Neuromorphic computing offers parallel data processing and low energy consumption and can be useful to replace conventional von Neumann computing. Memristors are two-terminal devices with varying conductance that can be used as synaptic arrays in hardware-based neuromorphic devices. In this research, we extensively investigate the analog symmetric multi-level switching characteristics of zinc tin oxide (ZTO)-based memristor devices for neuromorphic systems. A ZTO semiconductor layer is introduced between a complementary metal-oxide-semiconductor (CMOS) compatible Ni top electrode and a highly doped poly-Si bottom electrode. A variety of bio-realistic synaptic features are demonstrated, including long-term potentiation (LTP), long-term depression (LTD), and spike timing-dependent plasticity (STDP). The Ni/ZTO/Si device in which the adjustment of the number of states in conductance is realized by applying different pulse schemes is highly suitable for hardware-based neuromorphic applications. We evaluate the pattern recognition accuracy by implementing a system-level neural network simulation with ZTO-based memristor synapses. The density of states (DOS) and charge density plots reveal that oxygen vacancies in ZTO assist in generating resistive switching in the Ni/ZTO/Si device. The proposed ZTObased memristor composed of metal-insulator-semiconductor (MIS) structure is expected to contribute to future neuromorphic applications through further studies.

In this Letter, we present reset-voltage-dependent precise tuning operation of TiOx/Al2O3-based memristive devices. For the high resistance state (HRS) with high reset voltage, abrupt set operations are observed with a large variation, while the HRS obtained by low reset voltage provides gradual and uniform switching behaviors. The improvement of gradual switching and the programming accuracy are analyzed regarding cycle-to-cycle as well as device-to-device variations. We believe that these results can be applied to operate TiOx/Al2O3-based memristors in areas requiring highly accurate tuning characteristics.

Recently, ferroelectric tunnel junctions (FTJs) have gained extensive attention as possible candidates for emerging memory and synaptic devices for neuromorphic computing. However, the working principles of the FTJ remain controversial...

In this paper, we demonstrate retention improvement in nonvolatile charge-trapping memory cells by tunneling oxide engineering with Al 2 O 3 . By utilizing SiO 2 /Al 2 O 3 /SiO 2 layers for the tunneling oxide, it is shown that the threshold voltage window after 10 years is significantly improved from 0.78 V to 4.18 V through Synopsys Sentaurus technology computer-aided design simulation. In addition, retention improvement from incorporating SiO 2 /Al 2 O 3 /SiO 2 tunneling layers is compared with that using SiO 2 /Si 3 N 4 /SiO 2 tunneling layers. The relationship between charge-trapping layer thickness and trapped charge emission is also investigated. As a result, we open up the possibility of using HfO 2 as a charge-trapping layer with significant reliability enhancement.