用神经网络提升扫描探针显微镜图像分辨率，服务先进半导体节点量测
Image resolution enhancement for advanced semiconductor nodes
半导体量测圈的朋友可以看看：用神经网络把 SPM 图像分辨率提上去，还靠迁移学习省数据，200nm 和 100nm 两种结构都做了实测。
论文面向先进制程节点的在线量测需求，提出一个基于神经网络的 SPM 图像分辨率增强框架。该方法在 pitch 为 200 nm 的 Line/Space 结构实测数据上验证了有效性。团队还利用迁移学习，用 pitch 为 100 nm 结构的较小数据集完成再训练与校准，实现预训练模型复用。目标是在不牺牲通量的前提下达到亚纳米级分辨率，缩短成像时间。
Image resolution enhancement for advanced semiconductor nodes
Advanced semiconductor nodes are pushing the limits of feature sizes and require metrology with sub-nm resolution without compromising on the throughput as needed for in-line process control. Recently, high-throughput scanning probe microscopy (SPM) based metrology and inspection tools capable of meeting these needs have been introduced to the market and qualified for use in HVM. While innovative measurement methods and tool architecture have allowed for a leap of improvement in throughput, the next step in further reducing imaging time can be obtained through the application of machine learning for enhancing the resolution of measured images for extraction of relevant parameters. In this work, we provide the general framework under which a neural network-based resolution enhancer is designed and used for SPM images. We showcase the effectiveness of this framework using measurements performed on Line/Space structures with a pitch of 200 nm. For the reusability of a pre-developed pre-trained model, we additionally leverage transfer learning and show that a new model for slightly differing structures can be re-trained and calibrated with a smaller data set of measurements performed on Line/Space structures with a pitch of 100 nm.
