The use of reflective optical systems is essential to acquiring high-resolution image quality in aerospace applications that observe distant objects. The geometric shapes of large-aperture reflective optical systems can be deformed depending on various operating and space environments, which deformation consequently affects optical performance. In this study, we predict the image quality of a reflective aerospace optical system according to various environmental changes. In particular, the shape deformation due to vibration and heat generated from the launch vehicle was mainly observed, and the effect on gravity was also considered. The variations of image quality, such as Modulation Transfer Function (MTF) and wave-front error (WFE), were also observed by importing the deformed shapes into the optical simulation tool. This study is intended to provide approaches to reduce the cost and lead time to develop aerospace optical systems.

As the size of semiconductor devices gradually decreases, it is important to measure and analyze semiconductor devices, to improve the image quality of semiconductors. We use VDSR, one of the Super-Resolution methods to improve the quality of semiconductor devices’ SEM images. VDSR is also a convolutional neural network that can be optimized with various parameters. In this study, a VDSR model for semiconductor devices’ SEM images was optimized using parameters such as depth of layers and amount of training data. Meanwhile, the quantitative evaluation and the qualitative evaluation did not match at the low scale factor. To solve this problem, we proposed an MTF measurement method using the slanted edge for better quantitative evaluation. This method was verified by comparing the results with the PSNR and SSIM index results, which are known as quality indicators. Based on the results, it was confirmed that using the MTF value could be a better approach for the evaluation of SEM images of the semiconductor device than using PSNR and SSIM.