基于设备质量控制数据的虚拟机图像传感器缺陷数预测

Toshiya Okazaki, Kosuke Okusa, K. Yoshida
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引用次数: 4

摘要

本文介绍了图像传感器缺陷数预测的方法和评价结果。我们使用回归树和逐步AIC进行变量选择,并使用广义线性模型进行回归,而不是使用偏最小二乘(PLS)回归。结果表明,与传统方法相比,该方法的预测性能有所提高。通过这个,我们可以预测其他可计数的值,如缺陷或灰尘颗粒。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Prediction of the Number of Defects in Image Sensors by VM using Equipment QC Data
This paper describes methods and evaluation results of predicting the number of defects in image sensors. We used regression tree and stepwise AIC for variable selection and generalized linear model for regression, instead of partial least squares (PLS) regression. The results showed improvement in prediction performance in comparison with the conventional method. By this, we could predict other countable values such as defects or dust particles.
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