Search Results (2)

Micro- and nanopatterns perform unique functions and have attracted attention in various industrial fields, such as electronic devices, microfluidics, biotechnology, optics, sensors, and smart and anti-adhesion surfaces. To put fine-patterned products to practical use, low-cost patterning technology is necessary. Nanoimprint lithography (NIL) is a promising technique for high-throughput nanopattern fabrication. In particular, thermal nanoimprint lithography (T-NIL) has the advantage of employing flexible materials and eliminating chemicals and solvents. Moreover, T-NIL is particularly suitable for compostable and recyclable materials, especially when applying biobased materials for use in optics and electronics. These attributes make T-NIL an eco-friendly process. However, the processing time of normal T-NIL is longer than that of ultraviolet (UV) NIL using a UV-curable resin because the T-NIL process requires heating and cooling time. Therefore, many studies focus on improving the throughput of T-NIL. Specifically, a T-NIL process based on a roll-to-roll web system shows promise for next-generation nanopatterning techniques because it enables large-area applications with the capability to process webs several meters in width. In this review, the T-NIL process, roll mold fabrication techniques, and various materials are introduced. Moreover, metal pattern transfer techniques using a combination of nanotransfer printing, T-NIL, and a reverse offset are introduced. Full article

In the printing industry, defect detection is of crucial importance for ensuring the quality of printed matter. However, rarely has research been conducted for web offset printing. In this paper, we propose an automatic defect detection method for web offset printing, which consists of determining first row of captured images, image registration and defect detection. Determining the first row of captured images is a particular problem of web offset printing, which has not been studied before. To solve this problem, a fast computational algorithm based on image projection is given, which can convert 2D image searching into 1D feature matching. For image registration, a shape context descriptor is constructed by considering the shape concave-convex feature, which can effectively reduce the dimension of features compared with the traditional image registration method. To tolerate the position difference and brightness deviation between the detected image and the reference image, a modified image subtraction is proposed for defect detection. The experimental results demonstrate the effectiveness of the proposed method. Full article