Scanning electron microscopy (SEM) is indispensable in diverse applications ranging from microelectronics to food processing because it provides large depth-of-field images with a resolution beyond the optical diffraction limit. However, the technology requires coating conductive films on insulator samples and a vacuum environment. We use deep learning to obtain the mapping relationship between optical super-resolution (OSR) images and SEM domain images, which enables the transformation of OSR images into SEM-like large depth-of-field images. Our custom-built scanning superlens microscopy (SSUM) system, which requires neither coating samples by conductive films nor a vacuum environment, is used to acquire the OSR images with features down to ~80 nm. The peak signal-to-noise ratio (PSNR) and structural similarity index measure values indicate that the deep learning method performs excellently in image-to-image translation, with a PSNR improvement of about 0.74 dB over the optical super-resolution images. The proposed method provides a high level of detail in the reconstructed results, indicating that it has broad applicability to chip-level defect detection, biological sample analysis, forensics, and various other fields.

翻译：扫描电子显微镜（SEM）在微电子到食品加工等众多领域中不可或缺，因其能提供超越光学衍射极限分辨率的大景深图像。然而，该技术需对绝缘样品进行导电薄膜镀层并需真空环境。我们利用深度学习获取光学超分辨（OSR）图像与SEM域图像间的映射关系，从而将OSR图像转换为类似SEM的大景深图像。我们定制的扫描超透镜显微（SSUM）系统无需对样品进行导电薄膜镀层或真空环境处理，即可获取特征尺寸低至~80 nm的OSR图像。峰值信噪比（PSNR）和结构相似性指数测量值表明，该深度学习方法在图像到图像转换中表现优异，较光学超分辨图像PSNR提升约0.74 dB。所提方法在重建结果中呈现高保真细节，表明其在芯片级缺陷检测、生物样本分析、法医学及其他众多领域具有广泛适用性。