ScholarWorks@성균관대학교

초록

The recent surge in data generated by emerging applications has exposed intrinsic bottlenecks in conventional von Neumann architectures, where physically separated processing and memory units limit bandwidth and energy efficiency. Neuromorphic computing offers a brain-inspired approach that unifies computation and memory within a single hardware framework, enabling massively parallel, low-power information processing. Among various device candidates, memtransistors incorporate a gate terminal that enables independent modulation of resistive switching and channel conductance, thereby allowing the realization of complex learning functions and effectively suppressing sneak-path currents in array architectures. This perspective outlines the operation principles of memtransistors based on diverse physical mechanisms, including ion migration, charge trapping, ferroelectric switching, and phase transitions, and then discusses recent materials and architectural engineering strategies, with particular emphasis on two-dimensional channels and scalable array integration. Beyond device-level behavior, bio-inspired functionalities, such as heterosynaptic and homeostatic plasticity, are highlighted as key ingredients for stable and self-regulated learning in neural networks. The integration of memtransistors with sensory modules is further examined to enable near-sensor and in-sensor computing, paving the way for multimodal signal processing that parallels biological perception. Finally, critical challenges and opportunities in variability control, CMOS-compatible processing, and three-dimensional, multisensory integration are identified, indicating that continued progress in material design and architecture optimization will be essential for positioning memtransistors as key enablers of autonomous, bio-inspired intelligence in future robotics, healthcare, and cognitive electronics.

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