Grid-wise simulation acceleration of the electromagnetic fields of 2D optical devices using super-resolution

Jangwon Seo, Insoo Kim, Junhee Seok

Research output: Contribution to journalArticlepeer-review

6 Citations (Scopus)


The significance of simulation has been increasing in device design due to the cost of real test. The accuracy of the simulation increases as the resolution of the simulation increases. However, the high-resolution simulation is not suited for actual device design because the amount of computing exponentially increases as the resolution increases. In this study, we introduce a model that predicts high-resolution outcomes using low-resolution calculated values which successfully achieves high simulation accuracy with low computational cost. The fast residual learning super-resolution (FRSR) convolutional network model is a model that we introduced that can simulate electromagnetic fields of optical. Our model achieved high accuracy when using the super-resolution technique on a 2D slit array under specific circumstances and achieved an approximately 18 times faster execution time than the simulator. To reduce the model training time and enhance performance, the proposed model shows the best accuracy (R2: 0.9941) by restoring high-resolution images using residual learning and a post-upsampling method to reduce computation. It has the shortest training time among the models that use super-resolution (7000 s). This model addresses the issue of temporal limitations of high-resolution simulations of device module characteristics.

Original languageEnglish
Article number435
JournalScientific reports
Issue number1
Publication statusPublished - 2023 Dec

Bibliographical note

Funding Information:
This work was supported by Samsung Electronics Co., Ltd [grant number IO201214-08149-01] and the National Research Foundation of Korea [Grant Number NRF-2022R1A2C2004003].

Publisher Copyright:
© 2023, The Author(s).

ASJC Scopus subject areas

  • General


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