Autocorrelation of cycle times in semiconductor manufacturing systems

Schömig, Alexander K.; Mittler, Manfred

doi:10.1145/224401.224744

Cited by 6 publications

(4 citation statements)

References 13 publications

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“…They later proposed an approximate method to analyze the effect of splitting and superposition of autocorrelation processes in queues (Balcıoglu et al, 2008). Some works have shown examples of autocorrelation in data from industrial plants (Luxhoj and Shyur, 1995;Melamed and Hill, 1995;Mertens et al 2009;Schomig and Mittler, 1995;Young and Winistorfer, 2001).…”

Section: Introductionmentioning

confidence: 99%

Autocorrelation effects in manufacturing systems performance: A simulation analysis

Pereira

Vilas

Monteil

et al. 2012

Proceedings Title: Proceedings of the 2012 Winter Simulation Conference (WSC)

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Autocorrelation has been pointed out as one of the most challenging issues in manufacturing systems modeling. Numerical experimentation has shown that it may either enhance or harm performance. Furthermore, there is not yet a general agreement in what a realistic autocorrelation model is or whether it is actually relevant for practical applications. This paper provides a simulation analysis of the effects on performance caused by manufacturing process parameters following autoregressive (AR) processes. AR time series are employed for modeling variations in parameters that happen at a time scale different from the corresponding to process cycle execution. Three basic configurations are analyzed: serial line, assembly process and a disassembly process. A case study from the natural slate tiles industry is presented showing the differences obtained in simulation results between a model in which independent and identically distributed (i.i.d.) assumptions are adopted and one in which autocorrelation effects are considered.

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Section: Introductionmentioning

confidence: 99%

Autocorrelation effects in manufacturing systems performance: A simulation analysis

Pereira

Vilas

Monteil

et al. 2012

Proceedings Title: Proceedings of the 2012 Winter Simulation Conference (WSC)

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show abstract

“…Second area is related to the existing MAP fitting methods. Schomig and Mittler (1995) analyze the cycle time of semiconductor manufacturing systems and show that the cycle time is highly correlated. Inman (1999) demonstrates the presence of autocorrelation in the output data of some manufacturing stations in automotive industry.…”

Section: Literature Surveymentioning

confidence: 99%

“…Empirical studies show that the inter-departure times of a production system demonstrate significant correlation (Schomig and Mittler 1995;Inman 1999). In addition, analysis of the output dynamics of production system with i.i.d.…”

Section: Introductionmentioning

confidence: 99%

Modelling and analysis of the impact of correlated inter-event data on production control using Markovian arrival processes

Dizbin

Tan

2018

Flex Serv Manuf J

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Empirical studies show that the inter-event times of a production system are correlated. However, most of the analytical studies for the analysis and control of production systems ignore correlation. In this study, we show that real-time data collected from a manufacturing system can be used to build a Markovian arrival processes (MAP) model that captures correlation in inter-event times. The obtained MAP model can then be used to control production in an effective way. We first present a comprehensive review on MAP modeling and MAP fitting methods applicable to manufacturing systems. Then we present results on the effectiveness of these fitting methods and discuss how the collected inter-event data can be used to represent the flow dynamics of a production system accurately. In order to study the impact of capturing the flow dynamics accurately on the performance of a production control system, we analyze a manufacturing system that is controlled by using a basestock policy. We study the impact of correlation in inter-event times on the optimal base-stock level of the system numerically by employing the structural properties of the MAP. We show that ignoring correlated arrival or service process can lead to overestimation of the optimal base-stock level for negatively correlated processes, and underestimation for the positively correlated processes. We conclude that MAPs can be used to develop data-driven models and control manufacturing systems more effectively by using shop-floor inter-event data.

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“…Most of these methods assume independent and identically distributed (i.i.d) interarrival and service times, first-come first-serve discipline (FCFS) and consider a single server per block (Kuehn 1979, Dallery and Gershwin 1992, Buzacott and Shanthikumar 1993, Hopp and Spearman 2011. Empirical and analytical studies have indicated that these assumptions do not hold in many production systems, especially in complex systems such as semiconductor manufacturing plants (Schömig and Mittler 1995, Tan and Lagershausen 2017, Manafzadeh Dizbin 2020.…”

Section: Introductionmentioning

confidence: 99%

Supervised-learning-based approximation method for multi-server queueing networks under different service disciplines with correlated interarrival and service times

Khayyati

Tan

2021

International Journal of Production Research

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Developing e cient performance evaluation methods is important to design and control complex production systems e↵ectively. We present an approximation method (SLQNA) to predict the performance measures of queueing networks composed of multi-server stations operating under di↵erent service disciplines with correlated interarrival and service times with merge, split, and batching blocks separated with infinite capacity bu↵ers. SLQNA yields the mean, coe cient of variation, and first-lag autocorrelation of the inter-departure times and the distribution of the time spent in the block, referred as the cycle time at each block. The method generates the training data by simulating di↵erent blocks for di↵erent parameters and uses Gaussian Process Regression to predict the inter-departure time and the cycle time distribution characteristics of each block in isolation. The predictions obtained for one block are fed into the next block in the network. The cycle time distributions of the blocks are used to approximate the distribution of the total time spent in the network (total cycle time). This approach eliminates the need to generate new data and train new models for each given network. We present SLQNA as a versatile, accurate, and e cient method to evaluate the cycle time distribution and other performance measures in queueing networks.

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Autocorrelation of cycle times in semiconductor manufacturing systems

Cited by 6 publications

References 13 publications

Autocorrelation effects in manufacturing systems performance: A simulation analysis

Autocorrelation effects in manufacturing systems performance: A simulation analysis

Modelling and analysis of the impact of correlated inter-event data on production control using Markovian arrival processes

Supervised-learning-based approximation method for multi-server queueing networks under different service disciplines with correlated interarrival and service times

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