GA-based minimization of scheduling problem in photolithographic track systems

Track systems, which are widely used in the field of photolithography, can perform sequential coat, bake, chill and develop operations. These systems are typically configured by process engineers with their expert knowledge of the system in order to optimize flow interactions as much as possible in order to reduce the amount of bake plate temperature ramping required whilst still maintaining a high level of parallelism in order to maintain a high throughput. This experiential level of optimization however is not definitive and does not investigate the entire optimization search space. There also may be hidden effects that are unknown to engineers, resulting in optimal solutions which remain unknown. This paper proposes an optimization procedure using genetic algorithms to minimize the makespan of a given set of jobs processed on such a tool. To do this a software simulation of how the tool schedules jobs are coded and a chromosome representation of the configuration map (CM) relating to the bake modules within the tool is developed.