Scanning Electron Microscopy (SEM) technology makes use of an electron beam (e-beam) with wide energy range from 0.1 to 50 keV, so it is possible to measure wafers from surface to deep and buried structures. Due to its superior accuracy, it is widely used for in-line metrology and inspection (MI) process. As devices scaling down for performance enhancement, the MI process became inaccurate due to the target structure shrinkage and complicated electrons’ behavior inside it. To overcome the challenges, accurate simulation tool is required to understand its underlying mechanism theoretically. In this paper, we propose a unifying framework for simulating SEM operation by implementing Nebula e-beam computing algorithm on the Technology Computer-Aided Design (TCAD) environment. The proposed framework integrates various physics models including the scattering and transport behaviors of electrons, which enables to calculate the important trajectories of electrons in the most important regions of wafers. In addition, it gives an expandability on further integration thanks to the computability of TCAD environment. We validate the proposed framework with demonstrating key applications on real products.
KEYWORDS: Machine learning, Data modeling, Semiconducting wafers, Transmission electron microscopy, Metrology, Overfitting, Principal component analysis
With the increasing complexity of 3-D semiconductor structures, the use of optical critical dimension (OCD) metrology has become a popular solution due to its accuracy and fast inference time. Machine learning has been widely adopted in this field to further improve the efficiency and precision of OCD metrology. Especially for high aspect ratio structures such as DRAM and VNAND, where the required computing power for physical modeling increases exponentially, the importance of machine learning with reference data is crucial. However, one significant challenge of the machine learning-based metrology under rapidly changing process condition is the limitation of available labeled data, which causes overfitting and decreases recipe reliability in the manufacturing process as the cost of wafer consumption increases. To utilize machine learning algorithms in mass production, the development of robust algorithms that can be optimized with few-shot data is required. In this paper, we propose a few-shot machine learning algorithm that includes i) wafer-level statistical information-based data augmentation and ii) anomaly detection to automatically remove data with measurement errors. The proposed algorithm shows superior accuracy, repeatability, and in-wafer uniformity compared to the benchmark algorithm in tests with manufacturing phase data. Additionally, this robustness can be sustained with the minimum amount of data in metrology, as only 9 reference training data are used on three design of experiment (DoE) wafers. The proposed optimized solution is expected to contribute to the reduction of measurement costs and production yields of highly complicated 3D semiconductor structures.
Theoretical lithography performance prediction of photoresist material has important role to design better material but the exact prediction was still difficult because there are too many conditions to be considered together. We investigated the EUV-induced photochemical reactions of conventional triphenylsulfonium (Ph3S+; TPS) PAG-cation in both “electron-trapping” and “internal excitation” mechanisms using atomic-scale materials modeling. By obtaining full energy profiles of protonation process of TPS molecule, we could find that the acid generation yield strongly depends on two main factors: the LUMO of PAG-cation in which the lower LUMO of PAG-cation, the reduction step of PAG-cation is easier and the proton (H+) dissociation ability (pKa) at the ortho-positions of thiol ether fragment cation(Ph2S+), in which lower pKa will give high acid generation. By matching computational analysis with experimental results, we developed a two-parameter model to predict the EUV exposure Dose from the target PAG–cation’s LUMO and pKa of thiol ether-derivatives. We applied our new model to other three sets of TPS samples and they also shows good correlation with experimental data. Finally, we proposed a strategy to design new PAG molecules for sensitivity improvement by functionalization of TSP-cation with electron donating group. Our new strategy can be a powerful tool to design novel PAG cation for EUV photoresist for improving Resolution-LER-Sensitivity trade-off.
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