Dynamic capacity modeling in semiconductor assembly manufacturing

It is very challenging to gain a competitive advantage in semiconductor assembly manufacturing, mainly due to widely and quickly fluctuating customer requests, complicated workflows, and the co-existence of new and aging technology/equipment. In late 2003, Intel Shanghai experienced capacity degradation, mainly due to the introduction of multiple-chip products with re-entrant workflows. In response, we applied the joint protective capacity methodology to semiconductor assembly manufacturing and developed a dynamic capacity model that captures the variability of re-entrant manufacturing systems. Protective capacity and other parameters at constrained and near-constrained stations are regularly tracked and adjusted to accurately reflect line execution variability in terms of equipment, staff, and work-in-process. With the implementation and continuous improvement of these methodologies, we increased the factory output by 200% without major capital investment (avoiding millions of dollars in capital spending), cut unit cost by 30%, and brought down subcontractor costs by 26%, which resulted in cash savings of over ten million dollars for Intel.     

A decomposition of productivity change in the semiconductor manufacturing industry

This study divides a production system into three components: production design, demand support, and operations. Efficiency is then decomposed via network data envelopment analysis and integrated into the Malmquist Productivity Index framework to develop a more detailed decomposition of productivity change. The proposed model can identify the demand effect and the identity of the root cause of technical regress. Specifically, the demand effect allows the source of technical regress to be attributed to both demand deterioration and technical regress in the production technology. An empirical study using data from 1995 to 2000 for the semiconductor manufacturing industry is presented to demonstrate and validate the proposed method. The result shows that the regress of productivity in 1997–1998 and 1999–2000 is mainly caused by demand fluctuations rather than by technical regression in production capabilities.     

Optimal control of a continuum model for single-product re-entrant manufacturing systems

A semiconductor manufacturing system that involves a large number of items and many steps can be modelled through conservation laws for a continuous density variable on a production process. In this paper, the basic hyperbolic partial differential equation (PDE) models for multiple re-entrant manufacturing systems are proposed. However, through numerical examples, the basic continuum models do not perform well for small-scale multiple re-entrant systems, so a new state equation taking into account the re-entrant degree of the product is introduced to improve the basic continuum models. The applicability of the modified continuum model is illustrated through a numerical example. Based on the modified continuous model, this paper studies the optimal control problems for multiple re-entrant manufacturing systems. The gradient of the cost function with respect to the influx is solved by the adjoint approach, and then the optimal influx is computed by the steepest descent method. Finally, numerical examples on optimal influx profiles for steps in demand rate, linear demand rate and periodically varying demand rate are given. The relationships among influx, outflux and demand are also discussed in detail.     

Configuration design in scalable reconfigurable manufacturing systems (RMS); a case of single-product flow line (SPFL)

The dynamic nature of today’s manufacturing industry, which is caused by the intense global competition and constant technological advancements, requires systems that are highly adaptive and responsive to demand fluctuations. Reconfigurable manufacturing systems (RMS) enable such responsiveness through their main characteristics. This paper addresses the problem of RMS configuration design, where the demand of a single product varies throughout its production life cycle, and the system configuration must change accordingly to satisfy the required demand with minimum cost. A two-phased method is developed to handle the primary system configuration design and the necessary system reconfigurations according to demand rate changes. This method takes advantage of Reconfigurable Machine Tools in RMS. In fact, by adding/removing modules to/from a specific modular reconfigurable machine, its production capability could be increased, with lower cost. A novel mixed integer linear programming formulation is presented in the second phase of the method to optimise the process of selecting the best possible transformation for the existing machine configurations. Two different cases are designed and solved by implementing the established method. The results of these cases in terms of capital cost, capacity expansion cost, unused capacity and number of transformations, are compared with two hypothetical scenarios. Analyses of the obtained results indicate the efficiency of the proposed approach and offer a promising outlook for further research.     

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.     

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.     

System reliability of a manufacturing network with reworking action and different failure rates

From the perspective of network analysis, the manufacturing system can be constructed as a stochastic-flow network, since the capacity of each machine is stochastic (i.e. multistate) owing to the failure, partial failure, and maintenance. Considering reworking action and different failure rates of machines, the input flow (raw materials/work in process) processed by each machine might be defective, and therefore the output flow (work in process/products) would be less than the input amount. To evaluate the capability of the manufacturing system, we measure the probability that the manufacturing network can satisfy demand. Such a probability is defined as the system reliability. A decomposition method is first proposed to divide the manufacturing network into one general processing path and one reworking path. Subsequently, two algorithms are utilised for different network models to generate the lower boundary vector of machine capacity to guarantee that the manufacturing network is able to produce sufficient products fulfilling the demand. The system reliability of the manufacturing network can be derived in terms of such a capacity vector afterwards.     

