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Exa-scale computing needs to re-examine the existing hardware platform that can support intensive data-oriented computing. Since the main bottleneck is from memory, we aim to develop an energy-efficient in-memory computing platform in this book. First, the models of spin-transfer torque magnetic tunnel junction and racetrack memory are presented. Next, we show that the spintronics could be a candidate for future data-oriented computing for storage, logic, and interconnect. As a result, by utilizing spintronics, in-memory-based computing has been applied for data encryption and machine learning. The implementations of in-memory AES, Simon cipher, as well as interconnect are explained in details. In addition, in-memory-based machine learning and face recognition are also illustrated in this book.

Floating-gate transistors are increasingly used for digital and/or analogue non-volatile memory in standard CMOS integrated circuits. The mask design of the floating-gate's tunnelling junction, where erasure and/or writing occur, is examined. Aided by static and transient tunnelling current measurements for a variety of tunnelling junctions, recommendations for constructing these junctions to minimise the duration, power consumption and oxide degradation of programming are presented.

This paper presents silicon quantum dot channel (QDC) field effect transistors (FETs) and floating gate nonvolatile memory structures. The QDC-FET operation is explained by carrier transport in narrow mini-energy bands which are manifested in an array of SiO x -cladded silicon quantum dot layers. For nonvolatile memory structures, simulations of electron charge densities in the floating quantum dot layers are presented. Experimental threshold voltage shift in I D – V G characteristics is presented after the ‘Write’ cycle. The QDC-FETs and nonvolatile memory due to improved threshold voltage variations by incorporating the lattice-matched II–VI layer as the gate insulator.

Emerging nonvolatile memory (eNVM) devices are pushing the limits of emerging applications beyond the scope of silicon-based complementary metal oxide semiconductors (CMOS). Among several alternatives, phase change memory, spin-transfer torque random access memory, and resistive random-access memory (RRAM) are major emerging technologies. This review explains all varieties of prototype and eNVM devices, their challenges, and their applications. A performance comparison shows that it is difficult to achieve a “universal memory” which can fulfill all requirements. Compared to other emerging alternative devices, RRAM technology is showing promise with its highly scalable, cost-effective, simple two-terminal structure, low-voltage and ultra-low-power operation capabilities, high-speed switching with high-endurance, long retention, and the possibility of three-dimensional integration for high-density applications. More precisely, this review explains the journey and device engineering of RRAM with various architectures. The challenges in different prototype and eNVM devices is disused with the conventional and novel application areas. Compare to other technologies, RRAM is the most promising approach which can be applicable as high-density memory, storage class memory, neuromorphic computing, and also in hardware security. In the post-CMOS era, a more efficient, intelligent, and secure computing system is possible to design with the help of eNVM devices.

Nonvolatile memory technologies in Si-based electronics date back to the 1990s. Ferroelectric field-effect transistor (FeFET) was one of the most promising devices replacing the conventional Flash memory facing physical scaling limitations at those times. A variant of charge storage memory referred to as Flash memory is widely used in consumer electronic products such as cell phones and music players while NAND Flash-based solid-state disks (SSDs) are increasingly displacing hard disk drives as the primary storage device in laptops, desktops, and even data centers. The integration limit of Flash memories is approaching, and many new types of memory to replace conventional Flash memories have been proposed. Emerging memory technologies promise new memories to store more data at less cost than the expensive-to-build silicon chips used by popular consumer gadgets including digital cameras, cell phones and portable music players. They are being investigated and lead to the future as potential alternatives to existing memories in future computing systems. Emerging nonvolatile memory technologies such as magnetic random-access memory (MRAM), spin-transfer torque random-access memory (STT-RAM), ferroelectric random-access memory (FeRAM), phase-change memory (PCM), and resistive random-access memory (RRAM) combine the speed of static random-access memory (SRAM), the density of dynamic random-access memory (DRAM), and the nonvolatility of Flash memory and so become very attractive as another possibility for future memory hierarchies. Many other new classes of emerging memory technologies such as transparent and plastic, three-dimensional (3-D), and quantum dot memory technologies have also gained tremendous popularity in recent years. Subsequently, not an exaggeration to say that computer memory could soon earn the ultimate commercial validation for commercial scale-up and production the cheap plastic knockoff. Therefore, this review is devoted to the rapidly developing new class of memory technologies and scaling of scientific procedures based on an investigation of recent progress in advanced Flash memory devices.

The significant role and the great influence of microelectronic technology and industry on man's life have become an indisputable fact. This industry, relying on nanotechnology and its remarkable evolutional and revolutionary progresses, benefits from huge number of innovations and inventions and is going to play a major role in all areas of electronics, computers, information technology, aerospace defense, and consumer goods in the 21st century with an increasing momentum. The current business of semiconductor industry is over $300 billion, one fifth of which belongs to semiconductor memory. Unfortunately, Iran has no share in such a huge global market. In this paper, after a brief review of different types of volatile semiconductor memories and their short history and origin, their applications, advantages, shortcomings and technological limits during the last decade and the solutions to them will be explained. Finally, the volatile semiconductor memory development trend in future (short-term and long-term) will be described.

