Tuning resistive switching in complex oxide memristors

Ahmed, T 2017, Tuning resistive switching in complex oxide memristors, Doctor of Philosophy (PhD), Engineering, RMIT University.

Document type: Thesis
Collection: Theses

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Title Tuning resistive switching in complex oxide memristors
Author(s) Ahmed, T
Year 2017
Abstract The continuous demand of lightweight portable, cheap and low-power devices has pushed the electronic industry to the limits of the current technology. Flash memory technology which represents the mainstream non-volatile memories has experienced an impressive development over the last decade. This led their fabrication down to a 16 nm node and implementation of high-density 3D memory architectures. Due to the scaling limit of Flash technology, the need of new memories that combine the characteristics of a Flash but overcome the scaling limits is increasing. In this surge, oxide-based resistive memories – also called memristors – have emerged as a new family of storage-class memory. The extremely simple physical structure fast response, low cost and power consumption render resistive memories as a valid alternative of the Flash technology and an optimal choice for the next generation memory technology. The nanoscale resistive memories have demonstrated a variety of memory characteristics which depends on the electrochemical properties of the oxide system and several physical parameters including device structure and electrical biasing conditions. This indicates a complex nature of the underlying microscopic switching mechanisms which require a thorough understanding in order to fully benefit from the virtue of this technology.

The work presented in this Doctoral Dissertation focuses on the realization and fine tuning the memory characteristics of SrTiO3 based resistive switching memories. A novel synthesis route is adopted to realize highly complementary metal oxide semiconductor (CMOS) compatible nanoscale memristive devices and engineer the composition of the functional SrTiO3 perovskite oxide. By following the novel synthesis approach, SrTiO3 memristive devices with different stoichiometry such as different concentration of oxygen vacancies, metallic dopant species and physical structures are fabricated to achieve multifunctional characteristics of these devices. Rigorous electrical and material characterizations are carried out to analyze the resistive switching performance and understand the underlying microscopic mechanisms.

Stable multi-state resistive switching is demonstrated in donor (Nb) doped oxygen-deficient amorphous SrTiO3 (Nb:a-STOx) memories. The dynamics of multi-state switching behavior and the effect of Nb-doping on tuning the resistive switching are investigated by utilizing a combination of interfacial compositional evaluation and activation energy measurements. Furthermore, multiple switching behaviors in a single acceptor (Cr) doped amorphous SrTiO3 (Cr:a-STOx) memory cell are demonstrated. A physical model is also suggested to explain the novel switching characteristics of these versatile memristive devices.

A highly transparent and multifunctional SrTiO3 based memory system is fabricated which offers a reliable data storage and photosensitive platform for further transparent electronics. Also a unique photoluminescence mapping is presented as an identification technique for localized conduction mechanism in oxide resistive memories.

Finally, SrTiO3 resistive memories are engineered to mimic biological synapses. A hybrid CMOS-memristor approached is presented to demonstrate first implementation of higher order time and rate dependent synaptic learning rules. Furthermore, these artificial synapses are tuned for energy-efficient performance to highlight their potential for the future neuromorphic networks.
Degree Doctor of Philosophy (PhD)
Institution RMIT University
School, Department or Centre Engineering
Subjects Microtechnology
Microelectronics and Integrated Circuits
Functional Materials
Keyword(s) resistive memory
strontium titanate
resistive switching
perovskite oxide
artificial synapse
transparent memories
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Created: Tue, 13 Feb 2018, 08:32:14 EST by Denise Paciocco
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