Inverse Design of Photonic Devices Using Signed Distance Functions
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Solution Overview
Problem
Current techniques for designing photonic devices, such as those used in optical communication, are inefficient and costly due to the high number of design parameters and the reliance on manual or simple optimization methods, which limits their performance, size, and robustness.
Innovation Solution
The use of a computer-implemented method for designing photonic devices through an inverse design process, which involves generating an initial design based on a specification, determining signed distance fields, calculating structural parameters, simulating performance, and updating features using fabrication and performance loss values.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual or simple optimization methods are used for designing photonic devices, then the design process is simpler to implement, but the performance, size, and robustness of the devices are limited
Solution Approach 1:
The patent replaces manual optimization methods with an automated inverse design system that uses machine learning models and computational algorithms to optimize photonic device parameters, transitioning from mechanical/manual adjustment to computational automation
Solution Approach 2:
The system optimizes a large number of design parameters simultaneously by transforming the design space into a lower-dimensional latent space where parameters can be efficiently adjusted and optimized through machine learning, enabling comprehensive parameter optimization without exponential complexity increase
2Reliability
If a large number of design parameters are optimized, then device performance improves, but the computational complexity and time required increase significantly
Solution Approach 1:
The system performs preliminary training of machine learning models using pre-generated datasets from finite element simulations, creating a trained model that can rapidly evaluate design parameters without requiring full simulations during the optimization process, thus reducing design time while maintaining optimization comprehensiveness
Solution Approach 2:
The patent transforms the high-dimensional design parameter space into a lower-dimensional latent space using autoencoders and neural networks, allowing optimization of many parameters simultaneously by working in a compressed representation where computational complexity is reduced while preserving the essential design characteristics
3Productivity
If traditional design methods are used, then the design process is faster to complete, but the resulting devices have larger size and inferior performance
Solution Approach 1:
The system optimizes geometric and material parameters of the photonic device structure through machine learning to achieve superior performance in compact form factors, allowing smaller device size while maintaining or improving performance through intelligent parameter optimization rather than traditional scaling approaches
Data Source
AI summary
In some embodiments, a computer-implemented method for designing a physical device is provided. A computing system generates an initial design based on a design specification. The initial design includes a list of features, and each feature of the list of features represents a convex shape. The computing system determines a set of signed distance fields that includes a signed distance field for each feature of the list of features, and determines a set of structural parameters using the set of signed distance fields. The computing system simulates performance of the initial design using the set of structural parameters to determine a performance loss value. The computing system determines at least one fabrication loss value using the set of signed distance fields. The computing system updates at least one feature of the list of features using the at least one fabrication loss value and a gradient of the performance loss value.