Big Data Analytics for Smart Manufacturing: Case Studies in Semiconductor Manufacturing

Smart manufacturing (SM) is a term generally applied to the improvement in manufacturing operations through integration of systems, linking of physical and cyber capabilities, and taking advantage of information including leveraging the big data evolution. SM adoption has been occurring unevenly across industries, thus there is an opportunity to look to other industries to determine solution and roadmap paths for industries such as biochemistry or biology. The big data evolution affords an opportunity for managing significantly larger amounts of information and acting on it with analytics for improved diagnostics and prognostics. The analytics approaches can be defined in terms of dimensions to understand their requirements and capabilities, and to determine technology gaps. The semiconductor manufacturing industry has been taking advantage of the big data and analytics evolution by improving existing capabilities such as fault detection, and supporting new capabilities such as predictive maintenance. For most of these capabilities: (1) data quality is the most important big data factor in delivering high quality solutions; and (2) incorporating subject matter expertise in analytics is often required for realizing effective on-line manufacturing solutions. In the future, an improved big data environment incorporating smart manufacturing concepts such as digital twin will further enable analytics; however, it is anticipated that the need for incorporating subject matter expertise in solution design will remain.

[1]  Chenming Hu,et al.  3D FinFET and other sub-22nm transistors , 2012, 2012 19th IEEE International Symposium on the Physical and Failure Analysis of Integrated Circuits.

[2]  L. Rabiner,et al.  An introduction to hidden Markov models , 1986, IEEE ASSP Magazine.

[3]  Jay Lee,et al.  Service Innovation and Smart Analytics for Industry 4.0 and Big Data Environment , 2014 .

[4]  Klaus-Dieter Thoben,et al.  "Industrie 4.0" and Smart Manufacturing - A Review of Research Issues and Application Examples , 2017, Int. J. Autom. Technol..

[5]  Rong Pan,et al.  Advance Process Control solutions for semiconductor manufacturing , 2002, 13th Annual IEEE/SEMI Advanced Semiconductor Manufacturing Conference. Advancing the Science and Technology of Semiconductor Manufacturing. ASMC 2002 (Cat. No.02CH37259).

[6]  B. Kowalski,et al.  Partial least-squares regression: a tutorial , 1986 .

[7]  James Moyne,et al.  Predictive Maintenance in Semiconductor Manufacturing Moving to Fab-Wide Solutions , 2015 .

[8]  James Moyne,et al.  Towards pharma 4.0; Leveraging lessons and innovation from Silicon valley , 2017 .

[9]  David C. Drain.  Run-to-Run Control in Semiconductor Manufacturing , 2002 .

[10]  James Moyne,et al.  Next generation advanced process control: Leveraging big data and prediction , 2016, 2016 27th Annual SEMI Advanced Semiconductor Manufacturing Conference (ASMC).

[11]  James Moyne,et al.  Yield Management Enhanced Advanced Process Control System (YMeAPC)—Part I: Description and Case Study of Feedback for Optimized Multiprocess Control , 2010, IEEE Transactions on Semiconductor Manufacturing.

[12]  Thomas F. Edgar,et al.  State estimation in high-mix semiconductor manufacturing , 2009 .

[13]  Costas J. Spanos,et al.  Fundamentals of Semiconductor Manufacturing and Process Control , 2006 .

[14]  Nils J. Nilsson,et al.  Artificial Intelligence , 1974, IFIP Congress.

[15]  Alexander J. Smola,et al.  Support Vector Regression Machines , 1996, NIPS.

[16]  D.M. Tilbury,et al.  An Approach for Factory-Wide Control Utilizing Virtual Metrology , 2007, IEEE Transactions on Semiconductor Manufacturing.

[17]  Teuvo Kohonen,et al.  The self-organizing map , 1990 .

[18]  Jennifer J. Vogel-Walcutt,et al.  Cognitive load theory vs. constructivist approaches: which best leads to efficient, deep learning? , 2011, J. Comput. Assist. Learn..

[19]  Thorsten Wuest.  Identifying Product and Process State Drivers in Manufacturing Systems Using Supervised Machine Learning , 2015 .

[20]  Christopher M. Bishop,et al.  GTM: The Generative Topographic Mapping , 1998, Neural Computation.

[21]  James Moyne.  A blueprint for enterprise-wide deployment of advanced process control , 2009 .

[22]  Fan-Tien Cheng,et al.  Tutorial on Applying the VM Technology for TFT-LCD Manufacturing , 2015, IEEE Transactions on Semiconductor Manufacturing.

[23]  Thomas F. Edgar,et al.  Smart manufacturing, manufacturing intelligence and demand-dynamic performance , 2012, Comput. Chem. Eng..

[24]  James Moyne,et al.  Big Data Capabilities Applied to Semiconductor Manufacturing Advanced Process Control , 2016, IEEE Transactions on Semiconductor Manufacturing.

[25]  T. Edgar,et al.  Smart Manufacturing. , 2015, Annual review of chemical and biomolecular engineering.

[26]  J. Hanley,et al.  The meaning and use of the area under a receiver operating characteristic (ROC) curve. , 1982, Radiology.

[27]  James Moyne,et al.  Improving lithography throughput and minimizing waste using predictive multi-area scheduling , 2012, Advanced Lithography.

[28]  J. Ma,et al.  Time-series novelty detection using one-class support vector machines , 2003, Proceedings of the International Joint Conference on Neural Networks, 2003..

[29]  Taghi M. Khoshgoftaar,et al.  Deep learning applications and challenges in big data analytics , 2015, Journal of Big Data.

[30]  Sameer Singh,et al.  Novelty detection: a review - part 2: : neural network based approaches , 2003, Signal Process..

[31]  Klaus-Dieter Thoben,et al.  An approach to monitoring quality in manufacturing using supervised machine learning on product state data , 2013, Journal of Intelligent Manufacturing.

[32]  裕幸 飯田,et al.  International Technology Roadmap for Semiconductors 2003の要求清浄度について － シリコンウエハ表面と雰囲気環境に要求される清浄度, 分析方法の現状について － , 2004 .

[33]  James Kennedy,et al.  Particle swarm optimization , 2002, Proceedings of ICNN'95 - International Conference on Neural Networks.

[34]  Zhuoqing Morley Mao,et al.  Categorization of Anomalies in Smart Manufacturing Systems to Support the Selection of Detection Mechanisms , 2017, IEEE Robotics and Automation Letters.

[35]  Richard A. Johnson,et al.  Applied Multivariate Statistical Analysis , 1983 .

[36]  James Moyne,et al.  Maintenance of virtual metrology models , 2016, 2016 27th Annual SEMI Advanced Semiconductor Manufacturing Conference (ASMC).

[37]  Sameer Singh,et al.  Novelty detection: a review - part 1: statistical approaches , 2003, Signal Process..