Managing production level in new product diffusion: an agent-based simulation approach

Managing production level after the launch of a new product is a challenging problem and is critical to the overall profit of manufacturing firms. The problem involves concepts from different fields including production planning, manufacturing flexibility, forecasting, and marketing. In this paper, the gaps in the existing literature are first illustrated. With the goal of addressing the identified research gaps, agent-based modeling and simulation is employed to analyze the performance of different production planning strategies under various levels of volume flexibility and consumer social network structures. The key distinguishing feature of the developed model is the capability of the manufacturing firm to adjust its production level by forecasting the future demand. The analysis of the simulation outputs yields substantial results by challenging some intuitive and traditional understandings of manufacturing systems. The paper also provides a discussion on managerial implications of the results in order to provide the managers with guidelines on the implementation of the model in real-world diffusion environments.     

A review on strategic capacity planning for the semiconductor manufacturing industry

Due to the high capital investment cost in the semiconductor manufacturing industry, strategic capacity planning plays an important role in improving business performance. The reasons that make these problems especially difficult are: the complex fabrication process, rapid changes in technology and products, the long lead time and the high cost of capacity increment, and the high uncertain demand and capacity. Hence, a review of the existing solutions to capacity planning to cope with these difficulties is important for developing new methodologies and practices. In this paper, we provide a review of the current research and fundamental methods in strategic capacity planning, identify emerging methods for capacity planning, and take an in-depth look at future research interests.     

Operational tactics and tenets of a new manufacturing paradigm ‘instant customerisation’

An increasing trend towards a new manufacturing paradigm of ‘instant customerisation’ is occurring due to customers demanding their orders for customised products or services be fulfilled under zero customer lead time and at a price near the price of mass production. By means of the manufacturing paradigm innovation model (MPIM), the operational tactics, operational tenets, and an operational framework of instant customerisation are elicited and described. An important new tactic within all the operational tactics is finalise-to-individual demand forecasting, meaning end products are finalised according to the forecasting of individual customer's needs. The knowledge and information, which is the primary input of individual demand forecasting, is gleaned and understood by total customer participation and value fusion. Total customer participation and value fusion are developed through customer participation and customer relationship management, which have critical distinctions. The operational framework, which is termed manufacturing paradigm tree of instant customerisation, can characterise the operational tenets, the relationships between different operational tactics and the objectives, and the relationships among different operational tactics. With the proposed framework, instant customerisation can be understood more effectively and holistically.     

A new method for guiding process flexibility investment: flexibility fit index

Flexibility enables manufacturing firms to respond efficiently to changes in the environment. Many firms make great efforts to increase their manufacturing flexibility to remain competitive in today's turbulent market. However, it is not true to say that the more flexible the better, because the cost of flexibility investment is high, and the capital for flexibility investment is limited. In this paper, we present a new method to guide process flexibility investment by developing a flexibility fit index. Taking demand changes into account, our method first defines a measure to quantify the requirement level of process flexibility. Then, a flexibility fit index is defined, which specifically identifies where flexibility is insufficient and where flexibility is surplus for a manufacturing system operating in a changing environment. The proposed fit index is objective and dimensionless, and so can be used more universally than previous subjective or non-dimensionless measures proposed in the literature. A set of simulation experiments shows that the proposed method can better guide flexibility investment by indicating the system structure which fits best with a given business situation, and other measures that ignore demand information may lead to over-investment or adding links that bring little benefit.     

Optimising resource portfolio planning for capital-intensive industries under process-technology progress

This paper addresses the problem of resource portfolio planning of firms in high-tech, capital-intensive manufacturing industries. In light of the strategic importance of resource portfolio planning in these industries, we offer an alternative approach to modelling capacity planning and allocation problems that improves the deficiencies of prior models in dealing with three salient features of these industries, i.e. fast technological obsolescence, volatile market demand, and high capital expenditure. This paper first discusses the characteristics of resource portfolio planning problems including capacity adjustment and allocation. Next, we propose a new mathematical programming formulation that simultaneously optimises capacity planning and task assignment. For solution efficiency, a constraint-satisfied genetic algorithm (CSGA) is developed to solve the proposed mathematical programming problem on a real-time basis. The proposed modelling scheme is employed in the context of a semiconductor testing facility. Experimental results show that our approach can solve the resource portfolio planning problem more efficiently than a conventional optimisation solver. The overall contribution is an analytical tool that can be employed by decision makers responding to the dynamic technological progress and new product introduction at the strategic resource planning level.     

Performance evaluation for a footwear manufacturing system with multiple production lines and different station failure rates

This paper develops a three-phase procedure to measure the performance of a highly value-added footwear manufacturing system taking reworking actions into account, in which the system consists of multiple production lines. We mainly address the system reliability as a performance indicator to evaluate the possibility of demand satisfaction. First, we construct the manufacturing system as a manufacturing network by graphical transformation and decomposition. Second, capability analysis is implemented to determine the input flow of each station based on the manufacturing network. Third, a simple algorithm is proposed to generate all minimal capacity vectors that stations should provide to satisfy the given demand. We evaluate the system reliability in terms of the minimal capacity vectors. A further decision making issue is discussed to decide a reliable production strategy. Whenever the system state changes, the proposed performance evaluation procedure can be implemented easily and flexibly.     

