# ML-Based JIT1 Optimization for Throughput Maximization in Cluster Tool Automation
## 2 Related Work
### Maximizing the Throughput of the Cluster Tool
Figure 3 shows the relations between process time, WAC time, JIT1, throughput, PM, and target. PM means how many process modules are used. Target refers to calculated result data from the equation for maximum productivity of cluster tools. There is a significant correlation that could not be observed between throughput and JIT1. Figure 2: Petri net model of cluster tool simulation. Figure 4 shows that the following steps should be followed to optimize JIT1 for the current cluster facility. A data collection process is required for data analysis, but the data has to be processed due to security concerns in the semiconductor manufacturing fab. It takes significant time to analyze and simulate the processed data [7]. Manual simulation implementation can be inefficient because it takes a similar amount of time as the primary setting of the cluster facility. In addition, application through simulation is also conducted passively, so the above analysis method is a continuous inefficient work method under the multi-variety production system. **3. JIT1 Optimization for Throughput Maximization**  
The throughput maximization method of semiconductor etch cluster facilities proposed in this paper analyzes the current performance of the cluster through ML-based  
Figure 4: JIT1 optimization of semiconductor cluster tool (current). Figure 3: Correlation between throughput and process time, WAC time, and JIT1. algorithms, predicting the optimal JIT1, and reflecting real-time optimization parameters for the cluster. The semiconductor etch cluster facility can be changed according to mechanical factors such as load port performance, transfer robot performance, airlock pump/vent performance, VTM robot performance, and various door performances. In addition, characteristics of the semiconductor etching process and the process of the chamber may be changed depending on the process time, when the wafer performs the process in the chamber, and the WAC time, when the wafer proceeds without the wafer in the chamber. However, the method to obtain maximum productivity considering the constant mechanical performance, process time, and WAC time of this etch cluster lies in the optimization of JIT1 parameters related to scheduling that affect the start time of wafer movement [19]. The optimization of JIT1 can be obtained through simulation modeling of various parameters by analyzing the data of the cluster. However, this modeling simulation method is very inefficient in terms of time. The method proposed in this study is shown in Figure 5. We designed an architecture to optimize productivity by combining the optimal algorithms for data analysis of a semiconductor cluster through data generated from the cluster. KNN is the optimal ML algorithm for a semiconductor etch cluster [4]. The facilities of the semiconductor manufacturing fab are ready for daily 24-h production, and data generated from the facility can be collected in real time as standardized data. The content in the standardized data includes information such as process time, WAC time, and wafer transfer time of the wafer being processed in the facility. WPH can be analyzed through this.