# Uniform Pressing Mechanism in Large-Area Roll-to-Roll Nanoimprint Lithography Process
## 3 Experiment for Uniform Pressure Distribution
### Improving Uniformity Using Multiple Rollers and Machine Learning  
The most commonly used machine learning algorithms automate the decision-making process by creating generalized models based on known examples. This approach is called supervised learning: it provides inputs and expected outputs to the algorithm, and the algorithm finds a way to produce the desired output from the given inputs. This type of learned algorithm can produce an appropriate output from an arbitrary input. In supervised learning, there are processes of classification and regression. Classification is the prediction of one from several predefined, possible class labels. Regression predicts a continuous number. It is easy to distinguish between regression and classification problems based on whether the output has continuity or not [23]. In the large-area R2R uniform pressing system, the load value applied by the actuators during the pressing process was set as an input value and the pressure distribution value at the contact area was set as an output value. Regression analysis was performed assuming that the output values have continuity. The output value of the input value without the experimental result was predicted through the regression analysis supervised learning of the input value obtained by a relatively small number of experiments. For this, the sensor for measuring the pressure distribution on the contact area was replaced with an electronic pressure measurement sensor (Model 5555, Tekscan), which can be analyzed in real time. The electronic pressure measurement sensor is shown in Figure 8a; this sensor uses the same measurement principle as the strain gage. The experiment was set up as shown in Figure 8b; three sensors were connected to measure the entire area of contact between the master roller and the imprint roller. In addition, the un-measured area between the connected sensors was predicted and measured using an interpolation method. In this study, we try to make the contact pressure of the rollers uniform under static conditions. The contact pressure of the master roller is divided into the left (\(O_{L}\)), the center (\(O_{C}\)) and the right (\(O_{R}\)), and the uniformity is evaluated based on the average value of \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Pressure Value** & **(a)** & **(b)** & **(c)** \\ \hline Min (MPa) & 1.36 & 1.19 & 1.24 \\ Max (MPa) & 1.96 & 1.72 & 1.65 \\ Uniformity \(\left(\frac{Max-Min}{Min}\right)\) & 44\% & 44\% & 33\% \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Maximum and minimum pressure values and non-uniformity with only the center roller. Figure 8: Large-area R2R uniform pressing system used in experiments: (**a**) Electronic pressure measurement sensor; (**b**) Setup of electronic pressure measurement sensors on large-area R2R uniform pressing system. these areas. If the deviation of these values is not large, it is assumed that the uniformity is secured. To reduce this deviation, the pushing force of the left pneumatic cylinder (\(I_{L}\)), the pushing forces of each backup roller (\(I_{C1}\)\(-\)\(I_{C5}\)) and the pushing force of the right pneumatic cylinder (\(I_{R}\)) should be optimally selected. Therefore, the inputs of supervised learning were set as \(I_{L}\), \(I_{C1}\)\(-\)\(I_{C5}\) and \(I_{R}\), and the outputs were set as \(O_{L}\), \(O_{C}\) and \(O_{R}\). If one output continuously changes with respect to one input, it can be determined that several outputs for several inputs also change continuously. Here, \(I_{C3}\) is selected as the input and \(O_{C}\) is selected as the output. The relationship between them was regression-analyzed using the random forest method. The experimental condition is such that the sum of the input pushing forces is 400 kgf, which is the sum of the forces applied by the pneumatic cylinders installed at both ends of the imprinting roller and the servo motors of the backup rollers.