# ML-Based JIT1 Optimization for Throughput Maximization in Cluster Tool Automation
## 1 Introduction  
Research on the productivity of semiconductor cluster tools is being actively conducted in the increasingly diversified production environment of semiconductor manufacturing companies [1]. In semiconductor manufacturing, the single wafer processing method processes each wafer within a single facility and is widely used in cluster tools suitable for producing products that meet the various market requirements; it includes the lithography process, etching process, and deposition. It is also used in processes such as display manufacturing [2]. In particular, the quantity of semiconductor wafers produced per unit of time has become a field requiring more research. The importance of a foundry fab, a multi-variety environment, is becoming more prominent. Therefore, it is time to find a strategy to identify an optimization method for on-demand, up-to-date processes in existing cluster tools [3]. Technologies that can support services, through artificial intelligence, automation, optimization, and so on, will enable a direct or indirect effect not only on the production optimization of the already installed semiconductor manufacturing fabs but also on the optimization of semiconductor tools to be introduced in the future [4]. The scheduling of equipment units can be divided into deterministic and unstable environments. A deterministic environment mathematically performs the scheduling without considering arbitrary elements inside the equipment [3]. In a variable environment, a mathematical approach is not easy to perform, and an approach considering various situations and conditions inside and outside the facility is required. The research method under a deterministic environment must explain the productivity results of cluster tools that arechanged by various variables in the real world, so there are limitations in the practicality of studies [5]. Field engineers are also trying to analyze and improve the throughput affected by these various variables [6]. Recently, different situations have been examined, such as when several types of wafers are processed in parallel, when a failure occurs during the process, or when the chamber schedule is unstable [4,7]. To date, when throughput problems occur, the cause of the problem has been found in the following way. First, correlation analysis has been done between the measured values of equipment and throughput. Second, throughput correlation analysis has been done through abnormal operation analysis of the facility controller. Third, data comparison analysis has been performed through simulations. Recently, throughput modeling through data analysis using various ML methods has been proposed, but several points should be considered as follows. Scheduling strategies differ because data analysis based on real-time data is not stable. Therefore, an integrated schedule model, analysis method, and real-time facility optimization method has not been presented [4,7]. The use of unoptimized equipment may not result in optimal throughput results [8]. To simultaneously solve these two problems, some researchers have modeled the initial transition state schedule with Petri net and used operational research methodologies such as linear algebra or MIP (mixed-integer program). A study was conducted to determine the work's starting point [9,10]. Other researchers have tried to show that the best method is to use a strategy in which a possible task starts late in the start-up transient period and a strategy in which a possible task starts in a short end period. In addition, a PERT chart has been used. However, since these studies proposed a scheduling strategy considering the constant working hours of robots and PMs within the initial transition period, it is not clear whether the constraint can be satisfied even if there is time fluctuation [4,7,11]. In this study, we present an ML-based JIT1 optimization strategy using real-time data, robust to real-time fluctuations on cluster equipment.