ILILT: Implicit Learning of Inverse Lithography Technologies
Authors: Haoyu Yang, Haoxing Ren
ICML 2024 | Conference PDF | Archive PDF | Plain Text | 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 <haoyuy@nvidia.com>. |
| 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 |