Allocating metrology capacity to multiple heterogeneous machines

The measurement of lots to check process quality is crucial but also a non-added value operation in manufacturing systems. This paper is motivated by semiconductor manufacturing, where metrology tools are expensive, thus limiting metrology capacity which must be optimally used. In a context where multiple heterogeneous machines are sharing a common metrology workshop, the problem of minimising risk while considering metrology capacity arises. An integer linear programming (ILP) model is presented, which corresponds to a multiple-choice knapsack problem. Simple rounding heuristics are proposed, whose results on randomly generated instances are compared with the optimal solutions obtained using a standard solver on the ILP. Additionally, numerical experiments on industrial data are presented and discussed.     

Mitigating forecast errors by lot-sizing rules in ERP-controlled manufacturing systems

As enterprise resource planning (ERP) becomes the dominant management software in manufacturing and distribution systems in various industries, some problems associated with its origin, material requirements planning (MRP), still need to be resolved. We examine the effect of forecasting errors, one of the common operational problems in any business operation, in the context of an ERP-controlled manufacturing system. We consider a mitigating remedy, the use of a lot-sizing rule, to cope with the consequences of forecasting inaccuracy without resorting to costly inventory-oriented buffers. An ERP-controlled manufacturing system is simulated to see how these lot-sizing rules mitigate the forecast errors and subsequently generate acceptable system performance. The simulation results should help ease ERP users’ fear of committing another fatal error in demand forecasts, instead encouraging them to consider proper lot-sizing rules to cope with forecast errors.     

提升台湾IC制造业竞争力之策略选择

首先介绍台湾IC产业之发展状况,然后进一步分析IC制造之竞争力,接着根据制造策略相关文献及业界专家意见访查,找出提升台湾IC制造竞争力之策略：设备利用率、先进制造技术、供货商关系、品质管制、人力资源,这些策略造就了台湾IC制造之竞争优势,也提供半导体制造管理之新典范。     

云制造中产品个性化定制研究综述

消费者对个性化产品购买需求的增加,对制造企业的定制生产能力提出了越来越高的要求。而现有制造企业存在的信息化水平不高、制造资源不足等问题,严重影响了制造企业向个性化生产转型的进程。针对上述情况,结合云制造概念及技术提出了云制造中产品个性化定制生产模式。为了更加深入地研究云制造中产品个性化定制的运行模式及阐述云制造平台在定制生产过程中的主要作用,从体系架构、服务流程、关键技术等方面对相关研究进行了回顾和介绍,然后列举了个性化定制生产模式在汽车、软件和服装行业的应用,最后对研究趋势进行了展望。分析表明,云制造平台汇聚全球的制造资源为个性化定制服务,能够有效解决单一制造企业实施个性化定制过程中面临的一系列困难,做到以较低的成本快速高效地满足消费者日益多样化、个性化的需求。     

Book reviews

In many current semiconductor manufacturing operations, headcount is manually allocated periodically based on man-machine ratio. Attributed to non-optimised allocation of direct labour to operations/machines, considerable productivity loss occurs. The problem is further complicated by some dynamic and uncertain factors such as constantly changing production targets and work in progress, overlapped labour skills, and variability in manufacturing operations and labour absenteeism rates. Motivated by the needs in real practice, this study aims to develop a model for allocating a direct workforce among semiconductor manufacturing operations to meet production targets and maximise labour productivity. This paper presents a two-stage goal programming model for the headcount allocation problem. To enhance the model's pragmatic use, a queueing module is introduced to account for the interferences among the multi-machine operations. Computational experiments are carried out to evaluate the performances of the proposed algorithms and pilot runs are implemented in a factory. Finally, a prototype system is developed and has been proved to be useful in practice.     

Efficient development of cycle time response surfaces using progressive simulation metamodeling

In semiconductor manufacturing, hot lots are to provide marketing and engineering with extra flexibility regarding delivery lead times, and in turn enhance its competitive advantages against other companies. On the other hand, hot lots are among major sources of disruption of the smoothness of the manufacturing flow. They can lead to a significant increase of cycle time of normal lots, and in turn result in delayed delivery times and serious service deteriorations. Due to the complex nature of semiconductor manufacturing, evaluating the impact of hot lots on the cycle time of normal lots presents major challenges. In this paper, we propose a methodology, called progressive simulation metamodelling (PSM), that allows for an efficient development of the response surface between the cycle time of normal lots and the percentage of hot lots in semiconductor manufacturing. The response surface generated by the proposed PSM is like an easy-to-use analytical model, but with the fidelity of simulation that takes into account all important manufacturing details. The specially-designed mechanisms, including identifying the critical region and sequentially adding design points in the critical region, further grants PSM computational advantages compared to the traditional response surface method. An empirical study conducted in collaboration with a semiconductor company validates the viability of PSM in real settings.     

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.