share the same expensive equipment. Production lots will be shipped to customers whereas engineering lots are used to support production and process development efforts. While production activities might lead to large revenue, engineering activities result in increased future output. In the present paper, we propose three different production planning formulations. The first formulation assumes a reduced available capacity for production due to engineering activities. Costs for production products are minimised. The second formulation is based on the idea that aggregated demand is available for engineering activities for the entire planning window. Costs for production and engineering products are minimised. Learning effects are incorporated to model the capacity increase due to engineering activities. The third model assumes that demand information for engineering activities is available only for the first period. In addition to learning effects, forgetting effects are modelled to provide an incentive to plan releases of engineering lots in later periods. Costs for production and engineering products and forgetting effects are minimised. The performance of the production planning models is assessed using a simulation model of a large-scale wafer fab including specific dispatching strategies to deal with production and engineering lots. The simulation results demonstrate that the second model slightly outperforms the third one when a rolling horizon approach is taken, while the second model provides significantly higher profit than the first one.     

Using FANP approach on selection of competitive priorities based on cleaner production implementation: a case study in PCB manufacturer,Taiwan

The aim of this paper is to identify and discuss some of the important and critical decision criteria including cleaner production implementation of an efficient system to prioritize competitive priorities. Fuzzy analytic network process (FANP) based methodology is discussed to tackle the different decision criteria involved in the selection of competitive priorities in current business scenario. FANP is an efficient tool to handle the fuzziness of the data involved in deciding the preferences of different decision variables. The linguistic level of comparisons produced by the professionals and experts for each comparison are tapped in the form of triangular fuzzy numbers to construct fuzzy pairwise comparison matrices. The implementation of the system is demonstrated by a problem having four stages of hierarchy which contains different criteria, attributes and alternatives at wider perspective. The proposed model can provide a hierarchical framework for the cleaner production implemented organization to select on its competitive priorities.     

A fuzzy‐neural approach for output projection in a semiconductor fabrication factory

Abstract

Output projection is a critical task for a semiconductor fabrication factory. There are two steps in output projection: predicting the output time for every job in the factory, and projecting the outputs into each period. For enhancing the effectiveness of output projection, a fuzzy‐neural approach is proposed in this study. Firstly, a fuzzy back propagation network (FBPN) is applied to generate fuzzy‐valued output time forecasts. Then, the fuzzy output projection function is proposed to project the outputs into each period. To evaluate the effectiveness of the proposed methodology and to make comparison with some traditional approaches, production simulation is also applied in this study to generate test data.     

Capacity requirements planning for twin Fabs of wafer fabrication

Based on the assumption of infinite capacity, a Capacity Requirements Planning System (CRPS) is developed for twin fabs of wafer fabrication. Several shared equipments exist only in one of the twin fabs linked by an Inter-Fab Material Handling System. CRPS consists of four major modules. WIP-Pulling Module pulls work-in-process (WIP) that is the closest to the end of the process route to meet the Master Production Schedule. Workload Accumulation Module then calculates the expected equipment loading in different time buckets. If WIP cannot meet the Master Production Schedule (MPS) requirement, new wafer lots need to be released. Wafer Release Time Module is used to determine the release time of new lots by evaluating their expected equipment loading at the twin fabs on various time buckets. According to the lot release time, Wafer Start Fab Module can be used to evaluate the expected loading for each of the twin fabs and determine the start fab to optimise the workload balance among these twin fabs on various days. Based on experimental design, simulation results show that CRPS can balance the equipment loading between the twin fabs with shared equipment, on various days, and across various equipments at various levels of demands.     

Fuzzy goal programming for material requirements planning under uncertainty and integrity conditions

In this paper, we formulate the material requirements planning) problem of a first-tier supplier in an automobile supply chain through a fuzzy multi-objective decision model, which considers three conflictive objectives to optimise: minimisation of normal, overtime and subcontracted production costs of finished goods plus the inventory costs of finished goods, raw materials and components; minimisation of idle time; minimisation of backorder quantities. Lack of knowledge or epistemic uncertainty is considered in the demand, available and required capacity data. Integrity conditions for the main decision variables of the problem are also considered. For the solution methodology, we use a fuzzy goal programming approach where the importance of the relations among the goals is considered fuzzy instead of using a crisp definition of goal weights. For illustration purposes, an example based on modifications of real-world industrial problems is used.     

