AI-Assisted Digital Twin Development: From Literature Analysis to Physics-Based Silicon Ring Resonator Model

Photonics Research Workflow Automation

Axiomatic Co-Explorer Axiomatic AI

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Abstract

This case study presents a comprehensive workflow for developing a digital twin of a silicon ring resonator electro-optic modulator based on experimental data from literature. Using advanced AI tools and systematic methodology, we demonstrate the complete process from document analysis to optimized physics-based modeling. The workflow includes document parsing, data extraction from figures, interpolation, physics model development, parameter optimization, and photonic layout generation. The final optimized model achieves excellent agreement with experimental data (RMSE = 0.460 dB) and provides physically meaningful parameters for device design and analysis.

Reference: Xu, Q., Schmidt, B., Pradhan, S., & Lipson, M. (2005). Micrometre-scale silicon electro-optic modulator. Nature, 435(7040), 325-327.

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Keywords: Photonic integrated circuits, MCP servers, Claude Code, electromagnetic simulation, digital twin validation

1. Introduction

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Actor: Photonics Researcher and Integrated Optics Engineer Goal: Transform literature data into validated digital twin with optimized parameters and fabrication-ready layout Challenge: Extract experimental data from figures, develop physics-based models, optimize parameters, and generate manufacturable designs through reproducible workflows

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The field of silicon photonics has revolutionized optical communication and computing, with ring resonator-based electro-optic modulators standing as cornerstone devices due to their ultra-compact footprints and exceptional modulation efficiency. However, the development of accurate digital twins for these sophisticated devices presents significant challenges that have traditionally required extensive manual effort and specialized expertise.

Traditional Workflow Pain Points: