Scalar Loss Functions for Multi-Objective Device Optimization
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Solution Overview
Problem
Current techniques for designing optical and electromagnetic devices often focus on a single measure of performance, limiting their optimization capabilities as they fail to consider multiple performance metrics, especially as device complexity and feature sizes decrease.
Innovation Solution
A method involving a computing system that receives a design specification, generates a proposed design, determines a vector of loss values, calculates a scalar loss value based on the Euclidean distance between these values and desired characteristics, and updates the design iteratively using gradient-based optimization to achieve multi-metric optimization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If single-measure optimization is used, then optimization simplicity is maintained, but multi-performance optimization capability is lost
Solution Approach 1:
The patent segments the multi-objective optimization problem into two parts: (1) computing individual loss values for each performance metric separately, and (2) aggregating these into a scalar loss value. This segmentation allows each metric to be evaluated independently while still achieving comprehensive multi-objective optimization through the aggregation step.
Solution Approach 2:
The patent transforms the multi-dimensional optimization problem (multiple performance metrics) into a single-dimensional problem by mapping the vector of loss values onto a scalar loss value through aggregation. This dimensionality reduction enables the use of standard single-objective optimization algorithms while still achieving multi-performance optimization.
2Manufacturing precision
If multiple performance metrics are optimized concurrently, then design quality improves, but computational complexity increases
Solution Approach 1:
The patent introduces a scalar loss value as an intermediary that mediates between multiple performance metrics and the optimization algorithm. Instead of directly optimizing multiple metrics simultaneously (which would be computationally complex), the scalar loss value serves as a simplified proxy that captures the essence of multi-objective optimization, enabling efficient computation while maintaining design quality.
3Speed
If scalar aggregation of loss values is used, then optimization convergence is improved, but information about individual metrics is lost
Solution Approach 1:
The patent implements a feedback mechanism where the vector of individual loss values is computed and available for analysis, even though a scalar aggregation is used for the optimization step. This allows the system to monitor the performance of each individual metric throughout the optimization process, providing feedback on how each metric is evolving without preventing convergence through scalar aggregation.
Data Source
AI summary
In some embodiments, a method for creating a design for a physical device is provided. A computing system receives a design specification. The computing system generates a proposed design based on the design specification. The computing system determines a vector of loss values based on the proposed design. The computing system determines a scalar loss value based on a distance between the vector of loss values and a volume representing desired characteristics of the physical device. The computing system updates the proposed design based on the scalar loss value.