Control And Parameter Intelligent Adjustment Of High Speed And High Precision Motion Platform

With the rapid development of semiconductor industry,the complexity of integrated circuit is getting higher and higher,and chips are getting smaller and smaller,which makes chip packaging process more and more difficult.Semiconductor equipment has higher and higher performance requirements to support above development.As the core component of most semiconductor equipment,high-speed and high-precision motion platforms face great challenges on the structural design and design of servo control strategy.In this thesis,theoretical analysis and practical application research of control system design,control algorithm and parameter setting are carried out on the high-speed and high-precision motion platform.Contents and achievements of this thesis are as follows:1)Common control algorithms of servo system are studied and analyzed in this thesis.These algorithms include PID control algorithm,feedforward control algorithm and neural network PID control algorithm.The principle of each control algorithm listed above is analyzed."Three closed-loop PID + feedforward" algorithm is finally chosen as the controller of high-speed and high-precision motion platform as this composite control structure can take care of both the dynamic and static performances of the system at the same time.2)To avoid the inconvenience of manual parameter setting of a PID controller,the neural network algorithm and fuzzy system are studied and analyzed in this thesis.Through the analysis of back propagation algorithm in neural network,combined with LM algorithm and fuzzy logic,parameters of a PID controller are tuned based on neural network and fuzzy system.And the specific implementation flow of this method is given.The experimental results show that the method can obtain the appropriate PID parameters.This method avoids manual setting.It is suitable for mass production with large scale packaging equipment and lots of motion platforms to set PID parameters.The industry benefits from this method because it saves a lot of labor cost and time cost.3)Iterative learning control usually has many turns of iterations and slow convergence speed in the later stage.To solve above problem,a double iterative learning control strategyis proposed and applied to the compound control structure of "three closed-loop PID +feedforward".Two A-type open-loop iterative learning controllers are designed.One is based on the reference input signal iterative learning controller(ILC1)and the other is based on the control quantity iterative learning controller(ILC2).Then the convergence is analyzed and the zero phase filter is designed.Finally the experimental verification is carried out.4)The ILC1 based on the reference input signal is used for iterative learning to get the best reference input signal.The ILC2 based on the control quantity is used for iterative learning to get the best control compensation quantity.The experimental results show that the double iterative learning controller can effectively improve the tracking performance of the X-Y motion platform in a few iterations.The RMS value of the tracking error of the X-Axis is reduced by 86% and Y-Axis is reduced by 92%.And the control structure is simple.It is suitable to be applied to practical engineering.The research results of this thesis were applied to the high-speed and high-precision motion platform designed and provided by my research group.And good control results were achieved.The PID parameter tuning method proposed in this thesis is not only applicable to the experimental platform,but also to other servo systems.The double iterative learning control strategy proposed in this thesis can also be applied to high-end NC machines,and so on.