In Situ Optical Sensing and State Estimation for Control of Surface Processing

  • Rentian Xiong
  • Martha A. Grover


Many industrial processes involve the modification of surfaces. The semiconductor and microelectronics industries are extremely reliant upon surface processing [1], but other applications include solar cells, MEMS and microfluidics, thermal barrier coatings, and fuel cells. Optical measurements are commonly used to indirectly quantify the surface and bulk properties of materials. Unlike direct imaging methods, in which the local surface topography is visualized, indirect methods provide information on surface and bulk properties which are averaged over the entire area being sampled. Typically, a beam of light is directed at the surface to be measured, and the light interacts with the surface to produce the reflected or diffracted light that is then measured by a photodetector. One advantage of indirect measurement techniques is that they are non-invasive, and therefore can be used in extreme or harsh processing environments such as vacuum chambers and reaction vessels by passing the light through windows. These in situ optical sensors have been used for process control of film thickness and chemical composition. However, many more microscopic and nanoscale surface properties can be measured and potentially controlled using optical sensors. The barriers include the difficulty of modeling the optical response, the difficulty of inverting these relationships to robustly infer surface properties, and the practicality of including windows on the chamber to enable optical access. However, to create precise nanostructures in a high-throughput manufacturing setting, real-time measurement and control will be required, and optical sensors are expected to play a significant role.


Optical Measurement Optical Sensor Extended Kalman Filter Optical Model Model Predictive Control 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.



This research was supported by a grant from the National Science Foundation, entitled “CAREER: A Systems Approach to Materials Processing.” The research described in this chapter was performed with graduate student Rentian Xiong, with additional support on the experimental work by Paul Wissmann. Helpful discussions with Georgia Tech Professors Jay Lee and Ian Ferguson are appreciated. The generous contribution of the e-beam image of a lithographically patterned surface by Georgia Tech Professor Cliff Henderson and graduate student Richard Lawson is also gratefully acknowledged.


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© Springer Science+Business Media, LLC 2012

Authors and Affiliations

  1. 1.School of Chemical & Biomolecular EngineeringGeorgia Institute of TechnologyAtlantaUSA

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