AI-Enabled Photonic Design Automation

• AI-enabled photonic design automation is the integration of machine learning and physics-based modeling to streamline device, circuit, and system-level photonic design.
• It leverages surrogate electromagnetic modeling, inverse design, and reinforcement learning to drastically reduce simulation times and improve manufacturability.
• It enables automated placement, routing, and cross-layer co-design for scalable, high-performance photonic integrated circuits and nanophotonic devices, achieving up to 10³× speedups.

AI-enabled photonic design automation (AIPDA) refers to the integration of artificial intelligence techniques—spanning machine learning, neural operators, reinforcement learning, and LLMs—within the full stack of photonic device, circuit, and system design. AIPDA enables the efficient exploration of ultra-high-dimensional parameter spaces and the direct optimization of manufacturable, robust, and high-performance photonic integrated circuits (PICs) and nanophotonic devices. This paradigm shift accelerates both forward simulation and inverse design, supersedes traditional manual- or brute-force approaches, and bridges device-level design with large-scale system and architectural co-design.

1. Foundations and Motivations

AIPDA arises from the convergence of machine learning and physics-based electromagnetic (EM) modeling, driven by the ultra-large design spaces and complex fabrication constraints of next-generation photonic hardware. Traditional trial-and-error combined with slow numerical solvers is inadequate for modern PICs, which frequently integrate thousands of devices, require order-of-magnitude speedups in EM analysis, and are subject to stochastic fabrication variations and electro-optical co-integration requirements. Recent advances have demonstrated that AI surrogates (e.g., deep neural networks, Fourier neural operators, attention architectures) can accelerate EM simulation up to 10³× (Ma et al., 2 Mar 2025, Abdelraouf et al., 6 May 2025, Zhang et al., 30 Sep 2025), enable real-time inverse and multi-objective optimization (Zhou et al., 30 Jul 2025, Gu et al., 2021), and facilitate cross-domain co-design workflows spanning from device to AI algorithm (Zhou et al., 31 Dec 2025, Yin et al., 2024).

The scope includes device-level optimization (e.g., mode converters, bends, interferometers), system-level netlist and layout synthesis (placement and routing), and holistic hardware-software benchmarking. Motivation extends to high-speed analog computing, AI acceleration, and resilient, manufacturable photonic hardware (Yin et al., 31 Dec 2025).

2. AI-Accelerated Forward and Inverse Photonic Design

Machine learning has been applied to both surrogate EM modeling and automated topology optimization:

Forward Modeling (Surrogates):

• Fully connected networks, CNNs, and U-Nets efficiently learn mappings from device geometry or pixelated layouts to S-parameters and field response (e.g., amplitude/phase across wavelength) (Zhang et al., 30 Sep 2025, Abdelraouf et al., 6 May 2025).
• Fourier Neural Operators (FNO), including NeurOLight, model frequency-dependent responses and long-range field correlations (Ma et al., 2 Mar 2025, Zhou et al., 31 Dec 2025).
• Physics-Informed Neural Networks (PINNs) directly embed Maxwell residuals and boundary conditions in the loss, facilitating training with limited labeled data and enforcing physical fidelity (Abdelraouf et al., 6 May 2025).

Inverse Design:

• Adjoint methods are tightly integrated with AI surrogates: at each gradient step, replace expensive FDTD/FDFD simulation with a trained neural operator, enabling 100–1000× acceleration for shape/topology optimization (Ma et al., 2 Mar 2025, Zhou et al., 30 Jul 2025).
• Generative models (GANs, VAEs, diffusion models) and invertible networks synthesize device layouts achieving target optical objectives, often with built-in or penalized fabrication constraints (Zhang et al., 30 Sep 2025).
• Reinforcement learning, notably attractor-selection (AttSel), enables online exploration and exploitation directly coupled to FDTD evaluation, suitable for discovering ultra-compact, non-intuitive device topologies (Turduev et al., 2022).

