On-chip wave chaos for photonic extreme learning

2025-08-27

AI Summary

Scalable photonic neural networks face a fundamental trade-off between computational efficiency and hardware simplicity. Method: This work proposes an on-chip photonic extreme learning machine (ELM) leveraging wave-chaotic interference in mesa-type microcavities. Disordered SU-8 polymer microcavities are fabricated on glass substrates via direct laser writing; single-frequency tunable lasers encode inputs via wavelength modulation, while spatial speckle patterns generated by scattering walls from leaky cavity modes provide high-dimensional nonlinear feature mapping; flexible, reconfigurable optical readout enables task-adaptive output optimization. Contribution/Results: This is the first demonstration integrating wave-chaotic wavelength sensitivity with disorder-induced speckle for photonic ELMs—eliminating the need for on-chip nonlinear elements or complex weight tuning. The system achieves >95% classification accuracy across four benchmark tasks, maintaining robust performance and tunability under varying readout dimensions.

Technology Category

• Machine Learning: Hardware-aware ML
• Cognitive Modeling & Cognitive Systems: Neural Spike Coding
• Computer Vision: Learning & Optimization for CV

Application Category

• Economics, Online Markets and Human Computation: Architectures and workflows that use LLMs for crowd work
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Responsible Web: Machine-in-the-loop, human agency and autonomy

Abstract

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.

Problem

• Develops photonic extreme learning machine using wave chaos
• Leverages wavelength sensitivity in microcavities for information encoding
• Optimizes readout size for different classification tasks performance

Innovation

• Wave chaos interference in microcavity
• Wavelength encoding with tunable laser
• Scattering wall as readout layer