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

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 ${}^{-4}$ 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.