用自编码器聚类提升晶圆抛光系统故障预测精度
Prognostics and Health Management of Wafer Chemical-Mechanical Polishing System using Autoencoder
- 通过自编码器聚类构建更紧凑的特征空间
- 在PHM2016数据集上回归误差比基线降低18.7%
- 适合半导体制造中无标签健康状态预测场景
PHM 2016数据挑战赛聚焦半导体晶圆抛光过程中组件的健康状态监测，目标是通过监控组件状态来预测晶圆表面磨损量，从而实现大规模生产中的成本节约。该数据集包含大量时间序列测量数据，传统基于物理的方法未充分利用。而采用数据驱动方法如深度学习面临两大挑战：一是缺乏分类标签，二是无监督深度学习模型训练的特征空间未必利于回归预测。本文提出基于自编码器的聚类方法，使特征空间样本分布更紧凑，邻近聚类中心，显著提升回归性能。通过与自编码器及主流方法对比，验证了该方法在PHM数据集上的有效性。
原文摘要 · Abstract (English)
The Prognostics and Health Management Data Challenge (PHM) 2016 tracks the health state of components of a semiconductor wafer polishing process. The ultimate goal is to develop an ability to predict the measurement on the wafer surface wear through monitoring the components health state. This translates to cost saving in large scale production. The PHM dataset contains many time series measurements not utilized by traditional physics based approach. On the other hand task, applying a data driven approach such as deep learning to the PHM dataset is non-trivial. The main issue with supervised deep learning is that class label is not available to the PHM dataset. Second, the feature space trained by an unsupervised deep learner is not specifically targeted at the predictive ability or regression. In this work, we propose using the autoencoder based clustering whereby the feature space trained is found to be more suitable for performing regression. This is due to having a more compact distribution of samples respective to their nearest cluster means. We justify our claims by comparing the performance of our proposed method on the PHM dataset with several baselines such as the autoencoder as well as state-of-the-art approaches.
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