Bio-Semiconductor Technologies: From Memristors to Neuromorphic sensing Networks

With the deep integration of life science and semiconductor technology, high-sensitivity biosensing and intelligent signal processing have become key directions in next-generation bioelectronics. Biosensing plays a crucial role in early disease detection and cancer risk prediction, offering substantial application potential. Meanwhile, the global biosensor market continues to expand, driven by the increasing need for point-of-care testing, minimally invasive diagnostics, and intelligent medical devices. However, conventional biosensors suffer from complex detection procedures, low integration levels, and poor signal stability, which limit their capability for real-time and high-throughput analysis.

This dissertation introduces a new bio-semiconductor technology framework that unifies semiconductor device physics with interfacial molecular engineering. Experimental investigations reveal that quasi one-dimensional (1D) silicon nanowires (SiNW) exhibit multiple stable and reversible memristive behaviors that can be systematically modulated through interfacial oxidation and controlled surface chemistry. By integrating engineered biofunctionalization pathways, a memristive biosensing system is realized that directly couples molecular recognition processes with intrinsic switching kinetics of the semiconductor device. This approach enables quantitative and label-free detection of small biomolecules while preserving device-level electrical consistency. The proposed architecture supports high-density and multi-channel molecular analysis and provides a compact and manufacturing-compatible route for constructing multifunctional bioelectronic sensors.

Building on this platform, the dissertation further advances neuromorphic biosensing through the dynamic response characteristics of memristive elements. The switching kinetics of the device exhibit strong analogies to synaptic plasticity, allowing biochemical concentration information to be mapped into spiking-like electronic outputs. Network-level demonstrations using memristor arrays show that molecular inputs can be transformed into hardware-encoded signatures, enabling device-level decision making relevant to early cancer risk prediction and biomarker classification. This establishes a rigorous integration pathway from semiconductor physics to neuromorphic network operation, bridging low-level device behavior with high-level functional intelligence.

The demonstrated mechanisms offer new opportunities for implementing autonomous diagnostic nodes capable of real-time molecular sensing without relying on bulky external equipment. Such systems hold significant potential for continuous disease monitoring, early-stage cancer surveillance, and integration into wearable or implantable medical devices. As biosensor markets continue expanding toward intelligent hardware platforms, the technology explored in this dissertation provides an essential foundation for future commercial biosensing systems.

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