Memory materials and devices: From concept to application

Abstract

Memory cells have always been an important element of information technology. With emerging technologies like big data and cloud computing, the scale and complexity of data storage has reached an unprecedented peak with a much higher requirement for memory technology. As is well known, better data storage is mostly achieved by miniaturization. However, as the size of the memory device is reduced, a series of problems, such as drain gate‐induced leakage, greatly hinder the performance of memory units. To meet the increasing demands of information technology, novel and high‐performance memory is urgently needed. Fortunately, emerging memory technologies are expected to improve memory performance and drive the information revolution. This review will focus on the progress of several emerging memory technologies, including two‐dimensional material‐based memories, resistance random access memory (RRAM), magnetic random access memory (MRAM), and phase‐change random access memory (PCRAM). Advantages, mechanisms, and applications of these diverse memory technologies will be discussed in this review.

Stimuli‐Enabled Artificial Synapses for Neuromorphic Perception: Progress and Perspectives

“…[16] The use of 2D van der Waals (vdW) structures in synaptic devices for neuromorphic computing has provoked considerable interest because of the intriguing physical and chemical properties of these structures associated with their strictly defined low dimensionalities, [42,65] including their large surface-to-volume ratios, [33,66] dangling-bond-free surfaces, [65,67,68] and highly gatetunable bandgaps. [69][70][71][72] A variety of layered crystals, including MoO 3 , MoS 2 , WS 2 , and WSe 2 , can emulate both STP and LTP effectively. Zhu et al [33] demonstrated nanoscale synaptic transistors based on a vdW heterostructure that was composed of TMDs and phosphorus trichalcogenides.…”

Two-dimensional ferroelectric channel transistors integrating ultra-fast memory and neural computing

“…22 2D layered semiconductors with atomic thickness possess the potential for continuous shrinking, [23][24][25] which is a promising candidate for future high-density memory and computing systems. 26,27 Particularly, 2D layered α-In2Se3 exhibits robust ferroelectricity at room temperature (RT) without annealing, 14,28,29 and thanks to the intrinsic interlocking of dipoles in α-In2Se3, 18,22 it can maintain ferroelectric polarization even at atomic scale.…”

Improving the Recognition Accuracy of Memristive Neural Networks via Homogenized Analog Type Conductance Quantization

“…Resembling the operating principle of human brains that transmit and process information through huge amounts of interconnected neurons and synapses [1][2][3][4], neuromorphic computing paradigm based on new solid-state electronic devices have demonstrated advantages of high efficiency, low power consumption, and parallel processing ability when handling big-data analysis tasks [5][6][7]. Plenty of research efforts have been devoted to emulating the electrical functions of biological synapses and neurons with ferroelectric, magnetic, phase-change, and resistive switching devices [8][9][10], wherein the resistive switching memristors distinguish themselves as promising post-Moore era candidates with their simple device structure, crossbar array, and 3D stacking capability for very large scale integration [11][12][13][14][15][16][17]. In particular, the ion migration and filamentary conduction mechanism make the memristor devices extremely scalable, enabling them to easily approach the lithographic limitations [18][19][20][21].…”