Typical inverse design problems are formulated as: $$\min_{\theta} \; -F(\varepsilon (\theta)) + R(\varepsilon (\theta))$$ subject to differentiable fabrication models and process variation constraints (Ma et al., 2 Mar 2025, Zhou et al., 30 Jul 2025). Robust objectives are handled via stochastic expectation over a fabrication-induced perturbation distribution: $$\varepsilon^* = \arg \min_{\varepsilon} \; \mathbb{E}_{\delta, \theta \sim P}[ - \mathcal{F}(\varepsilon \oplus \delta; \theta) ] + \lambda R(\varepsilon)$$

3. Data-Driven Modeling, Multi-Fidelity and Physics-Informed Workflows

High-throughput data generation and multi-fidelity modeling are critical for machine learning efficacy and generalizability:

• MAPS provides an open-source infrastructure combining multi-fidelity dataset acquisition, standardized hierarchical data loaders, and adjoint-based inverse design (Ma et al., 2 Mar 2025). Labels span low- and high-fidelity field solutions, S-parameters, adjoint gradients, and device metadata, supporting both supervised and PINN-based training.
• Sampling strategies include uniform parameter sweeps, optimization trajectory (“opt-traj”) sampling, and perturbed opt-traj for distributional diversity (Ma et al., 2 Mar 2025).
• Physics-informed losses (PDE residuals, boundary conditions), gradient alignment, and multi-objective regularization are incorporated; for instance,

$$\mathcal{L}_{\mathrm{phy}} = \| R(\varepsilon, E_{\mathrm{pred}}) \|_2^2$$

enforces Maxwell consistency, while gradient-alignment loss checks surrogate fidelity for topology optimization.

Multi-fidelity co-kriging and multi-level Richardson extrapolation are applied for statistically robust merging of coarse and fine EM simulation data (Ma et al., 2 Mar 2025).

4. Physical Design Automation: Placement, Routing, and System Integration

Automated physical layout generation is a major bottleneck for scaling PICs. Modern AIPDA workflows deploy highly parallelized, AI- and optimization-based placement and routing engines:

• Apollo implements a GPU-accelerated placement framework with asymmetric, cosine-weighted wirelength (cosWA), bending- and orientation-aware placement cost, explicit modeling of routing congestion and crossings, and conditional projection for complex alignment constraints (Zhou et al., 26 Apr 2025). A custom blockwise adaptive Nesterov optimizer ensures robust convergence across mixed-scale components. Apollo achieves >94% routability—compared to ~51% for previous methods—and places up to 4096 devices in minutes.
• LiDAR and curvy-aware A* routers use heading-augmented 3D search spaces to generate GDSII-legal curved waveguide routes, enforcing minimum bend radius, inter-waveguide spacing, and design-rule constraints. DRV (Design Rule Violation) detection is intrinsic to the routing step (Zhou et al., 30 Jul 2025, Yin et al., 31 Dec 2025).
• System-level frameworks such as SimPhony support generic, multi-core, netlist-based representation, optics-specific dataflow modeling (multi-dimensional parallelism: spectral, spatial, temporal), data-aware energy modeling, and detailed link-budget analysis. This allows seamless translation from photonic device/circuit parameters to system performance (latency, energy, area, SNR) (Yin et al., 2024, Yin et al., 31 Dec 2025).

5. Co-Design, Multi-Layer Integration, and Hardware-Aware ML

End-to-end electronic-photonic design automation requires the integration of device, circuit, architecture, and algorithm layers (“cross-layer co-design”).

• Physical-level surrogates (e.g., NeurOLight) and fabrication-aware inverse design frameworks (e.g., MAPS, ADEPT) allow device-level characteristics (e.g., insertion loss, crosstalk, modulation efficiency) to inform higher-level circuit and system models (Zhou et al., 31 Dec 2025, Gu et al., 2021).
• Hardware-aware ML: device/circuit non-idealities (phase noise, quantization, link loss) are injected into AI model training loops, ensuring resilient co-evolution of AI algorithms and photonic hardware, with accuracy loss ≤0.5% relative to digital baselines (Zhou et al., 31 Dec 2025, Yin et al., 2024).
• Circuit topology search: ADEPT and ADEPT-Z provide fully differentiable and evolutionary search over circuit-building blocks, allowing simultaneous optimization for area, power/energy, and robustness under foundry-provided constraints (e.g., process design kit (PDK) parameters) (Gu et al., 2021, Yin et al., 31 Dec 2025).
• Control-system co-design: for resonator-weight banks in neuromorphic photonic systems, process-aware design, trimming, calibration, and closed-loop feedback protocols achieve 10–12 bit weight control, sub-0.1% drift, and sub-nanosecond settling—fully organized by automated Python toolchains and firmware generators (Lima et al., 2022).

