# Uniform Pressing Mechanism in Large-Area Roll-to-Roll Nanoimprint Lithography Process
## 4 Conclusions  
The purpose of this study is to reveal the contact pressure non-uniformity problem of the conventional R2R NIL system and to propose a system to improve it. Simple modeling, FEM analysis and experiments were conducted to reveal the problem. In order to improve the problem, a new pressing system was proposed, and the experiment and optimization \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Force (Input)** & **Left (\(I_{L}\))** & **Center 1 (\(I_{\mathrm{C1}}\))** & **Center 2 (\(I_{\mathrm{C2}}\))** & **Center 3 (\(I_{\mathrm{C3}}\))** & **Center 4 (\(I_{\mathrm{C4}}\))** & **Center 5 (\(I_{\mathrm{C3}}\))** & **Right (\(I_{\mathrm{R}}\))** \\ \hline Min (kgf) & 99.4 & 58.0 & 35.7 & 43.2 & 40.6 & 38.4 & 79.3 \\ Max (kgf) & 100.4 & 60.0 & 37.3 & 46.1 & 41.7 & 39.4 & 80.7 \\ Avg (kgf) & 100.0 & 59.0 & 36.5 & 44.5 & 41.3 & 38.8 & 79.9 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Optimized load value of Figure 11b. Figure 12: Optimal pressing for uniformity using multi regression analysis: (**a**) Output value with uniform pressing force (\(2\) kg\(\pm\)\(5\%\)); (**b**) Input value optimization result of input pushing force. Figure 13: Force distribution experiment results along rollers using regression analysis results. showed that the contact pressure was equalized. In the optimization process, machine learning regression analysis was used. In the conventional R2R NIL system, the pressing process is mainly performed by applying concentrated loads to both ends of the roller during the pressing process for imprinting. For high productivity, the axial length of the imprinting roller must be long; the contact area increases due to the lengthened roller, and a higher pressing force is required. If the conventional pressing process is applied to a large-area R2R NIL system, the pressure distribution on the contact area is not uniform during imprinting due to the bending of the roller. If the rubber skin of the roller is deformed, if there is a waviness error in the contact area or if rollers are misaligned, this non-uniformity of the contact pressure becomes severe. In this study, a new roller pressing system, which applies a distributed load to the entire roller, was applied. The proposed system is different from the method of applying concentrated loads at both ends, which is the conventional pressing method. In the conventional R2R NIL pressing process, the bending of the roller is predicted through mathematical modeling and simulation (FEM). Through actual experiments, it was confirmed that the conventional R2R NIL system has a non-uniform pressure distribution at an 85% level during the pressing process on the contact area. In addition, it was checked that the non-uniformity is not symmetrical, which means the other errors are engaged. A uniform pressing system was designed and implemented to perform pressing by individual and distributed load. It was confirmed that the pressure distribution uniformity of the contact area can be improved to a level of 33% by improving the bending of the roller via pressing the backup roller, which is the core mechanism of this system. A number of backup rolls were used to improve the uniformity of the pressure distribution, which was irregular due to the other errors from skin, waviness error in the contact area and misalignment. The effect of each backup roller on the pressure distribution of the contact area was learned and analyzed using regression analysis of supervised learning, a machine learning technique. Using the results of regression analysis, the optimum force of each backup roller was found. As a result of testing by applying the derived value, it was confirmed that the uniformity of the pressure distribution on the contact area can be improved to a level of 5%. In this research, to utilize the machine learning technique (regression), the pressure distribution value at the contact area was divided into three sections and averaged.