Space-Time Scattering Network for Optical Inverse Design
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Solution Overview
Problem
Current methods for light propagation modeling, such as Maxwell's equations, rely on continuous wave equations that do not provide a discrete physical model, forcing simulations to approximate discrete forms, and existing inverse design and tomography methods are inefficient for time-dependent and high-index contrast systems.
Innovation Solution
A space-time scattering network (STSN) is developed, which represents light scattering as a tensor field and uses a translation operator to connect subnets, enabling an inherently discrete and efficient computational model for light propagation and inverse design, capable of handling time-dependent and high-index contrast systems.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If continuous wave equations (Maxwell's equations) are used to model light propagation, then the model is physically accurate and ubiquitous, but the model does not provide a discrete physical model, forcing simulations to approximate discrete forms which increases computational complexity
Solution Approach 1:
The patent segments the continuous space-time domain into discrete space-time nodes, creating a grid-based representation where light propagation is modeled as transitions between discrete nodes. This segmentation transforms the continuous wave equation into a discrete scattering network that maintains physical accuracy while enabling efficient computation through discrete mathematical operations.
2Ease of manufacture
If conventional inverse design methods are used, then the methods can design optical components, but the methods are inefficient for time-dependent and high-index contrast systems, requiring excessive computational resources
Solution Approach 1:
The patent replaces conventional iterative optimization methods with a neural network-based inverse design system. The neural network is trained on the discrete scattering network model and can directly predict optical component designs that achieve desired time-dependent optical responses, eliminating the need for repeated forward simulations and significantly improving computational efficiency for high-index contrast systems.
3Adaptability or versatility
If conventional neural networks are used for inverse design, then the networks can learn optical design patterns, but the networks require extensive training data and computational resources, especially for time-dependent systems
Solution Approach 1:
The patent extends the neural network design from conventional spatial-only representations to space-time representations by incorporating the time dimension explicitly. This allows the network to learn and predict time-dependent optical responses directly, reducing the amount of training data needed compared to conventional approaches that would require separate training for each time point or frequency.
Data Source
AI summary
A system to generate a space-time scattering network includes a computing unit configured to store a space-time scattering network (STSN) algorithm. The computing unit also executes the STSN algorithm to represent scattering of light through a material mathematically as a tensor field at a location. The computing unit also generates a subnet that represents the tensor field at the location. The computing unit also identifies, based on a translation operator, connections between a plurality of subnets, where each of the subnets in the plurality of subnets represents a given tensor field at a given location. The computing unit also forms the STSN based at least in part on the identified connections between the plurality of subnets. The computing unit further uses the STSN to inversely design an optical component or to perform tomography.