Beam Controlling Technology Study Of Liquid Crystal Phased Array

Liquid crystal optical phased array(LCOPA) technology achieves high-precision and high-efficiency beam pointing, through accurate phase modulation of laser. It has important applications in modern radar, space communications and other fields, because of its advantages, such as random beam pointing and inertial beam steering, small equipment, etc. Limited by manufacturing technique, electric field disturbance between encodes and the working temperature, the factual steering efficiency is decreased. Therefore, it is of great importance to improve the performance of LCOPA through the beam controlling algorithms. This paper is dealt with the way to improve the diffraction efficiency of LCOPA.1. The mechanism of LCOPA is analyzed first, and the technique of obtaining static curve between voltage and phase delay is summarized then. Additionally, two kinds of beam controlling models are compared. Finally the affect factors such as the quantization of controlling voltage, disturbance of electric field between electrodes are analyzed.2. For the optimization of the periodic model, an algorithm based on the Hooke-Jeeves algorithm is analized. This algorithm can weaken the energy of zero order and other diffraction orders, and improve the diffraction efficiency of small period. Besides, the application platform is designed to check the property of the algorithm. The experimental results verify the simulation results.3. In order to decrease the iterations of Hooke-Jeeves, an algorithm named random disturbance is proposed. Its main principal is to add a random disturbance to all of the electrodes during one period in parallel. The situational results show that to achieve the same diffraction efficiency, it needs less iterations than the Hooke-Jeeves.4. For the optimization of the non-periodic model, the genetic algorithm(GA) is introduced in. through GA, the adaptive correction of phase distribution is realized, and the energy of the zero and other higher diffraction orders is decreased, therefore the diffraction efficiency is improved.5. The improved algorithm of GA is researched, so as to improve the convergence speed. This goal is achieved by adjusting the parameters of GA adaptively. To reach the same pointing efficiency, the literals of the improved GA is decreased to same degree.6. In order to improve the performance of GA better, the combination algorithm of GA and Hooke-Jeeves is proposed. After certain times of GA literal, the best individual is picked up to accept additional optimization by Hooke-Jeeves. The optimized individual will be sent back to the populations after that. The combined algorithm can improve the convergence speed to large degree, according to the simulation results.