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Controlling work in process during semiconductor assembly and test operations

机译:在半导体组装和测试操作期间控制过程中的工作

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This paper introduces a mid-term planning model for scheduling assembly and test operations aimed at minimising the difference between customer demand and product completions each day. A secondary objective is to maximise daily surplus which is a surrogate for throughput. Typically, semiconductor companies have 1000s of products or devices in their catalogue that can be organised into unique groups of up to a 100 devices each. This simplifies the planning process because it is only necessary to consider the groups as a whole rather than the individual devices when constructing schedules. In all, we developed and tested three related models. Each provides daily production rates at each process step for each device group for up to one month at a time. The models are distinguished by how cycle time is treated. The first takes a steady-state approach and uses Little's Law to formulate a WIP target constraint based on the average cycle time at each processing step. The second and third include integer and fractional cycle times in the variable definitions. To find solutions, raw production data are analysed in a preprocessing step and then converted to input files in a standard format. FlopC++ from the COIN-OR open source software project is used to write and solve the model. Testing was done using three data-sets from the Taiwan AT facility of a global semiconductor firm. By comparing model output with historical data for 6 device groups and 33 process steps, we were able to realise decreases in shortages of up to 40% per month.
机译:本文介绍了用于计划组装和测试操作的中期计划模型,旨在最小化每天客户需求与产品完成之间的差异。第二个目标是使每日剩余最大化,这是吞吐量的替代指标。通常,半导体公司在其目录中有数千种产品或设备,这些产品或设备可以组织成唯一的组,每组最多100个设备。这简化了计划过程,因为在构建计划表时只需要考虑整个组,而不是单个设备。总之,我们开发并测试了三个相关模型。每个设备在每个过程步骤中为每个设备组提供每天最多一个月的生产率。这些模型的区别在于如何处理周期时间。第一种采用稳态方法,并使用利特尔定律根据每个处理步骤的平均循环时间来制定WIP目标约束。第二个和第三个在变量定义中包括整数和分数循环时间。为了找到解决方案,在预处理步骤中分析原始生产数据,然后将其转换为标准格式的输入文件。来自COIN-OR开源软件项目的FlopC ++用于编写和求解模型。使用来自一家全球半导体公司的台湾AT设备的三个数据集进行了测试。通过将模型输出与6个设备组和33个处理步骤的历史数据进行比较,我们能够实现每月最多减少40%的短缺。

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