Thermal aberration of a projection lens is caused by lens heating and leads to degradation of image quality. Two tasks have been undertaken in this article: first, analysis of the impact of single aberration on imaging quality, and second, use of machine learning to calculate the combination of Zernike aberration coefficients to reduce the influence of aberration on the process window. The impact of aberration on deep ultraviolet (DUV) lithography imaging has been analyzed separately for 16 Zernike terms. The influence of single Zernike coefficient on depth of focus, mask error enhancement factor, and image log slope is comprehensively analyzed by source mask optimization. However, more than one Zernike term is present in the aberration of a real DUV optics. A fully connected neural network (FCNN) model based on machine learning is widely used in the screening of multivariate experiments and can solve the problem of the large number of input variables and the random values of the input data. Its network structure is simple to debug, with fast screening speed and high precision. To make reasonable budget for aberration, an FCNN model is used to find some combinations of Zernike coefficients for different lithographic performance indicators. This implies that the FCNN model has the ability to select a better Zernike combination while each individual Zernike term value falls in the range of −30 to 30 mλ, which plays a guiding role in DUV optics design.
With the continuous improvement of chip integration level, resolution enhancement techniques (RET) has become one of the important technologies to promote the continued development of process nodes, such as source mask optimization (SMO), optical proximity correction (OPC) and sub-resolution assist feature (SRAF). Model-based SRAFs are generated by the guidance of the Continuous Transmission Mask (CTM) or SRAF Guidance Map (SGM). During operation, a threshold combined with the ridge is used to extract a suitable location for SRAFs. However, this generation method ignores many details, resulting in the need to constantly adjust SRAFs referring to the SGM/CTM in the subsequent optimization process, which will undoubtedly increase the simulation time. Therefore, we propose a contour line based SRAFs generation method. The extreme value region and the gradient of CTM/SGM will be displayed intuitively, so that more precise positions can be obtained at the initial SRAFs extraction. The SRAFs will be extracted from the extreme value region, and have a distribution similar to the final result. The gradient of the contour line can also be referenced in the following steps to guide the SRAF cleanup. During cleanup process, the SRAFs at high altitude region will have higher priorities to ensure the image quality of main patterns. Another advantage of this method is that when extracting rules from the model-based method, different SRAF priorities can be set according to the contour line and be used in the rules, so as to improve the accuracy of rule-based SRAFs.
The effective test pattern is a crucial component for lithography process optimization such as Source Mask Optimization (SMO) and Optical Proximity Correction (OPC). The conventional parameterized test patterns cannot represent various contexts of patterns, thus sample patterns extracted from layout become an alternative option. This paper introduces a sample patterns extraction method based on the hierarchical clustering algorithm, according to the geometric characteristics. Meanwhile, an improved HLAC-based method is applied to the layout patterns at the stage of feature extraction for accurate characterization. The method can reduce the number of test patterns while maintaining high coverage of layout’s geometric features. The lithography process window is analyzed to validate the effectiveness of the patterns clustering flow. Moreover, the comparison between the spectrums of sample patterns and original layout also indicates that the proposed sampling method preserve a sufficient coverage of layout’s optical characteristics. Pattern extraction method in this paper could provide a candidate solution for fast test pattern generation with high coverage for lithography process exploration.
The placement and size of SRAF (sub-resolution assisted feature) can greatly affect the overlapped process window. The time-consuming inverse lithography technology (ILT) can provide the co-optimization for both main pattern and SRAFs, which can guarantee the results with high precision. Rule-based SRAF (RBSRAF) offers the efficient application in large scale layout, which relies mostly on the design of test patterns and the corresponding empirical data on wafer. Our paper demonstrates a methodology of SRAF rule extraction and insertion based on ILT. The SRAF rules are extracted from the results of ILT and inserted by the RBSRAF, which ensures the reliability of the SRAF rules and shortens the development cycle. The hotspots areas with substandard process variation (PV) band are then repaired by ILT tools. Besides, the SRAF printing model can further refine the placement and dimension. The experiment results validate the feasibility of our methodology to be applied in large scale layout finally.
For 1xnm node and beyond, even Extreme Ultraviolet Lithography (EUV) technology, the serious geometries distortions of the wafer patterns at new process are forcing chipmakers and foundries to utilize model-based SRAFs for ensuring the accuracy and manufacturability of the chips. Model-based Sub-Resolution Assistant Feature (SRAF) is based on inverse lithography (ILT), which is accurate but time-consuming. Therefore, it is necessary to extract the SRAF rules from model-based results and apply them to full chip layout. In this paper, we put forward a methodology of 2D SRAF rule extraction based on model-based results. We can describe and locate the SRAFs by introducing Projection Region, because it reflect the relationship between the SRAFs and main patterns. And the new concept Elongation can make the properties of SARFs more clearly. The experimental results show that the proposed method can extract the 2D SRAFs accurately and output the rules in a general format. The rule simplifying step can decrease the quantity of 2D SRAF rules through the identification and process of symmetry.
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