PICasso: An AI-Enabled Design Framework for Autonomous Optimization of Silicon Photonic Devices

Researchers introduced PICasso, an AI-enabled framework that automates the design of silicon photonic devices from natural-language specifications, and PIC-Set, a benchmark of 36 design tasks. In tests, PICasso achieved up to 92.7% structural Spec@3 and 52% functional Spec@3 on high-complexity circuits, and reduced mean insertion loss from 4.98 dB to 3.25 dB. The framework transforms large language models into practical photonic design agents, producing manufacturable layouts with competitive runtimes.

arXiv:2608.26113v1 Announce Type: new Abstract: We present PICasso, an AI-assisted framework for automated synthesis, verification, and optimization of photonic integrated circuits PICs from natural-language specifications. PICasso couples a structured NL - YAML - GDS generation pipeline with PDK aware knowledge injection, automated placement and routing, DRC/LVS validation, and SAX-based photonic simulation. To systematically evaluate AI-driven photonic design, we introduce PIC-Set, a benchmark of 36 parameterized PIC design tasks spanning core photonic primitives and multi-component circuits. Using PIC-Set, we benchmark several state-of-the-art Large Language Models LLMs under a unified evaluation protocol, including new metrics such as structural and functional $Spec@k$, optimization efficiency, and robustness under perturbations. Across the benchmark, PICasso significantly improves end-to-end specification satisfaction compared to vanilla LLM generation. Structural $Spec@3$ reaches up to 92.7% and functional $Spec@3$ up to 52% on high-complexity circuits. In addition, PICasso consistently reduces circuit insertion loss, lowering the mean loss from 4.98 dB to 3.25 dB 1.74 dB improvement through simulation-guided optimization. These results demonstrate that structured domain constraints, physical verification, and simulation feedback transform LLMs from brittle netlist generators into practical PIC design agents capable of producing manufacturable layouts with competitive runtimes relative to manual GUI-based workflows.