Using an Optical Motion Sensor for Visualization and Analysis of Maintenance Work on Semiconductor Manufacturing Equipment

We developed a motion capture technique to record a worker’s movements during preventive maintenance, especially wiping actions during wet cleaning. Time-series 3-D coordinate data of the worker’s hand was successfully obtained with a motion capture sensor, and four different possible movements were distinguished. This data was also correlated with changes in the number of particles on the surface of a chamber part before and after wiping. The results show that key indicators of high-quality maintenance work can be extracted from workers’ motions using this technique.

[1]  T. P. Caudell,et al.  Augmented reality: an application of heads-up display technology to manual manufacturing processes , 1992, Proceedings of the Twenty-Fifth Hawaii International Conference on System Sciences.

[2]  Joseph Psotka,et al.  Immersive training systems: Virtual reality and education and training , 1995 .

[3]  Marti A. Hearst.  Untangling Text Data Mining , 1999, ACL.

[4]  Zhaoying Zhou,et al.  A real-time articulated human motion tracking using tri-axis inertial/magnetic sensors package. , 2004, IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society.

[5]  Holger Regenbrecht,et al.  Augmented reality projects in the automotive and aerospace industries , 2005, IEEE Computer Graphics and Applications.

[6]  Adrian Hilton,et al.  A survey of advances in vision-based human motion capture and analysis , 2006, Comput. Vis. Image Underst..

[7]  Seul Jung,et al.  FPGA Design for Controlling Humanoid Robot Arms by Exoskeleton Motion Capture System , 2006, 2006 IEEE International Conference on Robotics and Biomimetics.

[8]  Tsuyoshi Moriya,et al.  Knowledge sharing and creation in the semiconductor equipment industry , 2008, 2008 International Symposium on Semiconductor Manufacturing (ISSM).

[9]  Javier Garcia,et al.  Projection of speckle patterns for 3D sensing , 2008 .

[10]  Manoj Kumar Tiwari,et al.  Data mining in manufacturing: a review based on the kind of knowledge , 2009, J. Intell. Manuf..

[11]  Dana Kulic,et al.  A stereo camera based full body human motion capture system using a partitioned particle filter , 2010, 2010 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[12]  Sebastian Thrun,et al.  Real time motion capture using a single time-of-flight camera , 2010, 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[13]  T. Moriya,et al.  Vertical re-startup of plasma etching tool from earthquake , 2011, 2011 e-Manufacturing & Design Collaboration Symposium & International Symposium on Semiconductor Manufacturing (eMDC & ISSM).

[14]  Aravind Kailas.  Basic human motion tracking using a pair of gyro + accelerometer MEMS devices , 2012, 2012 IEEE 14th International Conference on e-Health Networking, Applications and Services (Healthcom).

[15]  Christian Theobalt,et al.  On-set performance capture of multiple actors with a stereo camera , 2013, ACM Trans. Graph..

[16]  Peter Gaboury,et al.  Eradicating human errors during preventive maintenance understanding the psychological reasons we make errors and implementing proactive practices to manage and reduce human errors , 2013, ASMC 2013 SEMI Advanced Semiconductor Manufacturing Conference.

[17]  Xindong Wu,et al.  Data mining with big data , 2014, IEEE Transactions on Knowledge and Data Engineering.

[18]  Jürgen Schmidhuber,et al.  Deep learning in neural networks: An overview , 2014, Neural Networks.

[19]  Hidefumi Matsui,et al.  Observation and Elimination of Recoil Particles From Turbo Molecular Pumps , 2015, IEEE Transactions on Semiconductor Manufacturing.

[20]  Masayuki Okamoto,et al.  Automatic property visualization for material survey support , 2016, 2016 International Symposium on Semiconductor Manufacturing (ISSM).

[21]  Rogier Kuijpers,et al.  ASML: A decade of big data use , 2016, 2016 International Symposium on Semiconductor Manufacturing (ISSM).

[22]  Shintaro Sato,et al.  Unstructured data treatment for big data solutions , 2016, 2016 International Symposium on Semiconductor Manufacturing (ISSM).

[23]  Ryohei Orihara,et al.  A Comprehensive Big-Data-Based Monitoring System for Yield Enhancement in Semiconductor Manufacturing , 2017, IEEE Transactions on Semiconductor Manufacturing.