Cycle time management during production ramp-up

Aggressive capacity ramp rate of a semiconductor wafer fabrication is vital for the commercial success of the enterprise. Basic requirements are short and stable production cycle times to timely qualify equipment and to provide acceptable yield. Therefore, in the ramp-up environment which is characterized by high variability and uncertainty, an adequate methodology is required to properly manage the conflict of short cycle times and fast throughput increase. This paper presents a methodology to manage cycle time by closely monitoring and limiting the work in process (WIP), by means of the so-called “WIP caps”. Used consequently, this methodology allows the ramp rate to accelerate as soon as the factory performance enables this while keeping cycle times under control.     

A two-phase dynamic dispatching approach to semiconductor wafer testing

There have been numerous advancements and rising competition in semiconductor technologies. In light of this, the wafer test plays a more significant role in providing the most prompt yield feedback for quick process improvement. However, the wafer test shop floor is getting more complicated than ever before because of the increasing change-over rate, nonlinear wafer arrival, and preemption by urgent orders. Furthermore, the foundry wafer test is a heterogeneous production with different production cycle times and a large variety of nonidentical testers. Shop floor conditions, including work in process (WIP) pool, tester status, and work order priority, continuously change. There is a need to operate the kind of production line that simultaneously fulfills multiple objectives. Such objectives are maximum confirmed line item performance (CLIP) for normal lots, 100% CLIP for urgent lots, minimum change-over rate, and shortest cycle time. Thus, a reactive dispatching approach is proposed and expected to perform a real-time solution no matter how/what the shop floor would change. The dynamic approach is mainly triggered by two kinds of major events: one is when an urgent lot comes in, and the other is when a tester is idle. In addition, through a two-phase dispatching algorithm, lot ranking, and lot assignment methods, prioritized WIP lots and an appropriate lot assignment are suggested. A better performance measure is obtained by considering the multiple objectives the wafer test operations seek to achieve.     

Use of simulation in the analysis of shop floor operations

The use of computers in actual system applications is increasing with the availability of intelligent terminals on the shop floor. These terminals can be used by management as tools in the decision making process of planning shop floor operation. This paper discusses a pilot simulation study in the use of conventional Fortran-based simulation programs by shop floor management to:

1. 1. Participate in the evaluation of proposed FMS systems,

2. 2. Assess the impact of FMS acquisition on existing facilities,

3. 3. Assist in the identification of operational alternatives in “bottle neck” situations.

The pilot study employs a batch-oriented MRP system to provide daily updates of outstanding production center loadings on a monthly planning horizon. Two intelligent terminals are used to access a mini computer facility that executes the simulation models. The terminals have AT-compatible capabilities and are also used as data acquisition devices that support the numerically controlled operations within each work center.

The simulation models represent the 13 work centers of the firm and provide information about the average utilization of each work center, the number of parts in each queue and the average delay of parts in the queues. Future extensions of the models are planned to utilize the terminals' graphic animation capabilities to display the flow of production orders through the manufacturing facility.     

Evolving CBR and data segmentation by SOM for flow time prediction in semiconductor manufacturing factory

Flow time of semiconductor manufacturing factory is highly related to the shop floor status; however, the processes are highly complicated and involve more than 100 production steps. Therefore, a simulation model with the production process of a real wafer fab located in Hsin-Chu Science-based Park of Taiwan is built for further studying of the relationship between the flow time and the various input variables. In this research, a hybrid approach by combining Self-Organizing Map (SOM) and Case-Based Reasoning (CBR) for flow time prediction in semiconductor manufacturing factory is developed. And Genetic Algorithm (GA) is applied to fine-tune the weights of features in the CBR model. The flow time and related shop floor status are collected and fed into the SOM for clustering. Then, a corresponding SGA-CBR method is selected and applied for flow time prediction. Finally, using the simulated data, the effectiveness of the proposed method (SGA-CBR) is shown by comparing with other approaches.     

