34 research outputs found

    Cycle Time Analysis For Photolithography Tools In Semiconductor Manufacturing Industry With Simulation Model : A Case Study [TR940. S618 2008 f rb].

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    Perkembangan industri semikonduktor dalam bidang fabrikasi biasanya melibatkan kos pelaburan yang tinggi terutamanya dalam alatan photolithography. The industry of semiconductor wafer fabrication (“fab”) has invested a huge amount of capital on the manufacturing equipments particular in photolithograph

    Cycle Time Analysis For Photolithography Tools In Semiconductor Manufacturing Industry With Simulation Model: A Case Study

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    Perkembangan industri semikonduktor dalam bidang fabrikasi biasanya melibatkan kos pelaburan yang tinggi terutamanya dalam alatan photolithography. Perkembangan pesat dalam bidang industri semikonduktor kini telah memerangsangkan teknik untuk mengoptimumkan penggunaan mesin-mesin dengan efektif setelah membelanjakan beribu juta dalam perlaburan. Tanpa penggunaan perisian komputer yang canggih dalam analisis, adalah sukar untuk menggunakan teknik purba dalam analisis pengiraan apabila menghadapi perkembangan produk yang semakin tinggi teknologinya. Dalam kajian ini, satu model simulasi telah dibina untuk menganalisis masa mendulu dalam alatan photolithography melalui teknik yang lebih sistematik dan efektif. Model simulasi ini telah dibina berasaskan perisian computer yang memerlukan informasi yang teliti seperti mas a memproses dan juga aliran proses dalam alatan photolithography. The industry of semiconductor wafer fabrication ("fab") has invested a huge amount of capital on the manufacturing equipments particular in photolithography area which has driven the needs to re-look at the most profitable way of utilizing and operating them efficiently. Traditional industrial engineering analysis techniques through mathematical models or static models for the studies of photolithography process are simply not adequate to analyze these complex environments. In this research, a more realistic representation of photolithography tools that can give a better prediction results and a more systematic methodology for minimizing photolithography cycle time is presented. The proposed method is to reduce waiting time and increase utilization of the photolithography process, which would result in an overall equipment cycle time reduction

    A Simulation study of dispatching rules and rework strategies in semiconductor manufacturing

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    The semiconductor industry is fast paced and on the cutting edge of technology, resulting in very short life spans of semiconductor products. In order to stay competitive, manufacturers must be able to quickly adapt to produce new products, and they must achieve a high level of productivity. Two major operational components of semiconductor fabrication plants (fabs) that effect productivity are dispatching rules and rework strategies. Although prior research has been conducted independently on these two issues, the hypothesis is that the interrelationship between the dispatching rules and rework strategies has a significant effect on the productivity of the fab. Moreover, the goal is to determine which combination of widely-used dispatching rules and new and existing rework strategies results in the highest level of fab productivity. To test this hypothesis, the significance of rework is evalutated, and a four-factor experiment is conducted to determine the effect of dispatching rules, rework strategies, fab types, and rework levels on key fab performance measures. Five dispatching rules are combined with three previously studied rework strategies and the first bottleneck strategy which is developed in this study. The treatment combinations are compared based on fab performance measures including cycle time, percentage on time, work-in-process, and the XTheoretical value. Simulation models based on actual fab data are constructed to carry out the experiments. The detailed results of the experiment show that combinations of dispatching rules and rework strategies have a significant impact on fab performance measures at each rework level in both fab types. In general, two dispatching rules, rework priority and first-in-first-out, in combination with the first bottleneck rework strategy perform the best. Further analysis concludes that the rework priority dispatching rule and the first bottleneck rework strategy result in the highest level of fab performance and are most robust over alterative fab configurations

    IMPROVED PHOTOLITHOGRAPHY SCHEDULING IN SEMICONDUCTOR MANUFACTURING

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    Photolithography is typically the bottleneck process in semiconductor manufacturing. In this thesis, we present a model for optimizing photolithography job scheduling in the presence of both individual and cluster tools. The combination of individual and cluster tools that process various layers or stages of the semiconductor manufacturing process flow is a special type of flexible flowshop. We seek separately to minimize total weighted completion time and maximize on-time delivery performance. Experimental results suggest that our mathematical- and heuristic-based solution approaches show promise for real world implementation as they can help to improve resource utilization, reduce job completion times, and decrease unnecessary delays in a wafer fab

    MODELING AND SIMULATION OF A SEMICONDUCTOR MANUFACTURING FAB FOR CYCLE TIME ANALYSIS

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    The goal of the thesis is to conduct a study of the effects of scheduling policies and machine failures on the manufacturing cycle time of the Integrated Circuit (IC) manufacturing process for two processor chips, namely Skylake and Kabylake, manufactured by Intel. The fab simulation model was developed as First in First Out (FIFO), Shortest Processing Time (SPT), Priority based (PB), and Failure FIFO (machine failures) model, and the average cycle times and queue waiting times under the four scheduling policy models were compared for both the Skylake and Kabylake wafers. The study revealed that scheduling policies SPT and PB increased the average cycle time for Skylake wafers while decreasing the average cycle time for the Kabylake wafers, when compared to the base FIFO model. Machine failures increased the average cycle time for both types of wafers

