From Pixels to PCells: A Neurosymbolic Approach to Photonic Component Creation
Quick Answer
PixCell is a neurosymbolic system that transforms visual photonic components into executable parametric programs, achieving a mean IoU of over 0.9, significantly outperforming traditional models.
Quick Take
The system demonstrates effective training of the Qwen3.6-35B-A3B model, improving IoU from 0.422 to 0.491 after guided revisions, establishing a robust framework for photonic component design.
Key Points
- PixCell achieves a mean IoU of 0.9+, with scores up to 0.974 across eight targets.
- The system enables cheaper deterministic visual verification compared to generation attempts.
- Qwen3.6-35B-A3B model's IoU improved from 0.422 to 0.491 after three revision rounds.
- PixCell reconstructs primitive programs for various photonic stack configurations.
- The framework supports training without supervised demonstrations, enhancing design efficiency.
Paper Resources
Source Excerpt
We present PixCell, a neurosymbolic system in which multimodal agents convert a visually presented photonic component into a parametric program over a small domain-specific language (DSL) of geometric primitives. A system enabling deterministic visual verification renders evaluation asymmetrically cheaper than the generation attempt. While models using multi-seed sampling and iterative revision reach a mean best-turn IoU of only 0. 416, multimodal agents through PixCell's interface and verifier c
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