Interactive Q-Learning Approach for Pick-and-Place Optimization of the Die Attach Process in the Semiconductor Industry

Abstract

In semiconductor back-end production, the die attach process is one of the most critical steps affecting overall productivity. Optimization of this process can be modeled as a pick-and-place problem known to be NP-hard. Typical approaches are rule-based and metaheuristic methods. The two have high or low generalization ability, low or high performance, and short or long search time, respectively. The motivation of this paper is to develop a novel method involving only the strengths of these methods, i.e., high generalization ability and performance and short search time. We develop an interactive Q-learning in which two agents, a pick agent and a place agent, are trained and find a pick-and-place (PAP) path interactively. From experiments, we verified that the proposed approach finds a shorter path than the genetic algorithm given in previous research.