Product Quality Prediction with Convolutional Encoder-Decoder Architecture and Transfer Learning

Mining data collected from industrial manufacturing process plays an important role for intelligent manufacturing in Industry 4.0. In this paper, we propose a deep convolutional model for predicting wafer fabrication quality in an intelligent integrated-circuit manufacturing application. The wafer fabrication quality prediction is motivated by the need for improving product line efficiency and reducing manufacturing cost by detecting potential defective work-in-process (WIP) wafers. This work considers the following two crucial data characteristics for wafer fabrication. First, our model is designed to learn spatial correlation between quality measurements on WIP wafers and fabrication results through an encoder-decoder neural network. Second, we leverage the fact that different products share the same raw manufacturing process to enable the knowledge transferring between prediction models of different products. Performance evaluation on real data sets is conducted to validate the strengths of our model on quality prediction, model interpretability, and feasibility of transferring knowledge.

[1]  Qiang Yang,et al.  Distant Domain Transfer Learning , 2017, AAAI.

[2]  Jimmy Ba,et al.  Adam: A Method for Stochastic Optimization , 2014, ICLR.

[3]  Ross B. Girshick,et al.  Fast R-CNN , 2015, 1504.08083.

[4]  Xiaoou Tang,et al.  Image Super-Resolution Using Deep Convolutional Networks , 2014, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[5]  Jian Sun,et al.  Deep Residual Learning for Image Recognition , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[6]  Amit Dhurandhar,et al.  Continuous prediction of manufacturing performance throughout the production lifecycle , 2016, J. Intell. Manuf..

[7]  Yada Zhu,et al.  Hierarchical Multi-task Learning with Application to Wafer Quality Prediction , 2012, 2012 IEEE 12th International Conference on Data Mining.

[8]  King-Sun Fu,et al.  IEEE Transactions on Pattern Analysis and Machine Intelligence Publication Information , 2004, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[9]  Roberto Cipolla,et al.  MultiNet: Real-time Joint Semantic Reasoning for Autonomous Driving , 2016, 2018 IEEE Intelligent Vehicles Symposium (IV).

[10]  Qiang Yang,et al.  An Overview of Multi-task Learning , 2018 .

[11]  Alexei A. Efros,et al.  Split-Brain Autoencoders: Unsupervised Learning by Cross-Channel Prediction , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[12]  Rajat Raina,et al.  Self-taught learning: transfer learning from unlabeled data , 2007, ICML '07.

[13]  Wen-Chih Peng,et al.  Interpretable Multi-task Learning for Product Quality Prediction with Attention Mechanism , 2019, 2019 IEEE 35th International Conference on Data Engineering (ICDE).

[14]  Yoshua Bengio,et al.  Understanding the difficulty of training deep feedforward neural networks , 2010, AISTATS.

[15]  A. Azzouz 2011 , 2020, City.

[16]  Sebastian Ruder,et al.  An Overview of Multi-Task Learning in Deep Neural Networks , 2017, ArXiv.

[17]  Jason Weston,et al.  A unified architecture for natural language processing: deep neural networks with multitask learning , 2008, ICML '08.

[18]  Ivan Laptev,et al.  Learning and Transferring Mid-level Image Representations Using Convolutional Neural Networks , 2014, 2014 IEEE Conference on Computer Vision and Pattern Recognition.

[19]  Gaël Varoquaux,et al.  Scikit-learn: Machine Learning in Python , 2011, J. Mach. Learn. Res..