# Physics-Informed Optical Kernel Regression Using Complex-valued Neural Fields

arXiv: https://arxiv.org/abs/2303.08435 · HTML: https://mlsanity.com/paper/2303.08435/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 20 references that are in the map.
- First posted on arXiv 15 Mar 2023.

## Builds on (most cited first, 4 of 4)

- [Fourier Features Let Networks Learn High Frequency Functions in Low Dimensional Domains](https://mlsanity.com/paper/2006.10739/) (2020)
- [Deep Complex Networks](https://mlsanity.com/paper/1705.09792/) (2017)
- [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)

## Built on by (most cited first, 4 of 4)

- [AnalogCoder: Analog Circuit Design via Training-Free Code Generation](https://mlsanity.com/paper/2405.14918/) (2024)
- [LLM-Enhanced Bayesian Optimization for Efficient Analog Layout Constraint Generation](https://mlsanity.com/paper/2406.05250/) (2024)
- [Grounding and Enhancing Grid-based Models for Neural Fields](https://mlsanity.com/paper/2403.20002/) (2024)
- [Optimizing Predictive AI in Physical Design Flows with Mini Pixel Batch Gradient Descent](https://mlsanity.com/paper/2402.06034/) (2024)

## Abstract (arXiv)

Lithography is fundamental to integrated circuit fabrication, necessitating large computation overhead. The advancement of machine learning (ML)-based lithography models alleviates the trade-offs between manufacturing process expense and capability. However, all previous methods regard the lithography system as an image-to-image black box mapping, utilizing network parameters to learn by rote mappings from massive mask-to-aerial or mask-to-resist image pairs, resulting in poor generalization capability. In this paper, we propose a new ML-based paradigm disassembling the rigorous lithographic model into non-parametric mask operations and learned optical kernels containing determinant source, pupil, and lithography information. By optimizing complex-valued neural fields to perform optical kernel regression from coordinates, our method can accurately restore lithography system using a small-scale training dataset with fewer parameters, demonstrating superior generalization capability as well. Experiments show that our framework can use 31% of parameters while achieving 69× smaller mean squared error with 1.3× higher throughput than the state-of-the-art.