Hirai, Y.; Tsukamoto, S.; Tanabe, H.; Kameyama, K.; Kawata, H.; Yasuda, M. Smart Systems for Material and Process Designing in Direct Nanoimprint Lithography Using Hybrid Deep Learning. Nanomaterials2022, 12, 2571.
Hirai, Y.; Tsukamoto, S.; Tanabe, H.; Kameyama, K.; Kawata, H.; Yasuda, M. Smart Systems for Material and Process Designing in Direct Nanoimprint Lithography Using Hybrid Deep Learning. Nanomaterials 2022, 12, 2571.
Hirai, Y.; Tsukamoto, S.; Tanabe, H.; Kameyama, K.; Kawata, H.; Yasuda, M. Smart Systems for Material and Process Designing in Direct Nanoimprint Lithography Using Hybrid Deep Learning. Nanomaterials2022, 12, 2571.
Hirai, Y.; Tsukamoto, S.; Tanabe, H.; Kameyama, K.; Kawata, H.; Yasuda, M. Smart Systems for Material and Process Designing in Direct Nanoimprint Lithography Using Hybrid Deep Learning. Nanomaterials 2022, 12, 2571.
Abstract
A hybrid smart process and material design system for nanoimprinting is proposed, which combines with a learning system based on experimental and numerical simulation results. Instead of carrying out extensive learning experiments for various condition, the simulation learning results are partially complimented where the results can be theoretically predicted by numerical simulation. In other word, the lacking data in experimental learning are complimented by simulation-based learning results. Therefore, the prediction of nanoimprint results under various conditions without experimental learning could be realized even for unknown materials. In this study, material and process design for a low-temperature nanoimprint process using glycerol-containing polyvinyl alcohol are demonstrated. Experimental results under limited conditions are learned to investigate optimum Glycerol concentrations and process temperatures. On the other hand, simulation-based learning is used to predict the dependence on press pressure and shape parameters. The prediction results for unknown Glycerol concentrations agreed well with the follow-up experiments.
Keywords
direct nanoimprint; process design; deep learning
Subject
Engineering, Industrial and Manufacturing Engineering
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