6. AI Agents, LLMs, and the Shift Toward Natural Language Design

Recent advances deploy multi-agent LLM frameworks to automate PIC design from natural language specification:

• PhIDO is an end-to-end pipeline where interpreter, designer, algorithmic layout, and circuit verification agents synthesize GDSII layouts from free-form prompts (Sharma et al., 18 Aug 2025). LLMs extract components, map to PDKs, generate schematics, and interface with parametric layout engines.
• Pass@5 end-to-end success rates reach 58% for mid-sized circuits (≤15 components). Chain-of-thought and Mixture-of-Experts LLMs (e.g., Gemini-2.5-pro, Claude Opus 4) balance output brevity and structured reasoning, achieving high efficiency and lower computation/resource cost.
• LLMs/transformers are also being adapted to model geometry-spectrum mappings (“Chat to Chip”) and produce layer-by-layer device definitions directly from descriptive prompts (Zhang et al., 30 Sep 2025).

7. Performance Metrics, Benchmarking, and Future Directions

AIPDA workflows are compared and validated using metrics such as field prediction N-L2 norm, S-parameter error, gradient similarity, yield and trimming budget, energy per MAC, area efficiency, and end-to-end layout pass rates (Ma et al., 2 Mar 2025, Lima et al., 2022, Sharma et al., 18 Aug 2025, Zhou et al., 31 Dec 2025). Benchmark results show:

Capability	Traditional	AI-Enabled Stack	Speedup/Improvement
Device simulation time	hours/device	seconds/device	~10³×
Inverse design loop time	days/design	min–hours/design	~10²–10³×
Placement/routing runtime	hours–weeks	min–tens min	~10–100×
Routability (large PICs)	~51%	>94%	1.8×
Area compaction (PTCs)	baseline	up to 30×	up to 30×
Energy efficiency (TOPS/W)	baseline	up to 22.3 TOPS/W	>10×

AIPDA faces challenges including data scarcity for non-standard devices, generalization to new process nodes or materials, interpretability of neural surrogates, and ensuring fabrication robustness. Anticipated advances involve active and transfer learning, hybrid PINN-training with self-supervision (Abdelraouf et al., 6 May 2025), LLM+graph neural network (GNN) fusion for end-to-end 2D/3D design, and robotic automation to close the fabrication-test loop (Sharma et al., 18 Aug 2025). Open-source toolchains (MAPS, SimPhony, ADEPT) are democratizing access and enabling cross-disciplinary collaboration (Ma et al., 2 Mar 2025, Yin et al., 2024, Gu et al., 2021).

• (Yin et al., 2024) SimPhony: A Device-Circuit-Architecture Cross-Layer Modeling and Simulation Framework for Heterogeneous Electronic-Photonic AI System
• (Ma et al., 2 Mar 2025) MAPS: Multi-Fidelity AI-Augmented Photonic Simulation and Inverse Design Infrastructure
• (Zhou et al., 26 Apr 2025) Automated Routing-Informed Placement for Large-Scale Photonic Integrated Circuits
• (Abdelraouf et al., 6 May 2025) Physics-Informed Neural Networks in Electromagnetic and Nanophotonic Design
• (Zhou et al., 30 Jul 2025) Toward Intelligent Electronic-Photonic Design Automation for Large-Scale Photonic Integrated Circuits
• (Sharma et al., 18 Aug 2025) AI Agents for Photonic Integrated Circuit Design Automation
• (Zhang et al., 30 Sep 2025) Data driven approaches in nanophotonics: A review of AI-enabled metadevices
• (Yin et al., 31 Dec 2025) Toward Large-Scale Photonics-Empowered AI Systems: From Physical Design Automation to System-Algorithm Co-Exploration
• (Zhou et al., 31 Dec 2025) Democratizing Electronic-Photonic AI Systems: An Open-Source AI-Infused Cross-Layer Co-Design and Design Automation Toolflow
• (Gu et al., 2021) ADEPT: Automatic Differentiable DEsign of Photonic Tensor Cores
• (Turduev et al., 2022) Reinforcement learning enabled the design of compact and efficient integrated photonic devices
• (Lima et al., 2022) Design Automation of Photonic Resonator Weights