    Intelligent shop scheduling for semiconductor manufacturing

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    Semiconductor market sales have expanded massively to more than 200 billion dollars annually accompanied by increased pressure on the manufacturers to provide higher quality products at lower cost to remain competitive. Scheduling of semiconductor manufacturing is one of the keys to increasing productivity, however the complexity of manufacturing high capacity semiconductor devices and the cost considerations mean that it is impossible to experiment within the facility. There is an immense need for effective decision support models, characterizing and analyzing the manufacturing process, allowing the effect of changes in the production environment to be predicted in order to increase utilization and enhance system performance. Although many simulation models have been developed within semiconductor manufacturing very little research on the simulation of the photolithography process has been reported even though semiconductor manufacturers have recognized that the scheduling of photolithography is one of the most important and challenging tasks due to complex nature of the process. Traditional scheduling techniques and existing approaches show some benefits for solving small and medium sized, straightforward scheduling problems. However, they have had limited success in solving complex scheduling problems with stochastic elements in an economic timeframe. This thesis presents a new methodology combining advanced solution approaches such as simulation, artificial intelligence, system modeling and Taguchi methods, to schedule a photolithography toolset. A new structured approach was developed to effectively support building the simulation models. A single tool and complete toolset model were developed using this approach and shown to have less than 4% deviation from actual production values. The use of an intelligent scheduling agent for the toolset model shows an average of 15% improvement in simulated throughput time and is currently in use for scheduling the photolithography toolset in a manufacturing plant

    Online Simulation in Semiconductor Manufacturing

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    In semiconductor manufacturing discrete event simulation systems are quite established to support multiple planning decisions. During the recent years, the productivity is increasing by using simulation methods. The motivation for this thesis is to use online simulation not only for planning decisions, but also for a wide range of operational decisions. Therefore an integrated online simulation system for short term forecasting has been developed. The production environment is a mature high mix logic wafer fab. It has been selected because of its vast potential for performance improvement. In this thesis several aspects of online simulation will be addressed: The first aspect is the implementation of an online simulation system in semiconductor manufacturing. The general problem is to achieve a high speed, a high level of detail, and a high forecast accuracy. To resolve these problems, an online simulation system has been created. The simulation model has a high level of detail. It is created automatically from underling fab data. To create such a simulation model from fab data, additional problems related to the underlying data arise. The major parts are the data access, the data integration, and the data quality. These problems have been solved by using an integrated data model with several data extraction, data transformation, and data cleaning steps. The second aspect is related to the accuracy of online simulation. The overall problem is to increase the forecast horizon, increase the level of detail of the forecast and reduce the forecast error. To provide useful forecast results, the simulation model contains a high level of modeling details and a proper initialization. The influences on the forecast quality will be analyzed. The results show that the simulation forecast accuracy achieves good quality to predict future fab performance. The last aspect is to find ways to use simulation forecast results to improve the fab performance. Numerous applications have been identified. For each application a description is available. It contains the requirements of such a forecast, the decision variables, and background information. An application example shows, where a performance problem exists and how online simulation is able to resolve it. To further enhance the real time capability of online simulation, a major part is to investigate new ways to connect the simulation model with the wafer fab. For fab driven simulation, the simulation model and the real wafer fab run concurrently. The wafer fab provides several events to update the simulation during runtime. So the model is always synchronized with the real fab. It becomes possible to start a simulation run in real time. There is no further delay for data extraction, data transformation and model creation. A prototype for a single work center has been implemented to show the feasibility

    Analyzing Controllable Factors Influencing Cycle Time Distribution in Semiconductor Industries

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    abstract: Semiconductor manufacturing is one of the most complex manufacturing systems in today’s times. Since semiconductor industry is extremely consumer driven, market demands within this industry change rapidly. It is therefore very crucial for these industries to be able to predict cycle time very accurately in order to quote accurate delivery dates. Discrete Event Simulation (DES) models are often used to model these complex manufacturing systems in order to generate estimates of the cycle time distribution. However, building models and executing them consumes sufficient time and resources. The objective of this research is to determine the influence of input parameters on the cycle time distribution of a semiconductor or high volume electronics manufacturing system. This will help the decision makers to implement system changes to improve the predictability of their cycle time distribution without having to run simulation models. In order to understand how input parameters impact the cycle time, Design of Experiments (DOE) is performed. The response variables considered are the attributes of cycle time distribution which include the four moments and percentiles. The input to this DOE is the output from the simulation runs. Main effects, two-way and three-way interactions for these input variables are analyzed. The implications of these results to real world scenarios are explained which would help manufactures understand the effects of the interactions between the input factors on the estimates of cycle time distribution. The shape of the cycle time distributions is different for different types of systems. Also, DES requires substantial resources and time to run. In an effort to generalize the results obtained in semiconductor manufacturing analysis, a non- complex system is considered.Dissertation/ThesisMasters Thesis Mechanical Engineering 201

    Intelligent systems in manufacturing: current developments and future prospects

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    Global competition and rapidly changing customer requirements are demanding increasing changes in manufacturing environments. Enterprises are required to constantly redesign their products and continuously reconfigure their manufacturing systems. Traditional approaches to manufacturing systems do not fully satisfy this new situation. Many authors have proposed that artificial intelligence will bring the flexibility and efficiency needed by manufacturing systems. This paper is a review of artificial intelligence techniques used in manufacturing systems. The paper first defines the components of a simplified intelligent manufacturing systems (IMS), the different Artificial Intelligence (AI) techniques to be considered and then shows how these AI techniques are used for the components of IMS
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