Conformal, ultrathin crystalline-silicon-based Hall sensor arrays with deep learning models for early-stage monitoring of three-dimensional tumor tissues

Dynamic dimension assessments of tumor tissues have broad relevance in clinical diagnosis and the treatment of patients. Current technologies for such a purpose include quasi-static measurements that lack microscale resolution and sensing sites, with limited capabilities for time-dependent, three-dimensional profiling of tumors, particularly at the early growth stage. Here, we report the conformal Hall-sensor-based systems for continuous monitoring of tumor morphological features such as growth rates and volumes. Such platforms incorporate ultrathin crystalline-silicon nanomembranes (200 nm thick) as a basis for displacement sensing via magnetic flux detection, in an array design that yields spatiotemporal information of tumor geometries at high sensitivity. Evaluation involves real-time measurements on a living mouse model with tumor tissues at various pathological conditions, where the integration with deep learning algorithms can further enable the system for large-scale tumor profile reconstruction across tissue surfaces. These microsystems provide the potential for monitoring of tumor progression and treatment guidance in patients.