Abstract
This study investigates switching characteristics of the magnesium fluoride (MgFx)-based bipolar resistive random-access memory (RRAM) devices at different operating ambiances (open-air and vacuum). Operating ambiances alter the elemental composition of the amorphous MgFx active layer and Ti/MgFx interface region, which affects the overall device performance. The experimental results indicate that filament type resistive switching takes place at the interface of Ti/MgFx and trap-controlled space charge limited conduction (SCLC) mechanisms is dominant in both the low and high resistance states in the bulk MgFx layer. RRAM device performances at different operating ambiances are also altered by MgFx active layer treatments (air exposure and annealing). Devices show the better uniformity, stability, and a higher on/off current ratio in vacuum compared to an open-air environment. The Ti/MgFx/Pt memory devices have great potential for future vacuum electronic applications.
Highlights
Resistive switching random access memory (RRAM) devices are one of the emerging non-volatile memory (NVM) technologies with two terminal metal/insulator/metal (MIM)structures [1,2]
The simple MIM structures make RRAMs integrated into dense crossbar arrays and traditional, complementary metal-oxide-semiconductors (CMOS) [2]
This study revealed that the performance of Ti/MgFx /Pt RRAM devices varies at different operating environments due to the variation in elemental compositions of the
Summary
Resistive switching random access memory (RRAM) devices are one of the emerging non-volatile memory (NVM) technologies with two terminal metal/insulator/metal (MIM)structures [1,2]. The simple MIM structures make RRAMs integrated into dense crossbar arrays and traditional, complementary metal-oxide-semiconductors (CMOS) [2]. Binary information (“0” and “1”) can be stored within one device cell using high and low resistance states, respectively. More information can be stored within a single device cell using multiple resistance states for multi-level information storage. RRAM devices show remarkable similarities to biological synapses, dendrites, and neurons at both the physical mechanism level and unit functionality level. These similarities make the RRAM-based neuromorphic computing a promising technology for future artificial intelligence [4]. Even though significant performance improvements in the RRAM device have been achieved, one remaining drawback is the large parameter variability, whose cause has been ascribed to moisture present in the atmospheric environment [6]
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