# Three dimensional waveguide-interconnects for scalable integration of photonic neural networks

arXiv: https://arxiv.org/abs/1912.08203 · HTML: https://mlsanity.com/paper/1912.08203/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 Dec 2019.

## Built on by (most cited first, 3 of 3)

- [Noise mitigation strategies in physical feedforward neural networks](https://mlsanity.com/paper/2204.09461/) (2022)
- [Convergence and scaling of Boolean-weight optimization for hardware reservoirs](https://mlsanity.com/paper/2305.07908/) (2023)
- [Programmable metasurfaces for future photonic artificial intelligence](https://mlsanity.com/paper/2505.11659/) (2025)

## Abstract (arXiv)

Photonic waveguides are prime candidates for integrated and parallel photonic interconnects. Such interconnects correspond to large-scale vector matrix products, which are at the heart of neural network computation. However, parallel interconnect circuits realized in two dimensions, for example by lithography, are strongly limited in size due to disadvantageous scaling. We use three dimensional (3D) printed photonic waveguides to overcome this limitation. 3D optical-couplers with fractal topology efficiently connect large numbers of input and output channels, and we show that the substrate's footprint area scales linearly. Going beyond simple couplers, we introduce functional circuits for discrete spatial filters identical to those used in deep convolutional neural networks.