# On-chip wave chaos for photonic extreme learning

arXiv: https://arxiv.org/abs/2508.19878 · HTML: https://mlsanity.com/paper/2508.19878/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 5 of its 23 references that are in the map.
- First posted on arXiv 27 Aug 2025.

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

- [Scalable Optical Learning Operator](https://mlsanity.com/paper/2012.12404/) (2020)
- [Random Projections through multiple optical scattering: Approximating kernels at the speed of light](https://mlsanity.com/paper/1510.06664/) (2015)
- [Roadmap to Neuromorphic Computing with Emerging Technologies](https://mlsanity.com/paper/2407.02353/) (2024)
- [Ultrafast single-channel machine vision based on neuro-inspired photonic computing](https://mlsanity.com/paper/2302.07875/) (2023)
- [Weak Kerr Nonlinearity Boosts the Performance of Frequency-Multiplexed Photonic Extreme Learning Machines: A Multifaceted Approach](https://mlsanity.com/paper/2312.12296/) (2023)

## Abstract (arXiv)

The increase in demand for scalable and energy efficient artificial neural networks has put the focus on novel hardware solutions. Integrated photonics offers a compact, parallel and ultra-fast information processing platform, specially suited for extreme learning machine (ELM) architectures. Here we experimentally demonstrate a chip-scale photonic ELM based on wave chaos interference in a stadium microcavity. By encoding the input information in the wavelength of an external single-frequency tunable laser source, we leverage the high sensitivity to wavelength of injection in such photonic resonators. We fabricate the microcavity with direct laser writing of SU-8 polymer on glass. A scattering wall surrounding the stadium operates as readout layer, collecting the light associated with the cavity's leaky modes. We report uncorrelated and aperiodic behavior in the speckles of the scattering barrier from a high resolution scan of the input wavelength. Finally, we characterize the system's performance at classification in four qualitatively different benchmark tasks. As we can control the number of output nodes of our ELM by measuring different parts of the scattering barrier, we demonstrate the capability to optimize our photonic ELM's readout size to the performance required for each task.