This paper introduces a method for improving photomask CDU (Critical Dimension Uniformity) using machine learning. The CDU of a photomask is one of the key factors affecting device quality. To improve the CDU of photomask, local dosage corrections are performed on the writing tool. However, it is difficult for human to predict the amount of correction for entire writing area in advance. In particular, non-critical layer masks for memory device, which are often used in peripheral circuits, significant degradation in CD distribution has been observed. In this study, we evaluated the performance of several machine learning models, called GBDT (Gradient Boost Decision Tree), in predicting the CD distribution of non-critical layer masks. The motivation behind this research is that the consistent production of highly accurate photomasks leads to reduce costs in photomasks and device development. Methods for increasing the accuracy of the model are also presented. Not only numerical data but also categorical data were used to generate the features used in the model. To avoid leakage problem of time series data, the data were divided into training, validation, and test data along the time sequence. Multiple models were used in ensemble to construct a highly accurate and stable model. In the product test, simulation results with CD correction using machine learning predictions showed a 20% improvement in CDU at the median compared to the conventional method.
Ultraviolet nanoimprint lithography (NIL) is a simple contact process that is attractive and promising process for high pattern fidelity, without blurring effect due to light scattering or acid diffusion in the resist. Specifically, complicated 3D patterns, fine 2D patterns, and fine 1D patterns can be formed in fewer process steps compared to those for optical lithography. On the other hand, there are fewer adjustment knobs for process tuning in NIL; therefore, it is necessary to introduce design restrictions customized for NIL to improve the process margin. Since pattern transfer is performed through filling of a resist having a finite volume, a design constraint considering filling property is required to reduce defect density and improve throughput. In this study, two types of design constraints are examined to address the NIL process margin problem. One is a NIL alignment mark design that satisfies both signal strength and filling characteristics. The other is a combination of the pattern coverage rule with wafer topography that achieves good filling characteristics under various substrate unevenness conditions. Experiment results were interpolated with NIL process simulations and common areas under various conditions were extracted to identify the design rules for achieving large process margins. By using a design flow that considers these rules, we believe that high volume manufacturing (HVM) yields can be increased considerably by reducing yield issues and reducing redesign loops.
Nanoimprint lithography (NIL) is a promising technology on next generation lithography for the fabrication of semiconductor devices. NIL is a one-to-one lithographic technology with a contact transfer methodology using templates. Therefore, critical dimension (CD) error and defect performance of templates has direct impact on wafer performance. The previous paper reported that the self-aligned double patterning (SADP) process on master template had better performance on resolution and defect performance [2]. In proceeding with development of SADP template process technology, we found that CD errors occurred in the area with a pattern density change. CD control over any pattern density is one of the critical issues. In this report, we have investigated the impact of the proximity effect correction (PEC) and fogging effect correction (FEC) parameters for electron beam writing on gap space and core space. It was found that the optimal PEC parameter for resist CD is not the best for the core space and the gap space. The resist CD is uniform, but there is a difference in resist shape on the local pattern density variation. It was also found that the core space had dependency on global pattern density even if the optimal FEC parameter for resist CD was applied. FEC can correct resist CD, but it cannot adjust resist shape. By using the optimal PEC and FEC parameters for SADP process, the gap space range of 0.6 nm and the core space range of 0.5 nm were successfully obtained.
An essential element of sub-15 nm nanoimprint lithography is to create fine patterns on a template. However, it is challenging to create sub-15 nm half-pitch patterns on a template by direct drawing with a resist, owing to poor resolution and low sensitivity. We are currently researching the development of sub-15 nm half-pitch patterns by applying self-aligned double patterning on a template. The defect density of the template has not yet reached a high-volume manufacturing level. The aim of our study is to achieve a defect density of less than 1 pcs/cm2 for sub-15 nm templates. To achieve this, we need to overcome stochastics-induced resist defects. We aim to determine the mechanism of defect formation by observing the details of the defects. We challenged resist-pattern inspections using a grazing-incidence coherent scatterometry microscope, which illuminated an extreme ultraviolet light to the resist pattern and detected the diffraction signal from the pattern. This study was conducted in collaboration with University of Hyogo and Kioxia Corporation. In this paper, we present the results of damage evaluations and resist-pattern inspections.
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