Analysis and design of algorithms for the manufacturing process of integrated circuits

The stage of transporting semiconductor chips from the wafer to the support strip is crucial in the integrated circuit manufacturing process. This process can be modeled as a combinatorial optimization problem where the objective is to reduce the total distance the robotic arm must travel to pick up each chip and place it in its corresponding position within the support structure. This problem is of the pick-and-place type and is NPhard. The (approximate) solution proposals of state-of-the-art methods include rulebased approaches, genetic algorithms, and reinforcement learning. In the present work, one of these methods is analyzed, which models the problem as one of binary integer programming and proposes a genetic algorithm. Based on this analysis, we proposed and evaluated other methods, including a greedy algorithm and a genetic algorithm that improve the state-of-the-art results for test cases usually used in the literature. Additionally, the results obtained from a new ILP model for this problem indicate that the genetic algorithm results are very close to the optimal values.