An efficient approach to cross-fab route planning for wafer manufacturing

This paper proposes an efficient approach to solve a cross-fab route planning problem for semiconductor wafer manufacturing. A semiconductor company usually adopts a dual-fab strategy. Two fab sites are built neighbor to each other to facilitate capacity-sharing. A product thus may be produced by a cross-fab route; that is, some operations of a product are manufactured in one fab and the other operations in the other fab. This leads to a cross-fab routing planning problem, which involves two decisions—determining the cut-off point of the cross-fab route and the route ratio for each product—in order to maximize the throughput subject a cycle time constraint. A prior study has proposed a method to solve the cross-fab route planning problem; yet it is computationally extensive in solving large scale cases. To alleviate this deficiency, we proposed three enhanced methods. Experiment results show that the best enhanced method could significantly reduce the computational efforts from about 13 h to 0.5 h, while obtaining a satisfactory solution.     

A machine learning approach to predict production time using real-time RFID data in industrialized building construction

Industrialized building construction is an approach that integrates manufacturing techniques into construction projects to achieve improved quality, shortened project duration, and enhanced schedule predictability. Time savings result from concurrently carrying out factory operations and site preparation activities. In an industrialized building construction factory, the accurate prediction of production cycle time is crucial to reap the advantage of improved schedule predictability leading to enhanced production planning and control. With the large amount of data being generated as part of the daily operations within such a factory, the present study proposes a machine learning approach to accurately estimate production time using (1) the physical characteristics of building components, (2) the real-time tracking data gathered using a radio frequency identification system, and (3) a set of engineered features constructed to capture the real-time loading conditions of the job shop. The results show a mean absolute percentage error and correlation coefficient of 11% and 0.80, respectively, between the actual and predicted values when using random forest models. The results confirm the significant effects of including shop utilization features in model training and suggest that predicting production time can be reasonably achieved.     

A strategic decision support system for supply network design and management in the semiconductor industry

This paper presents a strategic decision support system (DSS) which has been conceptualized and designed by SEMATECH^* to assist the large semiconductor manufacturing organization in managing its extensive supply chain network. This DSS has been named “Manufacturing Enterprise Model” or “MEM”. MEM ties each factory and its primary metrics to the rest of the business enterprise to assess how changes in wafer fabrication affect other factories, the distribution system, and customer deliveries. The model is intended to be used to evaluate future factory concepts and to assist business planners in strategic decisions about product allocation and major resource/facility planning.     

相关工件车间调度问题的拓扑算法

针对加工装配型离散制造企业实际生产的特点,提出了一类用于表示工序之间偏序关系的相关工件车间调度问题。为了利用已有的求解表示工序之间的线序关系的传统车间调度算法求解相关工件车间调度问题,设计了一种拓扑算法,该算法能够将工序之间的偏序关系转化为线序关系,将相关工件车间调度问题转化为传统的车间调度问题,通过实证研究,结果表明了拓扑算法是可行和高效的。     

ASP: An Adaptive Setup Planning Approach for Dynamic Machine Assignments

This paper presents a decision-making approach towards adaptive setup planning that considers both the availability and capability of machines on a shop floor. It loosely integrates scheduling functions at the setup planning stage, and utilizes a two-step decision-making strategy for generating machine-neutral and machine-specific setup plans at each stage. The objective of the research is to enable adaptive setup planning for dynamic job shop machining operations. Particularly, this paper covers basic concepts and algorithms for one-time generic setup planning, and run-time final setup merging for dynamic machine assignments. The decision-making algorithms validation is further demonstrated through a case study. Note to Practitioners-With increased product diversification, companies must be able to profitably produce in small quantities and make frequent product changeovers. This leads to dynamic job shop operations that require a growing number of setups in a machine shop. Moreover, today's customer-driven market and just-in-time production demand for rapid and adaptive decision making capability to deal with dynamic changes in the job shop environment. Within the context, how to come up with effective and efficient setup plans where machine availability and capability change over time is crucial for engineers. The adaptive setup planning approach presented in this paper is expected to largely enhance the dynamism of fluctuating job shop operations through adaptive yet rapid decision makings.     

