A Frequency Domain Self-Attention Guided Multi-Scale Inverse 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 Multi-Scale Inverse Lithography Technology[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT251382

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

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

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A Frequency Domain Self-Attention Guided Multi-Scale Inverse 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 Programme (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 Effects (OPE) in lithographic processes cause printed patterns on wafers to deviate from target layouts, necessitating Optical Proximity Correction (OPC) through mask optimization prior to exposure. Traditional rule-based OPC methods suffer from significant accuracy degradation when handling complex layouts, while model-based OPC approaches incur high computational cost. In recent years, deep learning--based methods have been introduced to accelerate mask generation; however, their limited receptive fields hinder effective modeling of long-range optical interference effects, thereby constraining optimization accuracy. To address these challenges, this work proposes a Frequency Domain Self-Attention Guided Multi-Scale Inverse Lithography Technology (FMS-ILT), which jointly models local geometric details and global optical interactions, leading to improved printed image fidelity, edge placement accuracy, and process robustness. Methods FMS-ILT adopts a residual convolution--based multi-scale encoder--decoder architecture, where shallow layers extract fine-grained geometric features such as edges and corners, while deeper layers capture large-scale layout context. Residual blocks and multi-level skip connections are employed 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 enabled via the Fourier transform, and the resulting attention responses are 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 is used to jointly learn mask geometry and imaging consistency, providing physically meaningful initialization. During main training, lithography simulation is applied under nominal, maximum, and minimum process conditions to further refine mask optimization under physical constraints. Results and Discussions The comparison results with baseline models are summarized in Tables 2 and 3. Our method is set as the reference (Ratio = 1), and all experiments are conducted on the LithoBench dataset. In terms of overall imaging $ \mathcal{L}2 $ error, our method achieves the lowest value of 19,998, outperforming baseline models by 2%–107%. For the process robustness metric Process Variation Band (PVB), GAN-OPC obtains the best result of 19,156, which is 31% lower than ours; however, its $ \mathcal{L}2 $ error and EPE are 107% and 1115% higher, respectively, indicating an imbalance between imaging fidelity and edge accuracy. The remaining baseline models exhibit PVB performance comparable to ours. Regarding Edge Placement Error (EPE), our method also demonstrates a significant advantage, achieving an average EPE of 1.95, which is 47%–1115% lower than the baselines. These improvements can be attributed to three key factors: (1) a multi-scale encoder–decoder fusion mechanism that effectively integrates local and global features, (2) the combination of attention mechanisms and frequency-domain operations to guide the model toward critical regions, and (3) a dual-branch pretraining strategy that injects physical priors into the network. With these modules jointly contributing, FMS-ILT achieves more balanced and superior performance in imaging fidelity, process stability, and edge accuracy. Conclusions This work proposes a Frequency Domain Self-Attention Guided Multi-Scale Inverse Lithography Technology (FMS-ILT). The model adopts a residual convolution--based multi-scale encoder--decoder architecture to extract rich spatial features and incorporates a frequency-domain self-attention mechanism to jointly model local geometric details and global optical interference characteristics. A two-stage training strategy is employed. In the pretraining stage, a dual-branch task of mask generation and target image reconstruction is used to enhance 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 demonstrate that FMS-ILT achieves superior performance in terms of $ \mathcal{L}2 $, PVB, and EPE metrics, effectively improving printed image quality and providing a feasible and efficient solution for computational lithography.
- Mask optimization,
- Inverse lithography,
- Frequency Domain Self-Attention,
- Multi-Scale

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References(25)

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