A Systematic Cycle Time Reduction Procedure for  Enhancing the Competitiveness and Sustainability of  a Semiconductor Manufacturer

Chen, Tin-Chih

doi:10.3390/su5114637

Cited by 17 publications

(12 citation statements)

References 26 publications

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“…2. Results in accumulation of jobs resulting in bottleneck [6]. Throughput time is the amount of time required for a product to pass through a manufacturing process, thereby being converted from raw materials into finished goods.…”

Section: Introductionmentioning

confidence: 99%

Approach to Reduce Throughput Time in Grinding of Gundrills

Mahesh¹,

Shastry²,

Murthy³

et al. 2019

JESA

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In modern industries automated machines are considered as one of the most important constituent in improving productivity. Gundrills are special cutting tools used for deep-hole drilling to achieve a depth to diameter ratio ranging up to 300:1. Gundrill production is largely dependent on manual operations owing to the manufacturing complexities. The total throughput time in gundrill manufacturing is significantly affected by the manual grinding process. This paper presents a study on the various grinding operations and identifies bottlenecks during gundrill production. Simulation is done in ARENA by Rockwell Automation Inc. for the manual process as well as for the automation required for the required for reduction in cycle time. Non-parametric tests are conducted for validating the ARENA model. Conclusions are drawn to automate the production aimed at reducing the cycle time while improving quality and EHS (Environment, Health, Safety) practice by eliminating tedious and repetitive operations. The present work serves as a benchmark approach for any manufacturing engineer intended to reduce the throughput time of any components.

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Section: Introductionmentioning

confidence: 99%

Approach to Reduce Throughput Time in Grinding of Gundrills

Mahesh¹,

Shastry²,

Murthy³

et al. 2019

JESA

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show abstract

“…Inoue et al [2] highlighted the importance of observing and controlling the job cycle time to achieve competitive semiconductor manufacturing. In Chen [4], a back propagation network (BPN) was used to estimate the cycle time of a job from a number of factors. Then, the effects of controlling these factors on the cycle time could be analyzed, so that feasible actions to reduce the cycle time can be taken.…”

Section: Introductionmentioning

confidence: 99%

Combining statistical analysis and artificial neural network for classifying jobs and estimating the cycle times in wafer fabrication

Chen

2014

Neural Comput & Applic

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Estimating the cycle time of a job is meaningful in many ways for managing a wafer fabrication factory (wafer fab). However, this estimation is not easy, due to the complexity and uncertainty of the wafer fabrication environment. Recently, a number of hybrid methods have been proposed to improve the accuracy of estimating the cycle time of a job. Most of these methods used job classification, especially pre-classification. Among these methods, several were based on post-classification and achieved even better performances. Such post-classification-based methods were improved in this study by considering the required parameter adjustment instead of the estimation error. Thus, it is possible to classify jobs into more than two categories at the same time. From the view of neurocomputing, this study established a systematic and effective procedure to divide the input examples to an artificial neural network into several parts that can be better handled by different artificial neural networks. A real case was also used to illustrate the applicability of the proposed methodology. The effectiveness of the proposed methodology over several existing methods has been confirmed by statistical tests.

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“…In recent years, predicting the cycle times of jobs in a wafer fabrication factory has received a lot of attention from production control, soft computing, and operations management researchers because of its critical role in the competitiveness of a wafer fabrication factory (Chen, 2013b;Chen, 2014). Cycle-time prediction is also related to job scheduling (Sivakumar, 1999;Tsai & Chen, 2013), due-date assignment (Joseph & Sridharan, 2011;Mor et al, 2013), and avail-to-promise (ATP) calculation (Oracle, 2014).…”

Section: Introductionmentioning

confidence: 99%

A new cloud computing method for establishing asymmetric cycle time intervals in a wafer fabrication factory

Chen

2015

J Intell Manuf

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Abstract. Estimating the cycle time of each job in a wafer fabrication factory is critical.An equally critical problem is to estimate the range of a cycle time. This topic has rarely been discussed because most existing methods for range calculation establish only a symmetric range. However, a symmetric range does not meet the requirements of managerial applications based on the lower and upper bounds of a cycle time. Recently, a few researchers have attempted to establish the asymmetric bounds of a cycle time.However, these methods either have overly complex computations or do not consistently perform well. This study proposes a new approach for effective cycle-time bounding.First, construction of a back propagation network predicts the cycle time of a job. Second, two linear functions of the output are formed, and the threshold on the output node is fuzzified to derive the lower and upper bounds of the cycle time. In theory, such a treatment tightens the lower and upper bounds and improves the forecasting precision.Third, theorems for the feasibility of the two linear functions are proved. Fourth, a cloud computing scheme is proposed to improve the bounds in an effective manner. Finally, a real case illustrates the applicability of the proposed methodology. Experimental results show that this methodology narrows the range of cycle times for untrained data by 31%, while maintaining a considerably high hit rate of 92.5%.2

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A Systematic Cycle Time Reduction Procedure for Enhancing the Competitiveness and Sustainability of a Semiconductor Manufacturer

Cited by 17 publications

References 26 publications

Approach to Reduce Throughput Time in Grinding of Gundrills

Approach to Reduce Throughput Time in Grinding of Gundrills

Combining statistical analysis and artificial neural network for classifying jobs and estimating the cycle times in wafer fabrication

A new cloud computing method for establishing asymmetric cycle time intervals in a wafer fabrication factory

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