School of Electrical Engineering, Korea University, Seoul, Korea.
Sci Rep. 2023 Mar 6;13(1):435. doi: 10.1038/s41598-023-27449-y.
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.
由于实际测试的成本，模拟在器件设计中的重要性日益增加。随着模拟分辨率的提高，模拟的准确性也随之提高。然而，高分辨率的模拟并不适合实际的器件设计，因为随着分辨率的提高，计算量会呈指数级增加。在这项研究中，我们引入了一种使用低分辨率计算值来预测高分辨率结果的模型，该模型成功地以低计算成本实现了高模拟精度。快速残差学习超分辨率 (FRSR) 卷积网络模型是我们引入的一种可以模拟光的电磁场的模型。在特定情况下，我们的模型在对二维狭缝阵列进行超分辨率处理时实现了高精度，并且比模拟器的执行速度快约 18 倍。为了减少模型训练时间并提高性能，所提出的模型通过使用残差学习和后上采样方法来减少计算，使用高分辨率图像恢复来显示最佳准确性 (R2: 0.9941)。与使用超分辨率的模型相比，它的训练时间最短（7000s）。该模型解决了器件模块特性的高分辨率模拟的时间限制问题。
