Inverse design of photonic devices with strict foundry fabrication constraints

• URL: http://arxiv.org/abs/2201.12965v1
• Date: Mon, 31 Jan 2022 02:27:25 GMT
• Title: Inverse design of photonic devices with strict foundry fabrication constraints
• Authors: Martin F. Schubert, Alfred K. C. Cheung, Ian A. D. Williamson, Aleksandra Spyra, David H. Alexander
• Abstract summary: We introduce a new method for inverse design of nanophotonic devices which guarantees that designs satisfy strict length scale constraints. We demonstrate the performance and reliability of our method by designing several common integrated photonic components.
• Score: 55.41644538483948
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We introduce a new method for inverse design of nanophotonic devices which guarantees that designs satisfy strict length scale constraints -- including minimum width and spacing constraints required by commercial semiconductor foundries. The method adopts several concepts from machine learning to transform the problem of topology optimization with strict length scale constraints to an unconstrained stochastic gradient optimization problem. Specifically, we introduce a conditional generator for feasible designs and adopt a straight-through estimator for backpropagation of gradients to a latent design. We demonstrate the performance and reliability of our method by designing several common integrated photonic components.

Related papers

• Compositional Generative Inverse Design [69.22782875567547]
  Inverse design, where we seek to design input variables in order to optimize an underlying objective function, is an important problem. We show that by instead optimizing over the learned energy function captured by the diffusion model, we can avoid such adversarial examples. In an N-body interaction task and a challenging 2D multi-airfoil design task, we demonstrate that by composing the learned diffusion model at test time, our method allows us to design initial states and boundary shapes.
  arXiv  Detail & Related papers  (2024-01-24T01:33:39Z)
• High-Dimensional Bayesian Optimisation with Large-Scale Constraints -- An Application to Aeroelastic Tailoring [1.8004890248574648]
  Design optimisation potentially leads to lightweight aircraft structures with lower environmental impact. Due to the high number of design variables and constraints, these problems are ordinarily solved using gradient-based optimisation methods. The present study attempts to tackle the problem by using high-dimensional Bayesian optimisation in combination with a dimensionality reduction approach.
  arXiv  Detail & Related papers  (2023-12-14T12:51:29Z)
• Neural Fields with Hard Constraints of Arbitrary Differential Order [61.49418682745144]
  We develop a series of approaches for enforcing hard constraints on neural fields. The constraints can be specified as a linear operator applied to the neural field and its derivatives. Our approaches are demonstrated in a wide range of real-world applications.
  arXiv  Detail & Related papers  (2023-06-15T08:33:52Z)
• Tackling Multimodal Device Distributions in Inverse Photonic Design using Invertible Neural Networks [0.0]
  Inverse design is the process of matching a device or process parameters to exhibit a desired performance. Most traditional optimization routines assume an invertible one-to-one mapping between the design parameters and the target performance. We show how a generative modeling approach based on invertible neural networks can provide the full distribution of possible solutions to the inverse design problem.
  arXiv  Detail & Related papers  (2022-08-29T08:46:17Z)
• Optimal Design of Electric Machine with Efficient Handling of Constraints and Surrogate Assistance [5.387300498478744]
  This article proposes an optimization method incorporated into a popularly-used evolutionary multi-objective optimization algorithm - NSGA-II. The proposed method exploits the inexpensiveness of geometric constraints to generate feasible designs by using a custom repair operator.
  arXiv  Detail & Related papers  (2022-06-03T17:13:29Z)
• De-homogenization using Convolutional Neural Networks [1.0323063834827415]
  This paper presents a deep learning-based de-homogenization method for structural compliance minimization. For an appropriate choice of parameters, the de-homogenized designs perform within $7-25%$ of the homogenization-based solution.
  arXiv  Detail & Related papers  (2021-05-10T09:50:06Z)
• Speeding up Computational Morphogenesis with Online Neural Synthetic Gradients [51.42959998304931]
  A wide range of modern science and engineering applications are formulated as optimization problems with a system of partial differential equations (PDEs) as constraints. These PDE-constrained optimization problems are typically solved in a standard discretize-then-optimize approach. We propose a general framework to speed up PDE-constrained optimization using online neural synthetic gradients (ONSG) with a novel two-scale optimization scheme.
  arXiv  Detail & Related papers  (2021-04-25T22:43:51Z)
• Physics-informed neural networks with hard constraints for inverse design [3.8191831921441337]
  We propose a new deep learning method -- physics-informed neural networks with hard constraints (hPINNs) -- for solving topology optimization. We demonstrate the effectiveness of hPINN for a holography problem in optics and a fluid problem of Stokes flow.
  arXiv  Detail & Related papers  (2021-02-09T03:18:15Z)
• Conditional gradient methods for stochastically constrained convex minimization [54.53786593679331]
  We propose two novel conditional gradient-based methods for solving structured convex optimization problems. The most important feature of our framework is that only a subset of the constraints is processed at each iteration. Our algorithms rely on variance reduction and smoothing used in conjunction with conditional gradient steps, and are accompanied by rigorous convergence guarantees.
  arXiv  Detail & Related papers  (2020-07-07T21:26:35Z)
• Cogradient Descent for Bilinear Optimization [124.45816011848096]
  We introduce a Cogradient Descent algorithm (CoGD) to address the bilinear problem. We solve one variable by considering its coupling relationship with the other, leading to a synchronous gradient descent. Our algorithm is applied to solve problems with one variable under the sparsity constraint.
  arXiv  Detail & Related papers  (2020-06-16T13:41:54Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.