We aimed to increase the processing area of the roll-to-roll (R2R) nanoimprint lithography (NIL) process for high productivity, using a long roller. It is common for a long roller to have bending deformation, geometric errors and misalignment. This causes the non-uniformity of contact pressure between the rollers, which leads to defects such as non-uniform patterning. The non-uniformity of the contact pressure of the conventional R2R NIL system was investigated through finite element (FE) analysis and experiments in the conventional system. To solve the problem, a new large-area R2R NIL uniform pressing system with five multi-backup rollers was proposed and manufactured instead of the conventional system. As a preliminary experiment, the possibility of uniform contact pressure was confirmed by using only the pressure at both ends and one backup roller in the center. A more even contact pressure was achieved by using all five backup rollers and applying an appropriate pushing force to each backup roller. Machine learning techniques were applied to find the optimal combination of the pushing forces. In the conventional pressing process, it was confirmed that pressure deviation of the contact area occurred at a level of 44%; when the improved system was applied, pressure deviation dropped to 5%.

In this work, we investigated the compensation of strain deviation in the machine direction of a web in the roll-to-roll process. As flexible devices have become popular, many researchers have begun to study roll-to-roll processes for the mass-production of flexible devices at low cost. In the continuous roll-to-roll process, an electronic circuit pattern is printed on the web while the web is transferring. Due to tension and Poisson’s ratio, a non-uniform strain distribution can occur in the web. This strain distribution occurs mainly at the center of the web and causes a register error in the machine direction. In this work, we designed a roller to minimize the strain deviation. The design of the compensation roller was optimized using the design of experiments (DOE) methodology and analysis of variance (ANOVA), and the compensation performance was verified through experiments and simulations. According to the results of a comparative experiment conducted to confirm the correcting performance of the optimized roller, the strain deviation in the machine direction decreased by approximately 48% with the proposed roller compared to that of the conventional roller.