Artificial intelligence-based sampling planning system for dynamic manufacturing process

The dynamic sampling instead of static sampling can more effectively utilize the inspection capacity for quicker excursion detection and increase the throughput of inspection machines without affecting the quality of inspection, so that achieve cycle time reduction. Accordingly, many researchers and semiconductor fabs start investigating the impacts of using dynamic sampling and there is currently a growing need for the dynamic sampling strategies in today's highly competitive semiconductor industry. Meanwhile, the use of artificial intelligence (AI) for knowledge discovery has become more common in industrial and manufacturing process control systems and recent advances in technology, particularly in networking, and information processing, have made the implementation of dynamic process sampling feasible. In this paper the optimal dynamic sampling method and the associated decision process based on AI technique are proposed and the effectiveness of them is validated through actual data sets collected from a semiconductor fabrication line. Finally, we present an AI-based dynamic sampling planning system incorporated the proposed methodology, which possesses four sub-components: wafer bin map (WBM) data mart, optimal sampling method generator (OSMG), sampling knowledge, and sampling adaptation monitor. Our research results provide an effective solution to implement a successful dynamic process sampling.     

Discovering useful and understandable patterns in manufacturing data

Accurate planning of produced quantities is a challenging task in semiconductor industry where the percentage of good parts (measured by yield) is affected by multiple factors. However, conventional data mining methods that are designed and tuned on “well-behaved” data tend to produce a large number of complex and hardly useful patterns when applied to manufacturing databases. This paper presents a novel, perception-based method, called Automated Perceptions Network (APN), for automated construction of compact and interpretable models from highly noisy data sets. We evaluate the method on yield data of two semiconductor products and describe possible directions for the future use of automated perceptions in data mining and knowledge discovery.     

基于遗传算法的半导体生产线调度方法研究

在介绍遗传算法及其在流水车间调度(flow shop)应用现状的基础上,针对半导体生产线可重入的特性,研究了基于遗传算法的半导体生产线调度算法,并用一个例子给出了算法的计算过程。     

Shop floor control in computer-integrated manufacturing for large-scale integrated wafer fabrication systems

Manufacturing technology in the field of Large-Scale-Integrated (LSI) circuit production has overcome the problems of enlarged wafer size and increasing level of integration. The market environment of the LSI industry has become more demanding with increased requirements, delivery time and stiffer cost competition. A Computer-Integrated Manufacturing System (CIMS) well-adapted to the unique characteristics of LSI production system is thus urgently needed.

This paper describes the development of CIMS in LSI production systems, and presents the example of a DTS (Diffusion Total System), constructed in a real LSI wafer fabrication factory. Features of DTS are (a) on-line real-time manufacturing data acquisition and monitoring through a production information network, (b) automatic control of process equipment, (c) shop floor control for computer aided decision support at the shop floor level, and (d) data analysis and reporting derived from a large amount of accumulated data. This paper deals with shop floor control, and in particular with a priority control system unique to the LSI wafer fabrication line.     

A multi-criteria approach for scheduling semiconductor wafer fabrication facilities

In this research, we model a semiconductor wafer fabrication process as a complex job shop, and adapt a Modified Shifting Bottleneck Heuristic (MSBH) to facilitate the multi-criteria optimization of makespan, cycle time, and total weighted tardiness using a desirability function. The desirability function is implemented at two different levels of the MSBH: the subproblem solution procedure level (SSP level) and the machine criticality measure level (MCM level). In addition, we suggest two different methods of choosing the critical toolgroup at the MCM level: (1) the Local MCM approach, which chooses the critical toolgroup based on local desirability values from the SSP level and (2) the Global MCM approach, which chooses the critical toolgroup based on its impact on the desirability of the entire disjunctive graph. Results demonstrate the desirability-based approaches’ ability to simultaneously minimize all three objectives.     

Architectural requirements for rapid development of agile manufacturing systems

As product life cycle becomes shortened, high product quality becomes necessary for survival, and continuous and unexpected change becomes key obstacles in success, the need for a method of rapidly and cost-effectively developing products, production facilities and supporting software including design, process planning, shop floor control systems becomes urgent. The essence of this concept of manufacturing would be characterized by adopting a new term “agility”. Agile manufacturing can be successfully accomplished using various well-defined system architecture. This paper provides a primary sketch of architectural requirements for rapid development of agile manufacturing systems.There are several aspects of system architecture : control, function, process, information, communication, distribution, development, and implementation.In the past, the confusion of those architectures prohibited the successful construction of the automated CIM systems.     

