Optimizing Controller Tuning


To improve the efficiency and accuracy of semiconductor manufacturing processes by optimizing the controller parameters for the air bearing motor, which controls the wafer stage in KLA's semiconductor manufacturing equipment.

Key Features

Implemented particle swarm optimization in MATLAB to fine-tune 21 controller parameters

Utilized frequency-based techniques, such as loop shaping and Bode plots, to analyze and improve system performance

Defined objective functions that optimized gain margin, phase margin, bandwidth, and other factors for the air-bearing motor controller

Created technical documents for the Frequency Response Analysis in MATLAB to train employees

The project focused on enhancing the performance of the air-bearing motor controller used in KLA's semiconductor manufacturing process. The goal was to develop an optimization algorithm that optimizes the gain margin, phase margin, modulus margin, bandwidth, etc., of an open loop frequency response by varying 21 control parameters. By optimizing the controller parameters using particle swarm optimization in MATLAB, I successfully improved the overall efficiency, accuracy, and throughput of the wafer stage and the entire production line

Method - PSO

Particle Swarm Optimization (PSO) helps to minimize or maximize a function or parameters to obtain the best solution. It varies from other methods because it is:

  • Stochastic

  • Gradient Free

  • Population Based

  • Non linear, multimodal or multivariant functions

  • Time Efficient

  • Computationally Inexpensive

PSO equation
PSO equation


Defined objective functions that worked for all parameters of the air bearing motor controller

Optimized for factors such as gain margin, phase margin, bandwidth, and others to enhance system performance

Utilized MATLAB to implement particle swarm optimization for fine-tuning 21 controller parameters of the air-bearing motor

Increased efficiency and productivity of the wafer stage and the entire manufacturing process by optimizing controller performance

Generated comprehensive technical documents on the Frequency Response Analysis in MATLAB

Provided training materials for employees, ensuring a smooth transition and adoption of the new techniques

Particle Swarm Optimization
Objective Functions Definition
Technical Documentation and Training


The successful implementation of advanced optimization techniques and controller designs led to a significant improvement in controller performance for the air-bearing motor. This resulted in increased efficiency, stability, and accuracy of the wafer stage and the semiconductor manufacturing process. Moreover, the development of technical documents and training materials enabled employees to quickly adopt and utilize the new methods, further contributing to the long-term success of the project.

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