Wafer Lot Assignment for Parallel-Producing Tools Based on Heuristic Clustering Algorithm

Beyond the widely-studied scheduling of wafers within cluster tools, a novel and important perspective is raised in this paper to tackle an upper-level optimization problem in real-world production, i.e., the assignment of hybrid types of wafer lots to a set of cluster tools with parallel modules to minimize the maximum completion time for the lots. The main difficulty in addressing such a problem is that the objective, i.e., the maximum completion time, cannot be calculated explicitly beforehand. To make this problem tractable, the associated maximal overlap among tools is utilized to heuristically evaluate the objective for the problem. Besides, since the cluster tools for processing are identical, we further tackle this problem as a clustering issue. Accordingly, a clustering algorithm based on greedy searching is proposed to allocate wafer lots into cluster tools while minimizing the maximal overlap. To elucidate our method and its significance in real-world production, the wet bench tool in wet cleaning process is taken as a case study. We compare the proposed algorithm with the empirical method in fabs and several intelligent optimization algorithms, and the experimental results verify the effectiveness of our proposed method in terms of improved efficiency.