End-to-End Physical Design Automation Flow for Yield-Optimized Inverse-Designed Large-Scale Electronic-Photonic Integrated Circuits

2026-04-16

AI Summary

This work addresses the absence of a manufacturing-aware, yield-optimized end-to-end physical design automation flow for electronic–photonic integrated circuits (EPICs), which hinders their scalable deployment in AI systems. To bridge this gap, we present OptoSynthesizer—the first fully automated framework that spans from netlist to manufacturable GDS layout. Our approach innovatively integrates physics-informed AI-enhanced digital twin-assisted inverse design, GPU-accelerated routing-aware placement, hierarchical curved-waveguide modeling, and electro-photonic co-optimized global routing. This methodology enables the efficient generation of compact, large-scale photonic tensor cores and high-bandwidth interconnect architectures, establishing a foundation for high-yield, manufacturable, heterogeneous EPIC designs tailored for next-generation AI systems.

Technology Category

• Machine Learning: Hardware-aware ML
• Computer Vision: Computational Photography, Image & Video Synthesis
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Economics, Online Markets and Human Computation: Incentives in network design for Web infrastructures and ecosystems
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs

Abstract

As AI systems scale to multi-chiplet and wafer-level architectures, the demand for ultra-high bandwidth and system scalability has outpaced the capabilities of electrical interconnects and computing units. Large-scale heterogeneous electronic-photonic integrated chiplets (EPICs) provide a promising solution, but their practical adoption is limited by the lack of a unified, fabrication-aware physical design automation stack. At the same time, inverse-designed ultra-compact photonic devices offer orders-of-magnitude improvements in spatial and spectral density, yet remain constrained by insufficient design-for-manufacturing support and yield optimization. In this work, we present OptoSynthesizer, an end-to-end physical design automation flow for yield-optimized, inverse-designed EPICs. It integrates three key components across the physical design pipeline: (1) OptoSynthesizer-InvDes, a physical-AI-augmented, digital-twin-assisted photonic inverse design and photonics-aware inverse lithography framework; (2) OptoSynthesizer-Place, a GPU-accelerated routing-informed EPIC placer for large-scale routability-optimized layout; and (3) OptoSynthesizer-Route, a hierarchical curvy-aware waveguide router with global-planning-assisted electrical-optical co-routing. Together, these toolkits form a seamless flow from EPIC netlists to fabrication-ready, yield-robust GDS layouts. We demonstrate how this framework enables compact large-scale photonic tensor cores and high-bandwidth interconnect fabrics for heterogeneous EPIC platforms, providing a practical foundation for manufacturable large-scale EPICs in next-generation AI systems.

Problem

• electronic-photonic integrated circuits
• inverse design
• physical design automation
• yield optimization
• manufacturability

Innovation

• inverse design
• physical design automation
• electronic-photonic integration
• yield optimization
• co-routing