Abstract:TiN/AlOx:Ti/TaOx/TiN memory devices using bilayer resistive switching memory demonstrated excellent durability and capability of QLC (quad-level cell) memory devices. The best nonvolatile memory characteristics with the lowest operation current and optimized 4 bit/cell states were obtained using the Incremental Step Pulse Programming (ISPP) algorithm in array. As a result, a superior QLC reliability (cycle endurance > 1 k at each level of the QLC, data retention > 2 h at 125 °C) for all the 4 bits/cell o… Show more
“…This analysis indicates that TiON was formed between the HfO x and TiN layers. The set behavior occurring in the negative region was also formed by this TiON layer, which can perform a gradual set operation by acting as an oxygen reservoir [ 51 , 52 ]. Fig.…”
With the development of artificial intelligence and the importance of big data processing, research is actively underway to break away from data bottlenecks and modern Von Neumann architecture computing structures that consume considerable energy. Among these, hardware technology for neuromorphic computing is in the spotlight as a next-generation intelligent hardware system because it can efficiently process large amounts of data with low power consumption by simulating the brain’s calculation algorithm. In addition to memory devices with existing commercial structures, various next-generation memory devices, including memristors, have been studied to implement neuromorphic computing. In this study, we evaluated the synaptic characteristics of a resistive random access memory (ReRAM) with a Ru/HfOx/TiN structure. Under a series of presynaptic spikes, the device successfully exhibited remarkable long-term plasticity and excellent nonlinearity properties. This synaptic device has a high operating speed (20 ns, 50 ns), long data retention time (> 2 h @85 ℃) and high recognition rate (94.7%). Therefore, we propose that memory and learning capabilities can be used as promising HfOx-based memristors in next-generation artificial neuromorphic computing systems.
“…This analysis indicates that TiON was formed between the HfO x and TiN layers. The set behavior occurring in the negative region was also formed by this TiON layer, which can perform a gradual set operation by acting as an oxygen reservoir [ 51 , 52 ]. Fig.…”
With the development of artificial intelligence and the importance of big data processing, research is actively underway to break away from data bottlenecks and modern Von Neumann architecture computing structures that consume considerable energy. Among these, hardware technology for neuromorphic computing is in the spotlight as a next-generation intelligent hardware system because it can efficiently process large amounts of data with low power consumption by simulating the brain’s calculation algorithm. In addition to memory devices with existing commercial structures, various next-generation memory devices, including memristors, have been studied to implement neuromorphic computing. In this study, we evaluated the synaptic characteristics of a resistive random access memory (ReRAM) with a Ru/HfOx/TiN structure. Under a series of presynaptic spikes, the device successfully exhibited remarkable long-term plasticity and excellent nonlinearity properties. This synaptic device has a high operating speed (20 ns, 50 ns), long data retention time (> 2 h @85 ℃) and high recognition rate (94.7%). Therefore, we propose that memory and learning capabilities can be used as promising HfOx-based memristors in next-generation artificial neuromorphic computing systems.
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