Heuristic algorithms for scheduling an automated wet-etch station

Abstract

Wet-etching is a key step in wafer fabrication. A wet-etch station is a chemical batch process involving a complex interplay of mixed intermediate storage (MIS) policies and a shared robot for wafer transfers. Its operation poses a challenging resource-constrained scheduling problem that is crucial for enhancing productivity, improving yield and minimizing contamination. In this paper, we develop three new algorithms for scheduling wafer jobs for a given sequence, which comfortably outperform a literature algorithm in terms of solution quality without requiring excessive effort. Furthermore, we propose a simulated annealing (SA) algorithm for sequencing the wafer jobs. Using this SA algorithm, an existing sequencing algorithm based on tabu search (TS), two job-scheduling algorithms and two algorithms for initial job sequence, we identify eight complete algorithms for scheduling operations in an automated wet-etch station (AWS). After a thorough numerical evaluation, we conclude that the TS sequencing strategy combined with two of our three job-scheduling algorithms is the best option that yields up to 25–30% lower makespans than a literature algorithm, and requires acceptable computing times for industrial-scale problems.