Accurate prediction of EUV lithographic images and 3D mask effects using generative networks

Jinguji

DTCO and Computational Patterning II

2023

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.

Section: Introductionmentioning

confidence: 99%

Evaluation of CNN for fast EUV lithography simulation using iN3 logic mask patterns

Jinguji

DTCO and Computational Patterning II

2023

“…They are classified into three types depending on the target: near-field amplitude on the mask, [16][17][18][19] far-field diffraction amplitude at the pupil, 20 and image intensity on the wafer. 21,22 In our model, 20 a CNN is used to predict the far-field diffraction amplitude from the input mask pattern. Although the training of CNN takes a very long time (more than 1 day), the prediction time is very short, 0.05 s for 256 nm × 256 nm.…”

Section: Introductionmentioning

confidence: 99%

“…Recently, many attempts have been made to simulate the M3D effects using deep neural networks, such as convolutional neural network (CNN) or generative adversarial network. They are classified into three types depending on the target: near-field amplitude on the mask, 16 – 19 far-field diffraction amplitude at the pupil, 20 and image intensity on the wafer 21 , 22 . In our model, 20 a CNN is used to predict the far-field diffraction amplitude from the input mask pattern.…”

Section: Introductionmentioning

confidence: 99%

Evaluation of convolutional neural network for fast extreme ultraviolet lithography simulation using imec 3 nm node mask patterns

Jinguji

J. Micro/Nanopattern. Mats. Metro.

2023

Background: Mask 3D (M3D) effects distort diffraction amplitudes from extreme ultraviolet masks. In our previous work, we developed a convolutional neural network (CNN) that very quickly predicted the distorted diffraction amplitudes from input mask patterns. The mask patterns were restricted to Manhattan patterns.Aim: We verify the potentials and the limitations of CNN using imec 3 nm node (iN3) mask patterns.Approach: We apply the same CNN architecture in the previous work to mask patterns, which mimic iN3 logic metal or via layers. In addition, to study more general mask patterns, we apply the architecture to iN3 metal/via patterns with optical proximity correction (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.Results: When we use the training and validation data with the same characteristics, the validation loss becomes very small. Our CNN architecture is flexible enough to be applied to iN3 metal and via layers. The architecture has the capability to recognize curvilinear mask patterns. 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 for obtaining high accuracy. We examine the impact of M3D effects on iN3 metal layers. A large difference is observed in the tip to tip (T2T) critical dimension calculated by the thin mask model and thick mask model. This is due to the mask shadowing effect at T2T slits. Conclusions:The selection of training data is very important for obtaining high accuracy. Our test results suggest that layer specific CNN could be constructed, but further development of CNN architecture could be required.

“…Because the far-field amplitudes are described in momentum (wave vector) space and the source position corresponds to the incident momentum in Koehler illumination, our model naturally parametrizes the source position dependence of the amplitude. The third model 13 , 14 uses the image intensity on the wafer as the target of DNN. This model is much straightforward than other models because the image intensity is used in the following resist simulation.…”

Section: Introductionmentioning

confidence: 99%

Data augmentation in extreme ultraviolet lithography simulation using convolutional neural network

J. Micro/Nanopattern. Mats. Metro.

2022

Background: In the previous work, we developed a convolutional neural network (CNN), which reproduces the results of the rigorous electromagnetic (EM) simulations in a small mask area. The prediction time of CNN was 5000 times faster than the calculation time of EM simulation. We trained the CNN using 200,000 data, which were the results of EM simulation. Although the prediction time of CNN was very short, it took a long time to build a huge amount of the training data. Especially when we enlarge the mask area, the calculation time to prepare the training data becomes unacceptably long.Aim: Reducing the calculation time to prepare the training data.Approach: We apply data augmentation technique to increase the number of training data using limited original data. The training data of our CNN are the diffraction amplitudes of mask patterns. Assuming a periodic boundary condition, the diffraction amplitudes of the shifted or flipped mask pattern can be easily calculated using the diffraction amplitudes of the original mask pattern. Results:The number of training data after the data augmentation is multiplied by 200 from 2500 to 500,000. Using a large amount of training data, the validation loss of CNN was reduced. The accuracy of CNN with augmented data is verified by comparing the CNN predictions with the results of EM simulation.Conclusions: Data augmentation technique is applied to the diffraction amplitude of the mask pattern. The data preparation time is reduced by a factor of 200. Our CNN almost reproduces the results of EM simulation. In this work, the mask patterns are restricted to line and space patterns. It is a challenge to build several CNNs for specific mask patterns or ultimately a single CNN for arbitrary mask patterns.