A fuzzy mid-term single-fab production planning model

Production planning is a complicated task for a semiconductor fabrication plant because of the uncertainties in demand, product prices, cycle times, and product yields. Traditionally, mid-term production planning for a semiconductor fabrication plant is handled with MRP systems or optimized by solving LP or FLP problems. In this study, the philosophy of prioritizing demand classes with higher certainties as proposed by Leachman (1993) is applied to the FLP model of Chen and Wang (1998), and a new FLP model for planning the mid-term production of single wafer fabrication plant is constructed. Parameters in this model are given in the form of trapezoidal fuzzy numbers. Fuzzy comparison is adopted in dealing with the fuzzy objective function and expanding inequalities. The outputs are projected using Chen and Wang's fuzzy dynamic production function. The uncertain demand is classified and satisfied with four successively optimized FLP submodels according to their ascending uncertainties. Chen and Wang's example is adopted to demonstrate the proposed methodology and to make some comparisons. By moving more capacity to demand classes with higher certainties that are usually nearer and have larger discounted revenues, the proposed methodology achieves a higher value of the discounted cash flows than the two referenced models.     

Permitting updates through views of data bases

Providing different views (logical images of the structure of a data base) to various users creates the problem of determining how update operations expressed in terms of the views should affect the stored form of the data base. For data bases with a relational organization, we indicate the effects of a wide range of update operations on views. We conclude that some operations must be prohibited in order to assure harmonious interactions among data base users, but that many other operations can be allowed even though the structure of the view may differ substantially from the actual structure of the data base. We consider views not only as “windows” through which to see a data base in a particular way, but also as “shades” to conceal and protect information, and as “screens” to intercept any update operations that could leave the stored form of the data base in an unacceptable state.     

Effect of rework strategies on cycle time

In batch manufacturing, a fraction of the batch, or “lot,” may require reworking because its members fail to conform to standards. A rework station “undoes” the previous operation, so that the nonconforming members can go through the same operation additional times. This paper explores how policies dealing with these nonconforming members affect the cycle time of a facility.

Two different operating policies can be followed. In one, the “mother” lot is held back while the “child” sub-lots are reworked, after which all members are reunited for the next operation. In the other, the mother lot is allowed to proceed to the next operation while the child is held back; reworked members are then introduced by one of three methods: In the first, the reworked members of each mother lot are introduced by themselves. In the second, a minimum order quantity of reworked members is designated and a new lot introduced when this level is reached. In the third, the reworked members are added to the next mother lot that visits the operation.

In this paper, queuing models are developed for these policies, and the policies are simulated with regard to cycle time. Simulation of a wafer fabrication model is used to determine the effectiveness of these policies and their impact on cycle time.     

Run-to-run control and state estimation in high-mix semiconductor manufacturing

In the modeling and control of semiconductor manufacturing, the control engineer must be aware of all influences on the performance of each process. Upstream processes may affect the wafer substrate in a manner that alters performance in downstream operations, and the context within which a process is run may fundamentally change the way the process behaves. Incorporating these influences into a control method ultimately leads to better predictability and improved control performance. Control threads are a way of incorporating these effects into the control of a process by partitioning historical data into groups within which the deterministic sources of variation are uniform. However, if there are many products, which require many threads to be defined, there may be insufficient data to model each thread. This multi-product–multi-tool manufacturing environment (“high-mix”) requires advanced methodologies based on state estimation and recursive least squares. Several such approaches are compared in this paper based on simulation models for a high-mix fab.     

The process planner''s work place today and tomorrow

Process planning (PP) is perhaps the most difficult task in the area of production planning. It is the first task to be performed after the design phase and it establishes the necessary link between CAD and the manufacturing. The main purpose of PP is to select manufacturing processes and operations, machine tools, and clamping devices for optimal production. Needs for quality planning and design of special tools are also often initiated from the PP phase. PP has to be carried out with respect to available resources and the technological “know how” of the company and is, therefore, dependent on very skilled planners. Very good sw-systems exist.8