Title: TokMan:Tokenize Manhattan Mask Optimization for Inverse Lithography
Venue: NeurIPS 2025
Accepted: yes
Keywords: Structured Tokenization

Manhattan represenetation

Inverse Rendering
Abstract: Manhattan representations, defined by axis-aligned, orthogonal structures, are widely used in vision, robotics, and semiconductor design for their geometric regularity and algorithmic simplicity. In integrated circuit (IC) design, Manhattan geometry is key for routing, design rule checking, and lithographic manufacturability. However, as feature sizes shrink, optical system distortions lead to inconsistency between intended layout and printed wafer. Although Inverse Lithography Technology(ILT) is proposed to compensates these effects, learning-based ILT methods, while achieving high simulation fidelity, often generate curvilinear masks on continuous pixel grids, violating Manhattan constraints. Therefore, we propose TokMan, the first framework to formulate mask optimization as a discrete, structure-aware sequence modeling task. Our method leverages a Diffusion Transformer to tokenize layouts into discrete geometric primitives with polygon-wise dependencies and denoise Manhattan-aligned point sequences corrupted by optical proximity effects, while ensuring binary, manufacturable masks. Trained with self-supervised lithographic feedback through differentiable simulation and refined with ILT post-processing, TokMan achieves state-of-the-art fidelity, runtime efficiency, and strict manufacturing compliance on a large-scale dataset of IC layouts.


Peer reviews:
{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper presents TokMan, a novel approach for optical proximity correction (OPC) in semiconductor lithography that formulates mask optimization as a discrete sequence modeling problem. The key insight is to maintain Manhattan geometric constraints throughout the optimization process rather than treating them as post-hoc constraints.\n\nThe experimental evaluation on ICCAD2013 benchmarks shows significant improvements: >20% better edge placement error compared to state-of-the-art IL-ILT\n\nSTRENGTHS AND WEAKNESSES:\n## Strengths\n\n**Technical Quality:**\n- The core technical approach is sound - using diffusion transformers for sequence modeling of Manhattan geometries is well-motivated and properly executed. The mathematical formulation clearly connects forward lithography simulation to structured noise addition, making the denoising perspective natural.\n- Experimental validation is comprehensive with proper baselines (GAN-OPC, Neural-ILT, etc.) and industry-standard metrics (EPE, mask shots, TAT). The >20% EPE improvement and 4x mask shot reduction are substantial and well-documented.\n- Authors are honest about limitations, explicitly discussing scalability issues with SRAFs, evaluation only on sliced layouts, and need for industrial dataset validation.\n\n**Clarity and Organization:**\n- Paper is generally well-written with clear motivation and problem setup. The connection between Manhattan constraints and tokenization is well-articulated.\n- Method section logically progresses from segmentation → tokenization → training → post-processing.\n\n**Significance and Impact:**\n- First work to apply sequence modeling to lithography mask optimization - could inspire broader adoption of tokenization approaches in computational imaging.\n- Results demonstrate practical viability for semiconductor manufacturing, not just academic benchmarks.\n\n**Originality:**\n- Novel combination of diffusion models, transformers, and Manhattan geometric constraints. While individual components exist, their integration for this specific problem is original.\n- The \"tokenize everything\" perspective applied to semiconductor manufacturing is fresh and well-motivated.\n\n## Weaknesses\n\n**Technical Limitations:**\n- The segmentation algorithm description lacks sufficient detail for reproduction. The \"lithography-aware points segmentation\" process needs clearer algorithmic specification and parameter settings.\n- Limited theoretical analysis of why tokenization should work better than continuous optimization. The paper relies heavily on empirical validation without deeper theoretical insights.\n\n**Experimental Concerns:**\n- Evaluation limited to ICCAD2013 benchmarks which may not represent modern lithography challenges. More diverse and recent benchmarks would strengthen claims.\n- Training dataset is synthetically generated by \"rearranging polygons\" - unclear how well this represents real design patterns and whether the model would generalize to actual industrial layouts.\n- Some results may not align with those in the original referenced papers, such as the number of shots.\n\n\n**Minor issues**\n- Should the text in Figure 1 read \"w/ OPC\" instead of \"w/ OPT\"?\n\nThe paper would benefit from deeper theoretical analysis of why tokenization helps, more comprehensive evaluation on diverse datasets, and clearer algorithmic specifications for key components like the segmentation algorithm.\n\nQUESTIONS:\n- What is a clearer explanation of \"lithography-aware points segmentation\"?\n- What are the specific criteria for \"spatial alignment casting\" and corner detection?\n- How sensitive is overall performance to segmentation parameters?\n\nLIMITATIONS:\nYes.", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 3, "CLARITY": 3, "IMPACT": 3, "ORIGINALITY": 3, "DATE": "2025-06-30"}

comments: SUMMARY:
This paper proposes TokMan, a novel framework for inverse lithography that reframes mask optimization as a sequence modeling task. The method utilizes a Diffusion Transformer to generate masks that inherently comply with Manhattan geometry constraints. Trained in a self-supervised fashion using a differentiable simulator, TokMan demonstrates state-of-the-art performance on standard industry benchmarks in both pattern fidelity and manufacturability.

