Massively Multiplexed Photonic Chip With Cross‐Channel Fusion For Harmonized Biosensing

ABSTRACT Accurate, label‐free analysis of complex biological mixtures is limited by poor multiplexing scalability and inconsistent responses across sensor samples. We introduce a transformative sensing platform that integrates large‐scale photonic multiplexing with data‐driven analytics to overcome these constraints. Our modular, affordable glass‐chip whispering–gallery–mode (WGM) photonic architecture is designed to support simultaneous readout of up to 100 sensing channels comprising over 10 000 high‐Q microresonators, exceeding existing multiplexing capabilities by more than two orders of magnitude. The photonic system is reinforced by the novel, cross‐platform, deep learning framework BioCCF. It applies cross‐channel fusion in biosensing for the first time and enables harmonized operation across heterogeneous chips by leveraging domain adaptation. Using over 200 h of measurements from nine independent chips, we demonstrate chip‐independent multiplexed biosensing with 99.3% identification accuracy and relative quantification errors of 10 for immunoglobulin G mixtures. Beyond this validation, the platform supports multi‐analyte immunoassays, environmental monitoring, and distributed diagnostics, enabling aligned sensor networks with shared data infrastructures and continuously improving generalization of the common analyzing framework.