Manufacturing improvement team programs in the semiconductor industry

Bailey, Di

doi:10.1109/66.554476

Cited by 16 publications

(11 citation statements)

References 7 publications

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“…The first article was written to directly examine the differences between team and individual performance within the analyst industry. Their study showed that analyst teams have lower accuracy in their surplus forecasts than individual analysts, but the timeliness of research reports published by analyst teams is better [45]. Conversely, studies with hand-collected samples found that analysis showed and teams made more accurate surplus forecasts than individual analysts [46].…”

Section: Individual Performance Advantagementioning

confidence: 97%

Team Performance: A Review of Research and Future Prospects

Wang¹

2023

BCPBM

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With the increasing trend of economic globalization, on the one hand, countries and regions around the world are communicating more frequently than before, and in this mode of frequent interaction, industries, cultures and individual values are becoming more diversified and diverse, and corporate teams are also gradually developing in diversity, and the number of cross-regional and cross-industry teams is increasing. Team performance, as a means and standard of team behavior evaluation and management, is inseparable from the realization of the enterpriseization goals. On the other hand, the competition between enterprises at home and abroad is becoming more and more intense, and team management plays an increasingly important role in the 21st century. Now that the Internet winter has come, most enterprises are holding each other for warmth, and it is especially important to make the team in the enterprise work more actively, have a better atmosphere and higher performance. From the time American entrepreneurs started to focus on team management until today, there has been a great deal of literature related to teams, which have gradually become the basic work unit for organizations to engage in activities in a dynamic environment, and the factors affecting team performance have been a hot spot for researchers to focus on. This paper reviews the literature related to team performance in management and presents an outlook for future research. Collating the literature reveals that the difference between team performance and individual performance has not been agreed upon in research; the effect of team diversity on team performance is also controversial, suggesting that team performance needs further research.

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Section: Individual Performance Advantagementioning

confidence: 97%

Team Performance: A Review of Research and Future Prospects

Wang¹

2023

BCPBM

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“…However, the waiting time because of the unavailability of operators during operator works OMUH/OHUH should be reduced so that tool availability would also be improved. Second, skilled operators (cross functional operators [14]) are required to perform the OMUH work and the tool maintenance work without affecting the work during online unit hours.…”

Section: Operator Efficiencymentioning

confidence: 99%

Optimized operations by extended X-factor theory including unit hours concept

Kishimoto

Ozawa

Watanabe

et al. 2001

IEEE Trans. Semicond. Manufact.

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The relationships between tool performance, cycle time, and throughput have been described to achieve the highest productivity. Though tool utilization is low for manually operated tools, long waits prior to product processing lead to a productivity loss in our production line. We focus on operator efficiency in the CMP (chemical mechanical polishing) operation, which, as a typical manually operated tool, influences cycle time. This paper introduces an online/offline unit hour analysis method, which has been designed to optimize operator allocation and headcount in our production line, using -factor theory in order to achieve the shortest cycle time. By using online/offline unit hour analysis with -factor theory, the root cause of waiting time can be identified and the relationship between operator headcount and cycle time can be described. The waiting time can be reduced by 35% and the cycle time can be reduced by 25%, according to a simulator with optimized operator allocation and headcount based on online/offline unit hour analysis.

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“…This value is fixed for the process. [17]. The factors and factor values used for some of the modules in one case study are shown in Table I.…”

Section: A Yield Model Factorsmentioning

confidence: 99%

Yield learning in integrated circuit package assembly

Balasubramaniam

Sarwar²,

Walker³

1997

IEEE Trans. Comp., Packag., Manufact. Technol. C

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Abstract-This paper describes a yield learning model for integrated circuit package assembly. The goal was to provide a management tool for making yield projections, resource allocations, understanding operating practices, and performing what-if analyses. The model was developed using a series of case studies of packages entering manufacturing. These studies were a tape carrier package (TCP) at Intel, Chandler, AZ, a ceramic ball grid array (CBGA) and plastic quad flat pack (PQFP) at IBM, Bromont, P.Q., Canada, and a plastic ball grid array (PBGA) at Motorola, Austin, TX. These packages covered a wide range of technologies, including liquid and overmolded encapsulation, wirebond and controlled collapsed chip connection (C4) chip connections, and tape automated bonding (TAB), ceramic, laminate, and leadframe substrates. The factors that affect yield learning rates (e.g. process complexity, production volumes, personnel experience) were identified and a nonlinear spreadsheet-based response surface model was built. The model separates out the underlying chronic yield from excursions due to human error, equipment failure, etc. The model has been shown to accurately predict the yield ramp as a function of the factor values. One of the conclusions of this work is that all of the very dissimilar assembly processes had very similar factors, with very similar factor sensitivities and rankings in terms of how each affected the yield learning rate. In all cases, the most important factors were operator experience, changes in line volume, types of work teams, process complexity, equipment upgrades, and technology type. Since the yield ramp for a new product will hopefully be short, the model must be calibrated for a particular product very quickly. We have developed a graphical interface and tuning procedure so that when the production data is readily available, the tuning procedure takes only a few days.

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Manufacturing improvement team programs in the semiconductor industry

Cited by 16 publications

References 7 publications

Team Performance: A Review of Research and Future Prospects

Team Performance: A Review of Research and Future Prospects

Optimized operations by extended X-factor theory including unit hours concept

Yield learning in integrated circuit package assembly

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