# ML-Based JIT1 Optimization for Throughput Maximization in Cluster Tool Automation
## 4 Simulation and Results
### Result  
As can be seen from the relationship between JIT1 and throughput in column 4 and row 3 in Figure 3, a significant correlation could not be observed between throughput and JIT1. However, as shown in Figure 9, it was confirmed that the throughput showed a significant correlation with JIT1 when the processing time was fixed at 180 and the WAC and other parameters of JIT1 were changed. In Figure 9, it can be seen that the throughput had an S curve, but the point at which the throughput was stabilized differed depending on the WAC time and JIT1. Therefore, it can be seen that for optimizing the cluster, it is necessary to set the optimal JIT1 considering the processing time and WAC time. There are several ways to find the optimal JIT1 under the variable environment of process time and WAC time. We can determine whether the cluster facility operates efficiently throughEquation (1). If the cluster facility's throughput is smaller than the calculation result of Figure 9, the facility is operating at a non-rate. Table 2 shows the Pearson coefficient and RMSE results of ML modeling. The Pearson coefficient of linear modeling was 0.941, and RMSE was 6.8, so it can be said that it was unsuitable for throughput analysis of the cluster. The optimal algorithm for ML modeling of the cluster was KNN, which had a Pearson coefficient of 0.989 and an RMSE of 3.28. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Models** & **Pearson Coefficient** & **RMSE** \\ \hline Linear regression & 0.941 & 6.80 \\ Polynomial regression & 0.949 & 6.32 \\ SVM with rbf & 0.974 & 4.76 \\ KNN & 0.989 & 3.28 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Pearson coefficient and RMSE. Figure 8: Etch cluster tool simulation connected to semiconductor fab host. Figure 9: Throughput against JIT1 with different WAC time (recipe_time = 180). As a result of the study, it was possible to obtain a significant throughput improvement result in the facility, in which JIT1 optimization through simulation was applied. It can be assumed that similar results can be obtained in actual semiconductor fabs. This study confirms that it is possible to predict and apply optimization parameters by analyzing the data generated from the etch cluster facility in the semiconductor fab using the ML-based algorithm in the fab. Figure 10 is the result of working to achieve maximum productivity through the methodology presented herein. You can check the error of the target by comparing it with the engineer's manual analysis and result derivation process. Through simulation, you can check the throughput result. The host's command to change the value of JIT1 to 58 is accepted, and the simulator is shown in Figure 10. The simulation result of Table 3 can be obtained. The value of JIT1, before being modified to 58, was set to 30, and without the modification, it showed a throughput of 37.06. However, by modifying to 58, it showed a throughput of 38.20, and it was confirmed that it would have better productivity than before. The calculated maximum value is an idealized maximum throughput value calculated by Equation (1). In this section, we propose the utility of JIT1 optimization using ML-based data analysis using the KNN algorithm. When the user inputs process time and WAC time using the chatbot, the optimal JIT1 value is given. Through this chatbot service, the user can know the appropriate JIT1 according to the equipment's various products, that is, the products of variable process time and WAC time, and can apply it to the equipment. Figure 10 shows the result of improving the productivity of the cluster facility by changing the optimal JIT1 obtained by the user through the KNN ML analysis through the host. The chatbot service is shown on the right side of Figure 11. Implementing the chatbot in the simulation environment can inform users of the optimal JIT1 value according to various process times and WAC times in real time and be an appropriate alternative to the increasingly diversified production environment.