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首页> 外文期刊>International Journal of Production Research >Forecasting flow time in semiconductor manufacturing using knowledge discovery in databases
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Forecasting flow time in semiconductor manufacturing using knowledge discovery in databases

机译：使用数据库中的知识发现预测半导体制造中的流程时间

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摘要

Semiconductor manufacturing is characterised by a complex production process, advanced equipment, and volatile demand. Flow time (FT), noted cycle time in semiconductor manufacturing, is a key measure in the operations. This study develops FT forecasting models using knowledge discovery in databases. It follows cross industry standards for data mining, with the focus on business understanding, data pre-processing and classification techniques. The data include wafer lot transactions extracted from the manufacturing execution system of an 8-inch flash memory factory. The FT is forecasted for a single lot at a given production step. The models are constructed using 70% of the data and the rest 30% for their evaluation. The results illustrate that a decision tree model achieves 76.7% accuracy and a neural network model 88.2%. The novelty of this work is in a thorough understanding of operations, a single data source, and common classification techniques used to obtain high accuracy results. The models can generate FT forecasting for a single production step, a line segment or a complete line. They can be used to improve short term planning, overall operations and supply chain efficiency, via shift scheduling, labour and materials requirements planning, inventory management and delivery schedules.
机译：半导体制造的特征在于复杂的生产过程，先进的设备和不稳定的需求。半导体制造中的循环时间（FT），即周期时间，是操作中的关键指标。本研究使用数据库中的知识发现来开发FT预测模型。它遵循跨行业的数据挖掘标准，重点是业务理解，数据预处理和分类技术。数据包括从8英寸闪存工厂的制造执行系统中提取的晶圆批交易。在给定的生产步骤中，将预测单个批次的FT。使用70％的数据构建模型，其余30％用于评估。结果表明，决策树模型的准确率达到76.7％，神经网络模型的准确率达到88.2％。这项工作的新颖性在于对操作，单个数据源以及用于获得高精度结果的通用分类技术的透彻理解。这些模型可以为单个生产步骤，生产线段或完整生产线生成FT预测。通过轮班计划，人工和物料需求计划，库存管理和交货计划，它们可以用于改善短期计划，整体运营和供应链效率。

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