ScholarWorks@동국대학교

초록

The continuous advancement of computing technology has led to an increasing demand for efficient data processing methods. As data processing tasks become increasingly complex, traditional von Neumann computing systems suffer from bottlenecks, limiting large-scale data handling and overall system performance. To overcome these limitations, neuromorphic computing, inspired by the structure and principles of the biological brain, has emerged as a promising alternative. By mimicking the information processing between neurons and synapses, neuromorphic computing enables rapid and energy-efficient data processing. We fabricated an RRAM array memristor and demonstrated its potential as an artificial synapse for neuromorphic computing applications. The fabricated Ni/SiN/TiN memristor successfully emulated various synaptic plasticity learning rules, including spike-timing-dependent plasticity, paired-pulse facilitation. Based on its weight update characteristics, the memristor-based pattern recognition system achieved an accuracy of 95.36 %. Furthermore, by applying a combination of voltage pulses, we replicated adaptive learning behavior in a Pavlovian conditioning experiment. Additionally, the memristor successfully mimicked key nociceptive properties, such as threshold responses, allodynia, and hyperalgesia. Finally, by tuning the reset and compliance current, we successfully implemented multi-level cell storage, and an appropriate sequence of write/erase pulses enabled binary encoding of decimal numbers from 0 to 15, demonstrating the potential for four-state edge computing applications. These results highlight the feasibility of RRAM array memristors for neuromorphic computing, artificial synapse emulation, and energy-efficient edge computing applications.

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