Emerging Non-volatile Memory Technologies (eBook)

This book offers a balanced and comprehensive guide to the core principles, fundamental properties, experimental approaches, and state-of-the-art applications of two major groups of emerging non-volatile memory technologies, i.e. spintronics-based devices as well as resistive switching devices, also known as Resistive Random Access Memory (RRAM). The first section presents different types of spintronic-based devices, i.e. magnetic tunnel junction (MTJ), domain wall, and skyrmion memory devices. This section describes how their developments have led to various promising applications, such as microwave oscillators, detectors, magnetic logic, and neuromorphic engineered systems. In the second half of the book, the underlying device physics supported by different experimental observations and modelling of RRAM devices are presented with memory array level implementation. An insight into RRAM desired properties as synaptic element in neuromorphic computing platforms from material and algorithms viewpoint is also discussed with specific example in automatic sound classification framework.

Dr. Lew is a full-time academic staff (Assistant Professor) at the School of Physical and Mathematical Sciences (Physics Division), Nanyang Technological University, Singapore. He received his PhD degree from the University of Cambridge, UK. Dr Lew has been working on magnetism and magnetic materials research for more than ten years in industrial and research laboratories. Prior to joining NTU, he worked as Research Associate at the Cavendish Laboratory. Dr Lew's research team specializes on spin-electronics or 'spintronics' devices: thin film sensor growth, micro- and nano-fabrication, device transport measurement, and micromagnetic modeling.
Prof. Teruo Ono is a full professor at the Institute of Chemical Research, Kyoto University, Japan.  He received his D.Sc from Kyoto University, Japan in 1996. Since then he worked at Keio University and Osaka University. In 2004, he joined Kyoto University as a full professor and established the Nanospintronics group where he has been leading the research in the field of spinelectronics.  His current research is focused on magnetic materials, spintronics and nanofabrication. He was the recipient of multiple awards; namely Japan IBM Science Award, the Sir Martin Wood prize. He has more than 200 papers in peer-reviewed scientific journals including Science, Nature, Nature Materials and Physical Review Letters.
Dr. Sarjoosing Goolaup is a Senior Research Fellow in the Division of Physics and Applied Physics at the Nanyang Technological University, Singapore.  He received his PhD degree in Electrical Engineering from the National University of Singapore in 2008.  He was the recipient of the Singapore Millennium Foundation Post-Doctoral Fellowship.  Dr Goolaup research focus is on spin transport in magnetic nanostructures, spin logic and magnetic materials.  Dr Goolaup has published over 40 papers in peer-reviewed scientific journals.

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This book offers a balanced and comprehensive guide to the core principles, fundamental properties, experimental approaches, and state-of-the-art applications of two major groups of emerging non-volatile memory technologies, i.e. spintronics-based devices as well as resistive switching devices, also known as Resistive Random Access Memory (RRAM). The first section presents different types of spintronic-based devices, i.e. magnetic tunnel junction (MTJ), domain wall, and skyrmion memory devices. This section describes how their developments have led to various promising applications, such as microwave oscillators, detectors, magnetic logic, and neuromorphic engineered systems. In the second half of the book, the underlying device physics supported by different experimental observations and modelling of RRAM devices are presented with memory array level implementation. An insight into RRAM desired properties as synaptic element in neuromorphic computing platforms from material and algorithms viewpoint is also discussed with specific example in automatic sound classification framework.