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Fuzzy neural network based yield prediction model for semiconductor manufacturing system

机译:基于模糊神经网络的半导体制造系统产量预测模型

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摘要

Accurate die yield prediction is very useful for improving yield, decreasing cost and maintaining good relationships with customers in the semiconductor manufacturing industry. To improve prediction accuracy of die yield, a novel fuzzy neural networks based yield prediction model is proposed in which the impact factors of yield and critical electrical test parameters are considered simultaneously and are taken as independent variables. The mapping between these independent variables and yield is constructed in the fuzzy neural network (FNN). The lineal regression between FNN-based yield predicting output and actual yield demonstrates the effectiveness of the proposed approach by historical experimental data of semiconductor fabrication line in Shanghai. The comparison experiment verifies the proposed yield prediction method improves on three traditional yield prediction methods with respect to prediction accuracy.
机译:准确的芯片成品率预测对于提高成品率,降低成本以及与半导体制造行业的客户保持良好关系非常有用。为了提高模具成品率的预测精度,提出了一种基于模糊神经网络的成品率预测模型,该模型同时考虑了成品率的影响因素和关键的电测试参数,并将其作为独立变量。这些自变量与收益之间的映射是在模糊神经网络(FNN)中构建的。基于FNN的产量预测产量与实际产量之间的线性回归通过上海半导体生产线的历史实验数据证明了该方法的有效性。比较实验验证了所提出的产量预测方法相对于三种传统的产量预测方法在预测准确性方面的改进。

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