Optimal vehicle allocation for an Automated Materials Handling System using simulation optimisation

The Automated Materials Handling System (AMHS) in the semiconductor industry plays a vital role in reducing wafer cycle times and enhancing fabrication facility (fab) productivity. Due to the complexity of the manufacturing process and the stochasticity introduced by the inherent variability of processing times, the vehicle allocation for the AMHS is a challenging task, especially in 300?mm?wafer fabs where the AMHS comprises both the interbay and intrabay systems to perform the timely deliveries. This paper studied the vehicle allocation problem in a typical 300?mm?wafer fab. We formulated it as a simulation optimisation problem and proposed a conceptual framework to handle the problem. A discrete event simulation model was developed to characterise the AMHS, and the technique of simulation optimisation was applied to obtain the optimal vehicle allocation for both the interbay and intrabay systems. To demonstrate the feasibility and advantages of the simulation optimisation approach, a photobay example was used to compare the solution derived from the analytical model and simulation optimisation model. Finally, an empirical problem based on real data was conducted to show the viability of the proposed framework in practice.     

Estimating the sustainable capacity of semiconductor fab workstations

The cost of a semiconductor wafer fabrication facility (i.e. ‘fab’) may be as much as five or more billion US dollars. As such it is essential to determine the capacity (e.g. in terms of ‘wafer starts per week’) of such facilities. An accurate estimate of capacity – under real world conditions – is, however, difficult to achieve. Furthermore, the method for the computation of the capacity of a semiconductor fab is significantly different from that for the capacity of workstations in more conventional, less complex factories. This is due in part to the reentrant nature of the workstations (a.k.a. ‘toolsets’) that comprise a fab's production line as well as the ubiquitous employment of operation-to-machine dedications (a.k.a. layer-to-tool qualifications) – plus the need to consider multiple products employing, perhaps, a different sequence of processing steps. In this article, the matter of workstation capacity, in general, and semiconductor fabs, in particular, is examined. A means to quickly and effectively determine the maximum theoretical capacity of a workstation is developed and illustrated – followed by a way in which the more practical maximum sustainable capacity may be estimated.     

Generating daily production plans for complex manufacturing facilities using multi-objective genetic algorithms

Modern production tracking and planning systems produce good inventory plans. Unfortunately, daily production problems often produce an inventory profile that is significantly different from that of the production plan. Several manufacturing objectives are available to remedy inventory profile problems, but a weighted combination of them can be more effective. We propose using genetic algorithms to search for best weight assignments in multi-objective daily production planning problems. This approach takes snapshots of a factory's inventory, equipment capacity and demands to generate a near optimal shop floor daily production plan. The implemented system runs on a daily basis at midnight for 40 min and generates the following day's production plan. Tests of this system in a large-scale semiconductor manufacturing facility show the proposed approach generates production plans of high quality in reasonable run times under many factory conditions.     

The infrastructure of the timed EOPNs-based multiple-objective real-time scheduling system for 300 mm wafer fab

Modern semiconductor wafer fabrication systems are changing from 200?mm to 300?mm wafer processing, and with the dual promises of more chips per wafer and economy of scale, leading semiconductor manufacturers are attracted to developing and implementing 300?mm wafer fabs. However, in today's dynamic and competitive global market, a successful semiconductor manufacturer has to excel in multiple performance indices, such as manufacturing cycle time and on-time delivery, and simultaneously optimize these objectives to reach the best-compromised system achievement. To cope with this challenge, in this paper, the infrastructure of a timed EOPNs-based multiple-objective real-time scheduling system (MRSS) is proposed to tackle complex 300?mm wafer fabs. Four specific performance objectives pursued by contemporary semiconductor manufacturers are integrated into a priority-ranking algorithm, which can serve as the initial scheduling guidance, and then all wafer lots will be dynamically dispatched by the real-time state-dependent dispatching system. This dispatching control system is timed EOPN-based and adopts a heterarchical organization that leads to a better real-time performance and adaptability. As the foundation of real-time schedule, the timed EOPNs modelling approach is expounded in detail, and the prototype of the MRSS simulation system is also provided.     

基于遗传算法解决河流环境需水量优化调度的模糊多目标规划方法的研究

为实现城市受污染河流的环境需水量的合理调配,以各蓄水构筑物的调水量为决策变量,研究了在保证河流生态环境需求的基础上,实现整个流域的污染负荷的环境容量最大化、水量最大节约和调水成本最小化的方法,建立了环境需水调度的模糊多目标规划模型.模型中采用整合的非线性梯度隶属度函数作为目标函数,以遗传算法作为求解此模型的工具,并将该模型用于大庆市黎明河流域的环境水量调配,取得较好的效果.     

