Preface
A common driving force in the development of emerging memory technologies is
the urgency to overcome challenges faced by transistor-based memory and logic.
Semiconductor devices have to keep pace with the increasing demands for even
faster speeds, smaller footprint, and greater efficiency, while reducing the total
energy consumption. For integrated circuits to keep up with increasing transistor
density over the next few decades, novel approaches such as new transistor
architectures, 3D stacking, advanced lithography techniques, and new materials are
imperative. While these concerns are being tackled, it is also worth looking into
emerging, beyond-CMOS technologies. Even the International Roadmap for
Devices and Systems (IRDS) sees beyond-CMOS as a milestone in the evolution of
computing. Two very promising emerging memory technologies currently in high
industry demand are spintronic-based devices such as magnetic random-access
memory (MRAM), and resistive switching devices also known as resistive
random-access memory (RRAM).
In spintronic-based devices, the spin property of electrons is exploited alongside
its conventional charge property. The electronic spin that manifests as magnetization can be used as memory elements for non-volatile information storage, such as
in commercially available MRAM, using magnetic tunnel junctions (MTJ). On the
other hand, RRAM devices utilize the intrinsic defects modulation of metal–insulator–metal structures under an external electric field. The change in the structural
defects configuration results in significant alteration in conductance. RRAM devices have been implemented as both memory unit and computing element due to
their unique device dynamics and highly scalable architecture.
The dynamic response of spintronic and resistive switching devices continue to
be thoroughly investigated. With advanced material and device engineering, the
characteristics of these devices can be rigorously tuned to achieve functional
dynamics such as stochastic and deterministic behaviours. This aspect of the device
has an especially crucial role in the development of non-conventional computing
and neuromorphic engineering applications.
v
A common driving force in the development of emerging memory technologies is
the urgency to overcome challenges faced by transistor-based memory and logic.
Semiconductor devices have to keep pace with the increasing demands for even
faster speeds, smaller footprint, and greater efficiency, while reducing the total
energy consumption. For integrated circuits to keep up with increasing transistor
density over the next few decades, novel approaches such as new transistor
architectures, 3D stacking, advanced lithography techniques, and new materials are
imperative. While these concerns are being tackled, it is also worth looking into
emerging, beyond-CMOS technologies. Even the International Roadmap for
Devices and Systems (IRDS) sees beyond-CMOS as a milestone in the evolution of
computing. Two very promising emerging memory technologies currently in high
industry demand are spintronic-based devices such as magnetic random-access
memory (MRAM), and resistive switching devices also known as resistive
random-access memory (RRAM).
In spintronic-based devices, the spin property of electrons is exploited alongside
its conventional charge property. The electronic spin that manifests as magnetization can be used as memory elements for non-volatile information storage, such as
in commercially available MRAM, using magnetic tunnel junctions (MTJ). On the
other hand, RRAM devices utilize the intrinsic defects modulation of metal–insulator–metal structures under an external electric field. The change in the structural
defects configuration results in significant alteration in conductance. RRAM devices have been implemented as both memory unit and computing element due to
their unique device dynamics and highly scalable architecture.
The dynamic response of spintronic and resistive switching devices continue to
be thoroughly investigated. With advanced material and device engineering, the
characteristics of these devices can be rigorously tuned to achieve functional
dynamics such as stochastic and deterministic behaviours. This aspect of the device
has an especially crucial role in the development of non-conventional computing
and neuromorphic engineering applications.
v