For nanoimprint lithography (NIL), computational technologies are still being developed. Only a few simulators are applicable to the nanoimprint process, and these simulators are desired by device manufacturers as part of their everyday toolbox. In this paper, we introduce a new NIL process simulator which simulates the whole imprinting process, and evaluates the quality of the resulting resist film. To overcome the scale difference of each component of the system, which makes it difficult to calculate the process with conventional fluid structure interaction simulators, our simulator utilizes analytically integrated expressions which reduce the dimensions of the calculation region.
Computational technologies are still in the course of development for nanoimprint lithography (NIL). Only a few simulators are applicable to the nanoimprint process, and these simulators are desired by device manufacturers as part of their daily toolbox. The most challenging issue in NIL process simulation is the scale difference of each component of the system. The template pattern depth and the residual resist film thickness are generally of the order of a few tens of nanometers, whereas the process needs to work over the entire shot size, which is typically of the order of several hundred square millimeters. This amounts to a scale difference of the order of 106. Therefore, in order to calculate the nanoimprint process with conventional fluid structure interaction simulators, an enormous number of meshes is required, which results in computation times that are unacceptable. We introduce a process simulator which directly inputs the process parameters, simulates the whole imprinting process, and evaluates the quality of the resulting resist film for jet and flash imprint lithography process. To overcome the scale differences, our simulator utilizes analytically integrated expressions which reduce the dimensions of the calculation region. In addition, the simulator can independently consider the resist droplet configurations and calculate the droplet coalescence, thereby predicting the distribution of the non-fill areas which originate from the trapped gas between the droplets. The simulator has been applied to the actual NIL system and some examples of its applications are presented here.
For nanoimprint lithography, computational technologies are still being developed. In this paper, we introduce a new NIL process simulator which simulates the whole imprinting process, and evaluates the quality of the resulting resist film. To overcome the scale difference of each component of the system, which makes it difficult to calculate the process with conventional fluid structure interaction simulators, our simulator utilizes analytically integrated expressions which reduce the dimensions of the calculation region. Additionally, we report on the critical dimension uniformity of sub-20nm contact holes as a demonstration of pattern robustness and discuss advancements made in defectivity, throughput and overlay.
Computational technologies are still in the course of development for nanoimprint lithography (NIL). Only a few simulators are applicable to the nanoimprint process, and these simulators are desired by device manufacturers as part of their daily toolbox. The most challenging issue in NIL process simulation is the scale difference of each component of the system. The template pattern depth and the residual resist film thickness are generally of the order of a few tens of nanometers, while the process needs to work over the entire shot size, which is typically of the order of 10 mm square. This amounts to a scale difference of the order of 106. Therefore, in order to calculate the nanoimprint process with conventional fluid structure interaction (FSI) simulators, an enormous number of meshes is required, which results in computation times that are unacceptable. In this paper, we introduce a new process simulator which directly inputs the process parameters, simulates the whole imprinting process, and evaluates the quality of the resulting resist film. To overcome the scale differences, our simulator utilizes analytically integrated expressions which reduce the dimensions of the calculation region. In addition, the simulator can independently consider the positions of the droplets and calculate the droplet coalescence, thereby predicting the distribution of the non-fill areas which originate from the trapped gas between the droplets. The simulator has been applied to the actual NIL system and some examples of its applications are presented here.
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