Adjoint Simulation for Electromagnetic Device Optimization
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Solution Overview
Problem
Conventional design techniques for electromagnetic devices often rely on intuition and are inefficient, as they struggle to optimize the vast number of design parameters, leading to increased computational latency and suboptimal device performance, especially with smaller feature sizes and complex functionalities.
Innovation Solution
A physics simulator that performs first-principles based design and optimization using operational and adjoint simulations in parallel, leveraging low-rank objectives to reduce computational latency and enable simultaneous execution of forward and backward passes, thereby optimizing structural parameters of electromagnetic devices.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If conventional guess and check method is used for device design, then device functionality can be achieved with basic parameters, but computational latency increases and optimization of vast number of design parameters becomes inefficient
Solution Approach 1:
The patent performs preliminary actions by executing forward simulations to compute device performance metrics before optimization. The adjoint simulation is set up in advance to efficiently compute gradients of the loss function with respect to design parameters, enabling rapid iterative optimization without repeated expensive forward simulations.
Solution Approach 2:
The patent implements feedback mechanisms through the adjoint simulation that computes gradients of the loss function with respect to design parameters. These gradients provide feedback on how to adjust parameters to minimize loss, enabling automated iterative optimization that converges to optimal designs efficiently.
2Manufacturing precision
If device functionality is increased and manufacturing tolerances improve to allow for smaller device feature sizes, then device performance is enhanced, but the complexity of optimizing vast number of design parameters increases
Solution Approach 1:
The patent extracts the optimization problem from the complex design space by formulating it as a gradient-based optimization using adjoint simulations. This separates the complex forward simulation from the optimization process, allowing efficient navigation of high-dimensional parameter spaces even for devices with billions of parameters.
Solution Approach 2:
The patent changes the approach to parameter optimization by using gradient-based methods with adjoint simulations. Instead of brute-force search or simple parameter adjustment, the system computes efficient gradients that enable rapid convergence to optimal parameter values, handling billions of parameters effectively.
3Reliability
If full optimization of billions of design parameters is performed, then device performance is maximized, but computational resources and time requirements become intractable
Solution Approach 1:
The patent performs preliminary computation of adjoint simulations that efficiently compute gradients for all design parameters simultaneously. This preliminary setup enables subsequent optimization iterations to proceed much faster, as the gradient computation does not need to be repeated for each parameter adjustment.
Solution Approach 2:
The patent replaces brute-force computational search with a more efficient mathematical approach using adjoint simulations and gradient-based optimization. This substitution reduces the computational complexity from exponential to linear scaling with the number of parameters, making optimization of billions of parameters tractable.
Data Source
AI summary
A method and system for optimizing structural parameters of a physical device is described. The method includes receiving an initial description of the physical device that describes structural parameters of the physical device within a simulated environment. The method further includes performing an operational simulation of the physical device in response to an excitation source, performing an adjoint simulation by backpropagating a placeholder metric through a simulated environment to determine a loss gradient, updating the loss gradient based, at least in part, on a loss metric determined from the operational simulation. Additionally, the method further comprises computing a structural gradient corresponding to an influence of changes in the structural parameters on the loss metric and generating a revised description of the physical device by updating the structural parameters based on the structural gradient to reduce the loss metric.