Towards Neuromorphic Event-Based Sensing for High-Speed Multi-Spectral Classification and Tracking of Microparticles

Abstract

Conventional image-based microfluidic systems face an inherent trade-off between throughput, imaging speed, and data bandwidth, limiting their ability to monitor high-velocity flows without significant motion blur or prohibitive data generation. Event-based sensing has emerged as a high-speed, low-power alternative, but has so far been largely restricted to tracking monodisperse, spherical particles. In this work, we introduce a microfluidic sensing platform that enables the simultaneous extraction of kinematic and spectral information from polydisperse microparticles using a neuromorphic imaging approach. By integrating a spatially multiplexed RGB filter mask with an asynchronous event-based sensor, spectral signature and motion are encoded directly at the sensing stage, eliminating the need for image reconstruction or learning-based inference. The system achieves sub-millisecond temporal resolution and maintains robust classification performance across a broad range of particle sizes and flow velocities, including under non-laminar conditions, reaching up to 82% accuracy for classification of colored particles within the 0.08-0.18 mm range. The event-driven architecture reduces data bandwidth by >240x compared to conventional high-speed imaging, while sustaining an area throughput of 460 mm^2/s. By providing a computationally efficient and low-latency particle characterization, this framework paves the way for a scalable solution towards high-speed, label-free screening of heterogeneous analytes in clinical diagnostics and environmental monitoring.