Printed electronics is one of the emerging technologies owing to its low cost and productivity. Recently, many researchers tried to adapt printing technology to the fabrication of fine electronic patterns on flexible substrates, including the gate line of thin film transistors. In this study, we fabricated a flexible cliché using the nanoimprint process and used it in reverse offset printing. Then, we analyzed the effect of the surface energy of the imprinted cliché on process parameters, such as printing speed and rolling direction. We showed that the productivity of the process and quality of printed pattern can be considerably enhanced by controlling the surface energy of the cliché. When a flexible cliché is manufactured using a resin with a surface energy considerably different from that of the blanket, the ink can be detached easily and fine patterns can be engraved successfully regardless of the pattern shape.

Attempts have been made to introduce microstructures or wrinkles into the elastomer surface to increase the sensitivity of the elastomer. However, the disadvantage of this method is that when a force is applied to the pressure sensor, the contact area with the electrode is changed and the linear response characteristic of the pressure sensor is reduced. The biggest advantage of the capacitive pressure sensor using an elastomer is that it is a characteristic that changes linearly according to the change in pressure, so it is not suitable to introduce microstructures or wrinkles into the elastomer surface. A method of increasing the sensitivity of the capacitive pressure sensor while maintaining the linearity according to the pressure change is proposed. We proposed a bubble-popping PDMS by creating pores inside the elastomer. The sensitivity of the pressure sensor made of the bubble-popping PDMS was approximately 4.6 times better than that of the pressure sensor without pores, and the pressure sensor made of the bubble-popping PDMS showed a high linear response characteristic to the external pressure change. These results show that our pressure sensor can be used to detect applied pressures or contact forces of e-skins.

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 paper, experimental validation of high precision web handling for a two-actuator-based roll-to-roll (R2R) system is presented. To achieve this, the tension control loop is utilized to regulate the tension in the unwinder module, and the velocity loop is utilized to regulate the web speed in the rewinder module owing to the limitation of the number of actuators. Moreover, the radius estimation algorithm is applied to achieve an accurate web speed and the control sequence of the web handling in the longitudinal axis is developed to manipulate the web handling for convenience. Having these, the tension control performances are validated within ±0.79, ±1.32 and ±1.58 percent tension tracking error and 1.6, 1.53 and 1.33 percent web speed error at the speeds of 0.1 m/s, 0.2 m/s, and 0.3 m/s, respectively. The tension control performance is verified within ±0.3 N tracking error in the changes of the reference tension profile at 0.1 m/s web speed. Lastly, the air floating roller is used to minimize the friction terms and the inertia of the idle roller in the tension zone so that tension control performance can be better achieved during web transportation.

Micro- and nanofabrication on polymer substrate is integral to the development of flexible electronic devices, including touch screens, transparent conductive electrodes, organic photovoltaics, batteries, and wearable devices. The demand for flexible and wearable devices has spurred interest in large-area, high-throughput production methods. Roll-to-roll (R2R) nanoimprint lithography (NIL) is a promising technique for producing nano-scale patterns rapidly and continuously. However, bending in a large-scale R2R system can result in non-uniform force distribution during the imprinting process, which reduces pattern quality. This study investigates the effects of R2R imprinting module geometry parameters on force distribution via simulation, using grey relational analysis to identify optimal parameter levels and ANOVA to determine the percentage of each parameter contribution. The study also investigates the length and force ratio on a backup roller used for bending compensation. The simulation results and the artificial neural network (ANN) model enable the prediction of nip pressure and force distribution non-uniformity along the roller, allowing the selection of the optimal roller geometry and force ratio for minimal non-uniformity on a specific R2R system. An experiment was conducted to validate the simulation results and ANN model.

In order to enhance the sensitivity of elastomers, pores were integrated into their structure. These pores facilitate the adjustment of thickness in response to external pressure variations, thereby improving the sensitivity of pressure sensors. Pores were introduced by emulsifying immiscible polydimethylsiloxane (PDMS) and water with a surfactant. By controlling the water content in the PDMS and water emulsion, we controlled the size, density, uniformity, and spatial distribution (2D or 3D) of the pores within the PDMS matrix. The presence of these pores significantly improved the sensitivity of PDMS under low external pressure conditions compared to high pressures. Specifically, porous PDMS exhibited approximately 10-times greater sensitivity under low-pressure conditions than non-porous PDMS. The effectiveness of porous PDMS was demonstrated through dynamic loading and unloading detection of a small Lego toy and monitoring of human heartbeats. These results highlight the efficacy of our pressure sensor based on porous PDMS, which is fabricated through a simple and cost-effective process using a PDMS and water emulsion. This approach is highly suitable for developing the ability to detect applied pressures or contact forces.