STRENGTHS AND WEAKNESSES:
Strengths:
1.The paper is well-written, and the proposed methodology is clearly explained.
2.The formulation of inverse lithography as a token-based sequence modeling problem is novel and original.
3.Extensive experiments show the outstanding effects of proposed framework over previous learning-bases works.


Weaknesses:
1. As shown in Figure 6, the framework's final performance is heavily dependent on an ILT post-processing step, which weakens the contribution of the core learning model. However, the details of the post-processing step are not explained.
2. The paper claims to be state-of-the-art but only compares to learning models that don't all focus on optimizing manufacturability. Comparing to more relevant works, such as "Fracturing-aware Curvilinear ILT via Circular E-beam Mask Writer", would be more convincing.
3. The ablation study only targets EPE, not mentioning #shots or TAT. The reason for the effect of hyper-parameters on EPE is not fully explained.

QUESTIONS:
1. Could you please elaborate on the details of post-processing?
2. Why does high heads num or dec layer num, as shown in figure 6, result in worse effect?

LIMITATIONS:
yes
TITLE: Official Review
CLARITY: 3
IMPACT: 3
ORIGINALITY: 3
DATE: 2025-07-01

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper introduces TokMan, a framework for Inverse Lithography Technology (ILT). The key problem the authors address is that existing deep learning-based ILT methods often produce masks with curvilinear features, which are difficult and expensive to manufacture and violate the industry-standard \"Manhattan\" (axis-aligned, orthogonal) geometry constraints.\nThe main contribution is to reframe the mask optimization problem as a sequence modeling task. TokMan first segments the target IC layout into a sequence of Manhattan-aligned points. It then uses a Diffusion Transformer (DiT) model to learn how to predict precise, axis-aligned positional corrections for these points. The model is trained in a self-supervised manner, using a differentiable renderer and a lithography simulator to provide feedback, thus eliminating the need for ground-truth corrected masks. The authors demonstrate that their method achieves state-of-the-art results on standard benchmarks.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths:\n\n1. The core idea of \"tokenizing\" a Manhattan layout is novel and significant. It cleverly translates a geometric correction problem into a sequence-to-sequence task.\n2. The methodology is well-designed and technically sound. \n3. The paper presents an evaluation against multiple recent state-of-the-art methods on standard benchmarks. \n\nWeaknesses:\n\n1. There are no visualization results for the ablation study of each module.\n2. The current visualizations for comparison do not clearly highlight where the improvements occur, which may make it harder to interpret the gains.\n3. The visualization results comparing different methods are not included in the main text to show the improvements.\n\nQUESTIONS:\n1. Would it be possible to include visualization results for the ablation study of each module to better understand their individual contributions?\n\n2. Could the authors consider clarifying where the improvements occur in the visual comparisons to make the gains more interpretable? For example, highlighting the regions with more noticeable improvements in the figure using zoomed-in views.\n\nLIMITATIONS:\nYes", "TITLE": "Official Review", "RECOMMENDATION": 3, "REVIEWER_CONFIDENCE": 3, "CLARITY": 3, "IMPACT": 2, "ORIGINALITY": 3, "DATE": "2025-07-03"}

comments: SUMMARY:
The work proposes a DiT-based method for the ILT method, which achieves SOTA performance.

STRENGTHS AND WEAKNESSES:
1. This framework provides an effective approach to optimizing masks for the Imaging Lithography Technology (ILT) problem, showcasing its versatility in addressing complex design challenges.

2. It achieves state-of-the-art (SOTA) performance, setting a new benchmark in the field and demonstrating significant improvements over previous methodologies.

3. This technique is valuable for engineers and designers in the semiconductor industry, as it streamlines the process of designing masks, ultimately improving manufacturing efficiency and yield.

4. The research appears to benefit significantly from the implementation of a novel tokeniser module, which enhances the overall performance and flexibility of the optimization process.

QUESTIONS:
How does the work customize for patterns other than piecewise constant?

Is this work derived from some ideas on inverse problems in imaging? Cite relevant works, if any.

LIMITATIONS:
The work could have been written better. To motivate readers.
TITLE: Official Review
CLARITY: 3
IMPACT: 3
ORIGINALITY: 3
DATE: 2025-07-05