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首页> 外文会议>International Conference on Advanced Materials Research and Manufacturing Technologies >A Reliable Defect Detection Method for Patterned Wafer Image Using Convolutional Neural Networks with the Transfer Learning
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A Reliable Defect Detection Method for Patterned Wafer Image Using Convolutional Neural Networks with the Transfer Learning

机译：使用卷积神经网络与转移学习的可靠缺陷检测方法

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In the semiconductor manufacturing process,wafer inspection images have valuable information on defects and wafer yield.It is worthy to analysis these images and there are'many algorithms for this.Whenever new product designs are introduced,the algorithms are used to detect defects.Most new product designs have new types of defects.Hence,it is important to update the algorithms to cope with new types of defects.To update the algorithms,engineers collect the data and adjust a parameter values,such as threshold,to detect defects.It is time-consuming to collect enough data and only a knowledgeable engineer can find an appropriate parameter value.However,there is always a lack of engineer resources and time in the manufacturing industry.Due to these reasons,many algorithms can't be used reliably and have disappeared.Therefore,we propose the advanced method to update an algorithm easily using a deep learning model.Deep learning models achieve high accuracy with less engineer knowledge than traditional algorithms.But it also needs a lot of data to train the model,so we apply an advanced method of updating with a small amount of data in a short time.This is called as transfer learning.Once we train a deep learning model with high accuracy,we can update the model with a small amount of data.Transfer learning uses the information gained during the training.As a result,we make a model that is easy to update with high accuracy.In the experiments,we obtained 99% accuracy with sufficient training data.To update the model for the new data,we did transfer learning and got 97% accuracy.It was lower,but we only used 10% of the training data and 5% of the training time.Also,the accuracy we have obtained is enough to be used for defect analysis.Our approach outperformed and executed faster than traditional algorithms.
机译：在半导体制造过程中，晶片检测图像具有有关缺陷和晶片产量的有价值的信息。值得分析这些图像，并且对于此目的而言，无论何种新产品设计，算法都用于检测缺陷。最多新产品设计具有新类型的缺陷。最重要的是要更新算法以应对新类型的缺陷。要更新算法，工程师收集数据并调整阈值，例如阈值，以检测缺陷.it收集足够的数据和知识渊博的工程师只能找到合适的参数值。但是，在制造业中总是缺乏工程师资源和时间。为这些原因，许多算法不能可靠地使用并且已经消失了。因此，我们提出了使用深度学习模型轻松更新算法的先进方法。Deep学习模型以较少的工程师知识实现高精度但是，它还需要大量的数据来训练模型，因此我们在短时间内应用了一个高级数据更新的方法。这被称为传输学习。我们培训深入学习模型高精度，我们可以用少量数据更新模型.Transfer学习使用培训期间获得的信息。结果，我们制作了一种易于更新的模型，以高精度更新。在实验中，我们获得了99％准确性具有足够的训练数据。要更新新数据的模型，我们确实转移了学习并获得了97％的准确性。它较低，但我们只使用了10％的培训数据和5％的培训时间.Also，我们获得的准确性足以用于缺陷分析。我们接近比传统算法更快地执行并执行。

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