发表机构
Nearfield Instruments, B.V.（近场仪器有限公司）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文提出一种基于神经网络的SPM图像分辨率增强框架，通过迁移学习实现小数据集下的模型复用，并在200nm及100nm节距结构上验证了其有效性。
AI 中文摘要
先进半导体节点正在推动特征尺寸的极限，并需要在不影响在线工艺控制所需吞吐量的情况下，具备亚纳米分辨率的计量能力。近期，能够满足这些需求的高通量扫描探针显微镜（SPM）计量与检测工具已推向市场，并已通过认证可用于大规模生产（HVM）。虽然创新的测量方法和工具架构已使吞吐量实现了飞跃式提升，但进一步缩短成像时间的下一步可以通过应用机器学习来增强所测量图像的分辨率，从而提取相关参数。在本工作中，我们提供了一个通用框架，用于设计和应用基于神经网络的图像分辨率增强器来处理SPM图像。我们利用在节距为200纳米的线/空间结构上进行的测量，展示了该框架的有效性。为了提高预先开发的预训练模型的可重用性，我们还利用了迁移学习，并表明对于略有不同的结构，可以使用在节距为100纳米的线/空间结构上进行的较小测量数据集来重新训练和校准新模型。
英文摘要
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.
