# ILILT: Implicit Learning of Inverse Lithography Technologies

arXiv: https://arxiv.org/abs/2405.03574 · HTML: https://mlsanity.com/paper/2405.03574/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 28 references that are in the map.
- First posted on arXiv 6 May 2024.

## Builds on (most cited first, 13 of 13)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Image-to-Image Translation with Conditional Adversarial Networks](https://mlsanity.com/paper/1611.07004/) (2016)
- [Fourier Neural Operator for Parametric Partial Differential Equations](https://mlsanity.com/paper/2010.08895/) (2020)
- [High-Resolution Image Synthesis and Semantic Manipulation with Conditional GANs](https://mlsanity.com/paper/1711.11585/) (2017)
- [Deep Image Prior](https://mlsanity.com/paper/1711.10925/) (2017)
- [Deep Equilibrium Models](https://mlsanity.com/paper/1909.01377/) (2019)
- [UNet++: Redesigning Skip Connections to Exploit Multiscale Features in Image Segmentation](https://mlsanity.com/paper/1912.05074/) (2019)
- [Learning to Optimize: A Primer and A Benchmark](https://mlsanity.com/paper/2103.12828/) (2021)
- [DAMO: Deep Agile Mask Optimization for Full Chip Scale](https://mlsanity.com/paper/2008.00806/) (2020)
- [Generic Lithography Modeling with Dual-band Optics-Inspired Neural Networks](https://mlsanity.com/paper/2203.08616/) (2022)
- [Large Scale Mask Optimization Via Convolutional Fourier Neural Operator and Litho-Guided Self Training](https://mlsanity.com/paper/2207.04056/) (2022)
- [AdaOPC: A Self-Adaptive Mask Optimization Framework For Real Design Patterns](https://mlsanity.com/paper/2303.12723/) (2023)

## Built on by (most cited first, 7 of 7)

- [SemiKong: Curating, Training, and Evaluating A Semiconductor Industry-Specific Large Language Model](https://mlsanity.com/paper/2411.13802/) (2024)
- [Intelligent OPC Engineer Assistant for Semiconductor Manufacturing](https://mlsanity.com/paper/2408.12775/) (2024)
- [Pushing the Limits of Inverse Lithography with Generative Reinforcement Learning](https://mlsanity.com/paper/2602.19027/) (2026)
- [Deep Generative Prior for First Order Inverse Optimization](https://mlsanity.com/paper/2504.20278/) (2025)
- [Transforming Computational Lithography with AC and AI -- Faster, More Accurate, and Energy-efficient](https://mlsanity.com/paper/2602.15036/) (2026)
- [LithoGRPO: Fast Inverse Lithography via GRPO Reinforced Flow Matching](https://mlsanity.com/paper/2606.00228/) (2026)
- [VPEvolve: A Self-Evolving Virtual Process Engineer for Computational Lithography](https://mlsanity.com/paper/2609.32473/) (2026)

## Abstract (arXiv)

Lithography, transferring chip design masks to the silicon wafer, is the most important phase in modern semiconductor manufacturing flow. Due to the limitations of lithography systems, Extensive design optimizations are required to tackle the design and silicon mismatch. Inverse lithography technology (ILT) is one of the promising solutions to perform pre-fabrication optimization, termed mask optimization. Because of mask optimization problems' constrained non-convexity, numerical ILT solvers rely heavily on good initialization to avoid getting stuck on sub-optimal solutions. Machine learning (ML) techniques are hence proposed to generate mask initialization for ILT solvers with one-shot inference, targeting faster and better convergence during ILT. This paper addresses the question of textitwhether ML models can directly generate high-quality optimized masks without engaging ILT solvers in the loop. We propose an implicit learning ILT framework: ILILT, which leverages the implicit layer learning method and lithography-conditioned inputs to ground the model. Trained to understand the ILT optimization procedure, ILILT can outperform the state-of-the-art machine learning solutions, significantly improving efficiency and quality.