# ML-Based JIT1 Optimization for Throughput Maximization in Cluster Tool Automation
## 2 Related Work
### Throughput Optimization through ML  
Studies on data analysis and prediction methods using ML VM, as shown in Figure 5, are already in progress in various fields. However, this paper studied an efficient method of using ML systems in a semi-standard environment. This study confirmed the possibility of real-time data analysis and productivity improvement. Throughput data generated by a cluster tool is transfer to the VM and analyzed using ML. As a result of analysis and prediction using ML, the optimal JIT1 value is reported to the host, and the host commands a change in the JIT1 parameter of the cluster tool when producing the following production lot. This is a way to maximize productivity through more efficient work by presenting cases in which optimal parameters can be predicted and applied through data analysis using ML-based algorithms in the fab, considering the characteristics of a semiconductor fab with strict security. The productivity analysis of the cluster includes the data collection and analysis described in Section 3. The analyzed data manually create a test scenario including process time, WAC time, swap time, and JIT1.  
Figure 5: JIT1 optimization of semiconductor cluster tool (proposal).  
In Table 1, you can see a process scenario and the simulation result. In this paper, we will present an automated method through ML analysis based on the dataset of the cluster facility through simulation and a method to obtain the maximum productivity of the semiconductor etch cluster facility through a real-time analysis and application process.  
The following methods are used to improve the throughput of the etch cluster facility of the semiconductor manufacturing fab. The first method classifies and analyzes the motion data of various components generated in the cluster to create and reproduce simulation scenarios or compare and analyze scenario simulation data and field data to find parameters that need improvement and apply them to facilities to identify problems and solve them. The second method calculates the maximum throughput under the current facility configuration and sets it as a target, compares it with the facility's data, finds the problem, finds the variable to be improved, and applies it to the facility. Such comparative analysis requires significant time and effort through copying and moving data, replicating simulators, and post-comparison. In addition, the application of variables in the cluster is a manual variable adjustment method used by engineers. It is possible to first analyze the log generated in the equipment to change the JIT1 to obtain the maximum productivity of the equipment in the current semiconductor manufacturing fab. Through this, it is possible to obtain maximum efficiency with the existing method.  
The method proposed in this paper is to automate the data collection, data analysis, and deduction of variables, to obtain maximum productivity and reflection in JOB production planning. The data collection uses the event-based file transfer system to transfer files to the VM when they are created. In addition, data analysis uses an ML-based analysis algorithm based on the accumulated data. Figure 6 shows the data analysis result based on the KNN algorithm as a graph. Based on this, it is possible to obtain the optimal JIT1 value under various production environments. JIT1 derived in this way is applied to the facility through the host.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Process Time** & **WAC Time** & **Tput (JIT = 10)** & **Tput (JIT = 15)** & **\(\dots\)** \\ \hline
120.00 & 40.00 & 47.51 & 46.92 & \(\dots\) \\
120.00 & 50.00 & 44.48 & 46.87 & \(\dots\) \\
120.00 & 60.00 & 44.85 & 44.59 & \(