Cascaded Fabrication and Design Models for Inverse Device Optimization
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Solution Overview
Problem
Conventional design techniques for electromagnetic devices are inefficient and do not fully utilize improved manufacturing tolerances and increased functionality, often relying on guesswork due to the vast number of design parameters involved, leading to suboptimal device performance.
Innovation Solution
A methodology is introduced that cascades a differentiable fabrication model with a design model to optimize the design and fabrication of physical devices, allowing for end-to-end inverse design by propagating gradients through both models to refine structural and fabrication specifications iteratively.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If conventional guess and check design techniques are used, then design simplicity is maintained, but device performance optimization deteriorates due to the vast number of design parameters
Solution Approach 1:
The patent replaces manual guess-and-check design methods with an automated inverse design system that uses computational algorithms to optimize device parameters. The system substitutes human intuition with machine-based optimization algorithms that can systematically explore the vast design parameter space and converge on optimal solutions.
Solution Approach 2:
The patent introduces an inverse design model as an intermediary between design requirements and fabrication parameters. This model acts as a mediator that translates desired device performance specifications into optimal fabrication parameters, eliminating the need for manual iteration through countless design possibilities.
2Manufacturing precision
If manufacturing tolerances are improved to allow smaller feature sizes, then device functionality increases, but design optimization becomes more critical and complex
Solution Approach 1:
The patent performs preliminary optimization calculations through the inverse design model before actual fabrication begins. By pre-determining the optimal design parameters that will achieve the desired performance within tight tolerances, the system eliminates time-consuming trial-and-error iterations during the design phase.
Solution Approach 2:
The patent creates a virtual model or digital twin of the device through the inverse design system, allowing optimization to be performed in the digital domain before physical fabrication. This digital copy enables exhaustive parameter exploration without consuming physical materials or fabrication time.
3Adaptability or versatility
If the number of design parameters is increased to enhance device functionality, then device capability improves, but conventional design methods become inadequate
Solution Approach 1:
The patent systematically varies and optimizes multiple design parameters simultaneously through the inverse design algorithm. The system automatically adjusts numerous parameters across different scales and domains to achieve optimal device functionality, something impossible through manual design methods.
Solution Approach 2:
The inverse design system serves multiple functions: it optimizes geometric parameters, determines fabrication settings, predicts device performance, and generates design specifications all in one integrated platform. This universal tool handles the complexity of multi-parameter optimization that no single conventional design method could address.
Data Source
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AI summary
A technique for simulating and optimizing the fabrication and design of a physical device is described. The technique includes executing a fabrication simulation of the physical device with a fabrication model that receives a fabrication specification as input and outputs a structural design for the physical device in response to the fabrication simulation. An operational simulation of the physical device is executed with a design model that simulates a field response propagating through a simulated environment of the physical device. The structural design output from the fabrication model is forward cascaded to the design model and an output from backpropagation of a performance loss error through the design model is reverse cascaded to the fabrication model.