From Device-Level Implementation to In-Sensor Computing in Memristive-Device-Based Biosensors: A Review
Abstract
Memristive devices have attracted considerable attention as promising candidates for overcoming the energy and data-transfer limitations of conventional computing architectures. In particular, their integration with biosensors offers a pathway toward compact and energy-efficient diagnostic systems. This review examines the development of memristive-device-based biosensors from device-level transduction to system-level integration. At the device level, sensing strategies have evolved from direct sensing toward indirect sensing architectures, improving stability and reusability. At the system level, conventional off-chip implementations have progressively shifted toward fully integrated on-chip implementations. Furthermore, this review highlights the emerging paradigm of in-sensor computing, in which sensing, memory, and computation are co-located within a single physical platform. This approach enables reduced data movement and supports energy-efficient operation for point-of-care applications. Finally, key challenges-including CMOS compatibility, device variability, and reliable multi-threshold sensing operation-are discussed as critical factors for the practical realization of memristive-device-based electrochemical biosensing systems.
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