A Frequency Domain Self-Attention Guided MultiscaleInverse Lithography Technology

LUO Binling; WANG Ying; CAI Shuting

doi:10.11999/JEIT251382

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LUO Binling, WANG Ying, CAI Shuting. A Frequency Domain Self-Attention Guided MultiscaleInverse Lithography Technology[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT251382

Citation:

LUO Binling, WANG Ying, CAI Shuting. A Frequency Domain Self-Attention Guided MultiscaleInverse Lithography Technology[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT251382

LUO Binling, WANG Ying, CAI Shuting. A Frequency Domain Self-Attention Guided MultiscaleInverse Lithography Technology[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT251382

Citation:

LUO Binling, WANG Ying, CAI Shuting. A Frequency Domain Self-Attention Guided MultiscaleInverse Lithography Technology[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT251382

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A Frequency Domain Self-Attention Guided MultiscaleInverse Lithography Technology

doi: 10.11999/JEIT251382 cstr: 32379.14.JEIT251382

LUO Binling^{1, 2},
WANG Ying¹,
CAI Shuting^{1
,
,}

1.
School of Integrated Circuits, Guangdong University of Technology, Guangzhou 510006, China
2.
Guangxi Vocational College of Water Resources and Electric Power, NanNing 530013, China

Funds: Guangdong S&T Program (2022B0701180001)

Received Date: 2025-12-30
Accepted Date: 2026-05-14
Rev Recd Date: 2026-05-14

Available Online: 2026-06-03

Abstract

Abstract

Objective Optical Proximity Effect (OPE) in lithographic processes causes printed wafer patterns to deviate from target layouts. Therefore, Optical Proximity Correction (OPC) is required for mask optimization before exposure. Traditional rule-based OPC methods show reduced accuracy for complex layouts, whereas model-based OPC methods require high computational cost. Deep learning-based methods have recently been used to accelerate mask generation. However, their limited receptive fields make it difficult to model long-range optical interference, which restricts optimization accuracy. To address these limitations, this work proposes Frequency-Domain Self-Attention-Guided Multiscale Inverse Lithography Technology (FMS-ILT). The method jointly models local geometric details and global optical interference to improve printed image fidelity, edge placement accuracy, and process robustness. Methods FMS-ILT uses a residual convolution-based multiscale encoder-decoder architecture. Shallow layers extract fine geometric features, such as edges and corners, whereas deeper layers capture large-scale layout context. Residual blocks and multilevel skip connections are used to preserve high-frequency information and stabilize training. To overcome the limited receptive field of spatial convolutions, a Frequency-Domain Self-Attention Mechanism (FSAM) is introduced at the encoder output. Global feature interactions are modeled using the Fourier transform. The resulting attention responses are then mapped back to the spatial domain through the inverse Fourier transform to adaptively reweight feature representations. A two-stage training strategy is adopted. During pretraining, a dual-branch structure jointly learns mask geometry and imaging consistency, providing physically meaningful initialization. During main training, lithography simulation is applied under nominal, maximum, and minimum process corners to refine mask optimization under physical constraints. Results and Discussions The comparison results with baseline models are summarized in Tables 2 and 3. FMS-ILT is used as the reference method (Ratio = 1), and all experiments are conducted on the LithoBench dataset. For the overall imaging $ \mathcal{L}2 $ error, FMS-ILT achieves the lowest value of 19,998, outperforming the baseline models by 2%-107%. For Process Variation Band (PVB), GAN-OPC obtains the best value of 19,156, which is 31% lower than that of FMS-ILT. However, its $ \mathcal{L}2 $ error and Edge Placement Error (EPE) are 107% and 1 115% higher, respectively, indicating an imbalance between imaging fidelity and edge accuracy. The remaining baseline models show PVB performance comparable to that of FMS-ILT. For EPE, FMS-ILT also shows a clear advantage, achieving an average value of 1.95, which is 47%-1 115% lower than those of the baseline models. These improvements are mainly attributed to the multiscale encoder-decoder fusion mechanism, which integrates local and global features; the combination of attention mechanisms and frequency-domain operations, which guides the model toward critical regions; and the dual-branch pretraining strategy, which introduces physical priors into the network. These modules enable FMS-ILT to achieve balanced performance in imaging fidelity, process stability, and edge accuracy. Conclusions This work proposes FMS-ILT for mask optimization in computational lithography. The model uses a residual convolution-based multiscale encoder-decoder architecture to extract rich spatial features. It also incorporates FSAM to jointly model local geometric details and global optical interference. A two-stage training strategy is used. In the pretraining stage, mask generation and target image reconstruction are used as dual-branch tasks to improve the physical consistency between the mask and the printed image. In the main training stage, lithography simulation is introduced to further improve imaging accuracy and process robustness. Experimental results on the public LithoBench dataset show that FMS-ILT achieves strong performance in terms of L2, PVB, and EPE. The method improves printed image quality and provides a feasible and efficient solution for computational lithography.
- Mask optimization,
- Inverse lithography,
- Frequency Domain Self-Attention,
- Multiscale

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References(25)

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