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An Agent-Based Fuzzy Collaborative Intelligence Approach for Precise and Accurate Semiconductor Yield Forecasting

机译：基于Agent的模糊协同智能方法进行半导体产量的精确精确预测

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摘要

Yield forecasting is an important task for the manufacturer of semiconductors. Owing to the uncertainty in yield learning, it is, however, often difficult to make precise and accurate yield forecasts. To solve this problem, we propose an agent-based fuzzy collaborative intelligence approach that is modified from the fuzzy linear regression and back propagation network approach. In the proposed methodology, software agents rather than domain experts are used to improve the efficiency of collaboration. In addition, an agent decides the adjustable parameters by referencing to others so that the overall prediction performance can be improved in an effective way. In addition, we proposed a simple and effective way to aggregate the fuzzy forecasts by agents. Compared with the fuzzy linear regression and back propagation network approach, the proposed methodology reduced the average range and mean absolute percentage error by 18% and 99%, respectively.
机译：产量预测对于半导体制造商而言是一项重要任务。但是，由于收益学习的不确定性，通常很难做出准确而准确的收益预测。为了解决这个问题，我们提出了一种基于代理的模糊协作智能方法，该方法是从模糊线性回归和反向传播网络方法中改进而来的。在所提出的方法中，软件代理而非域专家被用于提高协作效率。此外，代理可以通过参考其他参数来确定可调参数，从而可以有效地提高整体预测性能。此外，我们提出了一种简单有效的方法来汇总代理商的模糊预测。与模糊线性回归和反向传播网络方法相比，该方法将平均范围误差和平均绝对百分比误差分别降低了18％和99％。

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