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.     

基于FANP方法的供应链配送中心评价选择

供应链配送中心的评价选择是供应链管理的重要课题.层次分析法(AHP)只能分析经过大量简化处理的树状结构或者其他理想结构.因此,采用解释结构模型(ISM)建立配送中心评价选择指标体系,用模糊网络分析法(FANP)进行计算分析,对以制造业为主导的供应链配送中心进行评价和选择,不仅考虑了指标的层次关系,也考虑到各层中因素之间的相互关系,解决了对具有网络关系的混合型结构评价指标体系的评价选择问题,克服了层次分析法(AHP)等传统算法的局限,在实际应用中具有很好的效果.     

A simulation analysis for evaluating TFT-LCD fab capacity expansion with a distant transportation problem

Capacity planning for large-scale high-tech manufacturing processes such as semiconductor manufacturing and thin film transistor-liquid crystal display (TFT-LCD) using simulation of an entire fabrication facility (fab) requires a large computational effort and thus few studies have been in real settings. To address the needs of a realistic problem, this study aimed to develop an effective approach based on a discrete-event simulation model for evaluating the throughput, cycle time and utilisation in an integrated fab to integrate manufacturing and transportation resources. In particular, we conducted an empirical study in a real TFT-LCD fab expansion facing a difficult capacity planning problem arising from the expectation that one or more bottlenecks may shift to different sites, including the transportation system between the incumbent and the expansion fabs. Different product-mix alternatives and feeding policies are investigated to determine the best fab configuration. The results have shown practical viability of the proposed simulation technique to significantly reduce the computational effort associated with the capacity planning process and derive useful alternatives for supporting capacity expansion decisions.     

Developing a resilient supply chain through supplier flexibility and reliability assessment

In this study, we examine the optimal allocation of demand across a set of suppliers in a supply chain that is exposed to supply risk and environmental risk. A two-stage mixed-integer programming model is used to develop a flexible sourcing strategy under disruptions. Our model integrates supplier selection and demand allocation with transportation channel selection and provides contingency plans to mitigate the negative impacts of disruptions and minimise total network costs. Finally, a numerical example is presented to illustrate the model and provide insights. The findings suggest that developing contingency plans using flexibility in suppliers’ production capacity is an effective strategy for firms to mitigate the severity of disruptions. We also show that flexibility and reliability of the suppliers and regions play a significant role in determining contingency plans for during disruption. Findings generally show that highly flexible suppliers receive less allocation, and their flexible capacity is reserved for disruptions. For firms that do not incorporate risk management into supplier selection and allocation, the recommendation is to source from fewer, more reliable suppliers with less risk of disruption. Our findings also emphasise that the type of disruption has important implications for supplier selection and demand allocation. This study highlights the supply chain risk management strategy of regionalising as a means for minimising the impact of environmental disruptions.     

A practical fab design procedure for wafer fabrication plants

Facility design is crucial to the performance of wafer fabs in the semiconductor industry. This research proposes a practical Fab Design Procedure (FDP) to conduct quick calculations to develop and evaluate initial fab design alternatives. A series of practical formulae are presented to sequentially determine the following design parameters from an interbay point of view: the required number of machines; machine grouping and allocation; interbay flow matrix; bay dimension and location; interbay distance matrix; the required number of vehicles in an interbay material handling system; and the average wafer moving distance. Rules-of-thumb for wafer fab design on the basis of FDP are also suggested. A case study is used to demonstrate the effectiveness and efficiency of FDP. Results indicate that FDP is able to quickly calculate the required number of machines and related fab design parameters. With this tool, fab designers would be able to evaluate design alternatives and conduct what-if analysis in the initial phase of fab design.     

Fuzzy neural network based yield prediction model for semiconductor manufacturing system

Accurate die yield prediction is very useful for improving yield, decreasing cost and maintaining good relationships with customers in the semiconductor manufacturing industry. To improve prediction accuracy of die yield, a novel fuzzy neural networks based yield prediction model is proposed in which the impact factors of yield and critical electrical test parameters are considered simultaneously and are taken as independent variables. The mapping between these independent variables and yield is constructed in the fuzzy neural network (FNN). The lineal regression between FNN-based yield predicting output and actual yield demonstrates the effectiveness of the proposed approach by historical experimental data of semiconductor fabrication line in Shanghai. The comparison experiment verifies the proposed yield prediction method improves on three traditional yield prediction methods with respect to prediction accuracy.