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Mask 3D effects distort diffraction amplitudes from EUV masks. In the previous work, we developed a CNN which predicted the distorted diffraction amplitudes very fast from input mask patterns. The mask patterns in the work were restricted to Manhattan patterns. In general, the accuracy of neural networks depends on their training data. The CNN trained by Manhattan patterns cannot be used to general mask patterns. However, our CNN architecture contains 70 M parameters, and the architecture itself could be applied to general mask patterns. In this work, we apply the same CNN architecture to mask patterns which mimic iN3 logic metal or via layers. Additionally, to study more general mask patterns, we train CNNs using iN3 metal/via patterns with OPC and curvilinear via patterns. In total we train five different CNNs: metal patterns w/wo OPC, via patterns w/wo OPC, and curvilinear via patterns. After the training, we validate each CNN using validation data with the above five different characteristics. When we use the training and validation data with same characteristics, the validation loss becomes very small. Our CNN architecture is flexible enough to be applied to iN3 metal and via layers. On the other hand, using the training and validation data with different characteristics will lead to large validation loss. The selection of training data is very important to obtain high accuracy. We examine the impact of mask 3D effects on iN3 metal layer. Large difference is observed in T2T CD calculated by thin mask model and thick mask model. This is due to the mask shadowing effect at T2T slits. Our CNN successfully predicts T2T CD of thick mask model, which is sensitive to the mask 3D effect.
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