Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
ILILT: Implicit Learning of Inverse Lithography Technologies
Authors: Haoyu Yang, Haoxing Ren
ICML 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | To demonstrate the effectiveness of the ILILT framework, we adopt the latest AI for computational lithography benchmark suite Litho Bench (Zheng et al., 2023), where stateof-the-art mask optimization solutions are evaluated and will be used as comparison baselines. |
| Researcher Affiliation | Industry | 1Design Automation Research Group, NVIDIA, Austin, TX. Correspondence to: Haoyu Yang <EMAIL>. |
| Pseudocode | No | The paper describes the algorithm steps in text and equations but does not provide pseudocode or an algorithm block. |
| Open Source Code | No | The paper does not contain any statement about releasing open-source code or provide a link to a code repository for the methodology described. |
| Open Datasets | Yes | To demonstrate the effectiveness of the ILILT framework, we adopt the latest AI for computational lithography benchmark suite Litho Bench (Zheng et al., 2023) |
| Dataset Splits | No | The paper mentions training data and evaluation metrics but does not specify explicit training, validation, and test splits (e.g., percentages or counts) for the dataset used. |
| Hardware Specification | No | The paper mentions 'GPU-ILT' as a comparison method but does not specify the hardware (e.g., specific GPU models, CPUs) used for its own experiments. |
| Software Dependencies | No | The paper mentions using a 'pytorch layer' for the lithography simulator and 'Optimizer Adam' in Table 1, but it does not provide specific version numbers for PyTorch, Adam, or any other software dependencies. |
| Experiment Setup | Yes | Detailed configurations of the generator used to reproduce our results are listed in Table 1. Table 1: Max Epoch 5, Initial Learning Rate 0.004, Learning Rate Decay Policy step, Optimizer Adam, Weight Decay 0.0001, Loss Equation (13), Sequence Unrolling Depth (T) 4-8, Batch Size 4 |