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This dissertation presents a high-precision positioner with a novel superimposed concentrated-field permanent-magnet matrix. This extended-range multi-axis positioner can generate all 6-DOF (degree-of-freedom) motions with only a single moving part. It is actuated by three planar levitation motors, which are attached on the bottom of the moving part. Three aerostatic bearings are used to provide the suspension force against the gravity for the system. The dynamic model of the system is developed and analyzed. And several control techniques including SISO (single input and single output) and MIMO (multi inputs and multi outputs) controls are discussed in the dissertation. The positioner demonstrates a position resolution of 20 nm and position noise of 10 nm rms in x and y and 15 nm rms in z. The angular resolution around the x-, y-, and z-axes is in sub-microradian order. The planar travel range is 160 mm ?? 160 mm, and the maximum velocity achieved is 0.5 m/s at a 5-m/s2 acceleration, which can enhance the throughput in precision manufacturing. Various experimental results are presented in this dissertation to demonstrate the positioner??s capability of accurately tracking any planar trajectories. Those experimental results verified the potential utility of this 6-DOF high-precision positioner in precision manufacturing and factory automation.

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Moore’s law states that the number of transistors in an Integrated Circuit (IC) doubles every two years. Ever-increasing performance in mask writing machinery is therefore required being the first step in the manufacturing process. Many factors affect the quality of the end product, with the motion control system playing an important role. This thesis analyzes the performance of the motion controller for the positioning system in a mask writer application. The target motion in the X-axis in the mask writer is by design highly repetitive and predictable. As of today a feedforward-feedback controller is used, tuned for low deviation during writing. In this thesis it is found that the motion control can be improved by exploiting the repetitive nature of the motion task. Two iterative methods are explored, Iterative Feedback Tuning (IFT) and Iterative Learning Control (ILC). IFT is implemented as a parameter optimizing method for the existing Proportional-Integral-Derivative (PID) controller. Given suboptimal initial parameters the algorithm converges to a global minimum using a cost function to minimize total deviation and constraints on the maximum deviation. With the optimized parameter settings an improvement of a 31 % decrease in total deviation is seen compared to the default setting. ILC is implemented as a replacement to the current controller in an exposure motion. With the use of saved data from previous iterations the control signal is updated and refined to better suit the target motion. ILC is a promising method within high precision motion control by virtue of not needing a model of the system and its ability to suppress reoccurring disturbances. The algorithm achieves an improvement of a 94% decrease in total deviation during writing compared to the current controller. However, with this implementation long term stability is not guaranteed. A stable implementation of the algorithm tested on a test rig achieves an improvement of a 79.8% decrease in deviation during writing compared to the current feedforward-feedback controller. Additionally, correlations between parameter values of the current feedback controller and servo characteristics are analyzed to aid in the manual tuning process. Tuning the PID controller for fast rise time decreases the total deviation during writing. The derivative gain in the controller should be high to decrease the overshoot caused by the aggressive controller. This will induce some oscillations into the system, however not at the cost of performance as a result of the smooth motion during writing.