Mapping the global design space of nanophotonic components using machine learning pattern recognition

Abstract

Nanophotonics finds ever broadening applications requiring complex component designs with a large number of parameters to be simultaneously optimized. Recent methodologies employing optimization algorithms commonly focus on a single design objective, provide isolated designs, and do not describe how the design parameters influence the device behaviour. Here we propose and demonstrate a machine-learning-based approach to map and characterize the multi-parameter design space of nanophotonic components. Pattern recognition is used to reveal the relationship between an initial sparse set of optimized designs through a significant reduction in the number of characterizing parameters. This defines a design sub-space of lower dimensionality that can be mapped faster by orders of magnitude than the original design space. As a result, multiple performance criteria are clearly visualized, revealing the interplay of the design parameters, highlighting performance and structural limitations, and inspiring new design ideas. This global perspective on high-dimensional design problems represents a major shift in how modern nanophotonic design is approached and provides a powerful tool to explore complexity in next-generation devices.

References in corpus (6)

• Inverse design and demonstration of a compact and broadband on-chip wavelength demultiplexer
• Nanophotonic Computational Design
• Inverse Design of Near Unity Efficiency Perfectly Vertical Grating Couplers
• Fully-automated optimization of grating couplers
• Transformation Inverse Design
• Elastic Maps and Nets for Approximating Principal Manifolds and Their Application to Microarray Data Visualization

Cited by in corpus (20)

• Deep neural networks for the evaluation and design of photonic devices
• Tunable nanophotonics enabled by chalcogenide phase-change materials
• Knowledge Discovery In Nanophotonics Using Geometric Deep Learning
• Machine learning-assisted design of material properties
• A newcomer's guide to deep learning for inverse design in nano-photonics
• Iterative optimization of photonic crystal nanocavity designs by using deep neural networks
• 2023 Astrophotonics Roadmap: pathways to realizing multi-functional integrated astrophotonic instruments
• Machine learning and evolutionary algorithm studies of graphene metamaterials for optimized plasmon-induced transparency
• Manifold Learning for Knowledge Discovery and Intelligent Inverse Design of Photonic Nanostructures: Breaking the Geometric Complexity
• Design of Compact and Efficient Silicon Photonic Micro Antennas with Perfectly Vertical Emission
• Inverse Design of Grating Couplers Using the Policy Gradient Method from Reinforcement Learning
• Parametric Model Embedding
• Adjoint optimization of polarization-splitting grating couplers
• Inverse Design of a Graphene-Based Quantum Transducer via Neuroevolution
• Deep learning for nano-photonic materials -- The solution to everything!?
• A FEM enhanced Transfer Matrix method for optical grating design
• Identifying topology of leaky photonic lattices with machine learning
• Strategies for optimizing plasmonic grating couplers with topology-based inverse design
• Multiplexed Supercell Metasurface Design and Optimization with Tandem Residual Networks
• Design of slow-light-enhanced bimodal interferometers using dimensionality reduction techniques