# Inverse design of photonic devices with strict foundry fabrication constraints

arXiv: https://arxiv.org/abs/2201.12965 · HTML: https://mlsanity.com/paper/2201.12965/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 41 references that are in the map.
- First posted on arXiv 31 Jan 2022.

## Builds on (most cited first, 5 of 5)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Estimating or Propagating Gradients Through Stochastic Neurons for Conditional Computation](https://mlsanity.com/paper/1308.3432/) (2013)
- [Quantized Neural Networks: Training Neural Networks with Low Precision Weights and Activations](https://mlsanity.com/paper/1609.07061/) (2016)
- [Understanding Straight-Through Estimator in Training Activation Quantized Neural Nets](https://mlsanity.com/paper/1903.05662/) (2019)
- [Inverse design of photonic crystals through automatic differentiation](https://mlsanity.com/paper/2003.00379/) (2020)

## Built on by (most cited first, 11 of 11)

- [SurCo: Learning Linear Surrogates For Combinatorial Nonlinear Optimization Problems](https://mlsanity.com/paper/2210.12547/) (2022)
- [GenCO: Generating Diverse Designs with Combinatorial Constraints](https://mlsanity.com/paper/2310.02442/) (2023)
- [MAPS: Multi-Fidelity AI-Augmented Photonic Simulation and Inverse Design Infrastructure](https://mlsanity.com/paper/2503.01046/) (2025)
- [Datasets and Benchmarks for Nanophotonic Structure and Parametric Design Simulations](https://mlsanity.com/paper/2310.19053/) (2023)
- [SEMU-Net: A Segmentation-based Corrector for Fabrication Process Variations of Nanophotonics with Microscopic Images](https://mlsanity.com/paper/2411.16973/) (2024)
- [Physics-guided and fabrication-aware inverse design of photonic devices using diffusion models](https://mlsanity.com/paper/2504.17077/) (2025)
- [Differentiating through binarized topology changes: Second-order subpixel-smoothed projection](https://mlsanity.com/paper/2601.10737/) (2026)
- [Gradient-Informed Bayesian and Interior Point Optimization for Efficient Inverse Design in Nanophotonics](https://mlsanity.com/paper/2602.18148/) (2026)
- [Machine Learning Models to Identify Promising Nested Antiresonance Nodeless Fiber Designs](https://mlsanity.com/paper/2603.13302/) (2026)
- [Harnessing Photonics for Machine Intelligence](https://mlsanity.com/paper/2604.10841/) (2026)
- [VortexChat: An agentic framework for autonomous multi-objective integrated photonic design](https://mlsanity.com/paper/2608.20688/) (2026)

## Abstract (arXiv)

We introduce a new method for inverse design of nanophotonic devices which guarantees that resulting 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.