With the continuous advancement of autonomous driving technology, the precision and efficiency of perception systems have become increasingly critical. Among various sensors, LiDAR plays a central role, and solid-state optical phased arrays (OPAs) are widely regarded as a promising future direction. However, traditional uniform OPAs often face challenges such as high power consumption and limited scalability.

This thesis addresses the design and optimization of sparse non-uniform OPAs, aiming to balance trade-offs among the number of antennas, element spacing, beamwidth, and side lobe level. We propose a novel formulation that simultaneously considers array sparsity and performance while enforcing distance constraints, which is solved using a modified genetic algorithm. The simulation results reveal a clear trade-off between sparsity and array performance, while also offering practical solutions to the constraints faced by current LiDAR systems. Furthermore, we investigate the impact of array configuration on beam steering and introduce a mathematical transformation that reformulates the steering design problem to be compatible with our model. The comparison results demonstrate that the proposed approach significantly improves array performance across the entire steering range.