HighlightsFerroelectricity and domain dynamics of emerging ferroelectric AlScN films were discussed.The performance optimization of ferroelectric AlScN films grown by different deposition techniques was comprehensively analyzed.The challenges and perspectives regarding the commercial avenue of AlScN-based memories and in-memory computing applications were summarized.Ferroelectrics have great potential in the field of nonvolatile memory due to programmable polarization states by external electric field in nonvolatile manner. However, complementary metal oxide semiconductor compatibility and uniformity of ferroelectric performance after size scaling have always been two thorny issues hindering practical application of ferroelectric memory devices. The emerging ferroelectricity of wurtzite structure nitride offers opportunities to circumvent the dilemma. This review covers the mechanism of ferroelectricity and domain dynamics in ferroelectric AlScN films. The performance optimization of AlScN films grown by different techniques is summarized and their applications for memories and emerging in-memory computing are illustrated. Finally, the challenges and perspectives regarding the commercial avenue of ferroelectric AlScN are discussed.

Ferroelectric memory is a promising candidate for next‐generation nonvolatile memory owing to its outstanding performance such as low power consumption, fast speed, and high endurance. However, the ferroelectricity of conventional ferroelectric materials will be eliminated by the depolarization field when the size drops to the nanometer scale. As a result, the miniaturization of ferroelectric devices was hindered, which makes ferroelectric memory unable to keep up with the development of integrated‐circuit (IC) miniaturization. Recently, a two‐dimensional (2D) In2Se3 was reported to maintain stable ferroelectricity at the ultrathin scale, which is expected to break through the bottleneck of miniaturization. Soon, devices based on 2D In2Se3, including the ferroelectric field‐effect transistor, ferroelectric channel transistor, synaptic ferroelectric semiconductor junction, and ferroelectric memristor were demonstrated. However, a comprehensive understanding of the structures and the ferroelectric‐switching mechanism of 2D In2Se3 is still lacking. Here, the atomic structures of different phases, the dynamic mechanism of ferroelectric switching, and the performance/functions of the latest devices of 2D In2Se3 are reviewed. Furthermore, the correlations among the structures, the properties, and the device performance are analyzed. Finally, several crucial problems or challenges and possible research directions are put forward. We hope that this review paper can provide timely knowledge and help for the research community to develop 2D In2Se3 based ferroelectric memory and computing technology for practical industrial applications. Two‐dimensional (2D) In2Se3 is a novel ferroelectric capable of fighting against the depolarization field at nanoscale. Thus, 2D In2Se3‐based low‐power consumption, high‐density ferroelectric devices are promising candidates for data storage applications. This review summarizes the major advances in 2D In2Se3, including structures, properties, phase/switching transitions, and device performance. Prospects for its future development and research directions are also presented.

In the advancement of the semiconductor device technology, ZnO could be a prospective alternative than the other metal oxides for its versatility and huge applications in different aspects. In this review, a thorough overview on ZnO for the application of resistive switching memory (RRAM) devices has been conducted. Various efforts that have been made to investigate and modulate the switching characteristics of ZnO-based switching memory devices are discussed. The use of ZnO layer in different structure, the different types of filament formation, and the different types of switching including complementary switching are reported. By considering the huge interest of transparent devices, this review gives the concrete overview of the present status and prospects of transparent RRAM devices based on ZnO. ZnO-based RRAM can be used for flexible memory devices, which is also covered here. Another challenge in ZnO-based RRAM is that the realization of ultra-thin and low power devices. Nevertheless, ZnO not only offers decent memory properties but also has a unique potential to be used as multifunctional nonvolatile memory devices. The impact of electrode materials, metal doping, stack structures, transparency, and flexibility on resistive switching properties and switching parameters of ZnO-based resistive switching memory devices are briefly compared. This review also covers the different nanostructured-based emerging resistive switching memory devices for low power scalable devices. It may give a valuable insight on developing ZnO-based RRAM and also should encourage researchers to overcome the challenges.

This paper presents an overview of emerging memory technologies. It begins with the presentation of stand-alone and embedded memory technology evolution, since the appearance of Flash memory in the 1980s. Then, the progress of emerging memory technologies (based on filamentary, phase change, magnetic, and ferroelectric mechanisms) is presented with a review of the major demonstrations in the literature. The potential of these technologies for storage applications addressing various markets and products is discussed. Finally, we discuss how the rise of artificial intelligence and bio-inspired circuits offers an opportunity for emerging memory technology and shifts the application from pure data storage to storage and computing tasks, and also enlarges the range of required specifications at the device level due to the exponential number of new systems and architectures.