Graphite-based selectorless RRAM: improvable intrinsic nonlinearity for array applications

Han

Adv Materials Technologies

et al. 2018

194

126

required. Several types of emerging mem ories have been researched in the past few decades such as magnetic memory, phase change memory, ferroelectric tunnel junc tions, and resistive switching memory. Among these emerging devices, resistive switching memory called memristors, introduced by Chua in 1971, [1] have strong points of small cell size, nonvolatile and random data access possibility, easy fabri cation process, and simple structure. [2,3] Because of these advantages, various mate rials are examined for achieving memris tive properties.In addition, different from the past sev eral decades, information is being made depending on experiences or repeated stimuli similar to that in the human brain. The human brain contains ≈10 11 neurons and 10 15 synapses, occupies a small space, and consumes less than 20 W, which is lower than the power required to run a household light bulb. [4][5][6] Moreover, the human brain is currently considered as the most intelligent and fastest operation system. Therefore, neuromorphic computing, which emu lates the human brain, has been regarded as a promising nextgeneration computing system. Studies on neuromorphic computing have been rapidly growing and highlighted for various applications such as artificial intelligence, sensors, robotic devices, and memory devices.Existing neural networks are implemented by the combination of machine learning as software and the von Neumann archi tecture as hardware based on the complementary metaloxide semiconductor (CMOS) technology. However, CMOSbased cir cuits require 6-12 transistors and the design is not flexible. [7] The present computing system with the von Neumann architecture is implemented by a serial operation through a central processing unit (CPU). Because of the von Neumann bottleneck, memory devices have limitations in data processing speed between memory and CPU and require high power and large space. [8][9][10] Therefore, a new neuromorphic computing system that is exe cuted by parallel operation with a high operation speed, low energy consumption, and small volume is critically required.To achieve such requirement, memristive materials have been actively examined as emulating several functions of human brain. A memristor could act as a single unit of synapse without software programming supports. Memristorbased neu romorphic architecture is implemented by parallel operation with efficient power, small volume, and high data processing Neuromorphic architectures are in the spotlight as promising candidates for substituting current computing systems owing to their high operation speed, scale-down ability, and, especially, low energy consumption. Among candidate materials, memristors have shown excellent synaptic behaviors such as spike time-dependent plasticity and spike rate-dependent plasticity by gradually changing their resistance state according to electrical input stimuli. Memristor can work as a single synapse without programming support, which remarkably satisfies the requirements of neuromorphic computing. Here, the mo...

Section: D Materialsmentioning

confidence: 99%

Recent Advances in Memristive Materials for Artificial Synapses

Han

Adv Materials Technologies

et al. 2018

194

126

“…In our previous work, we reported the selectorless RRAM in high-k/low-k bilayer stacks, in which the intrinsic nonlinearity has been demonstrated by inserting a low-k layer (e.g. SiO x layer or graphite oxide layer) and optimized by SET compliance current limit (CCL) modulation 21–24 . In addition, the bilayer or multilayer nonuniform metal-oxide-stacked structures for self-rectifying behavior have been studied, e.g.…”

Section: Introductionmentioning

confidence: 99%

A Novel Resistive Switching Identification Method through Relaxation Characteristics for Sneak-path-constrained Selectorless RRAM application

Chen

Lin

et al. 2019

Sci Rep

Self Cite

Resistive random access memory (RRAM) is a leading candidate in the race towards emerging nonvolatile memory technologies. The sneak path current (SPC) problem is one of the main difficulties in crossbar memory configurations. RRAM devices with desirable properties such as a selectorless, 1R-only architecture with self-rectifying behavior are potential SPC solutions. In this work, the intrinsic nonlinear (NL) characteristics and relaxation characteristics of bilayer high-k/low-k stacked RRAMs are presented. The intrinsic nonlinearity reliability of bilayer selectorless 1R-only RRAM without additional switches has been studied for their ability to effectively suppress SPC in RRAM arrays. The relaxation properties with resistive switching identification method by utilizing the activation energy (Ea) extraction methodology is demonstrated, which provides insights and design guidance for non-uniform bilayer selectorless 1R-only RRAM array applications.

Advanced Intelligent Systems

“…Molybdenum disulfide (MoS 2 ), graphene and black phosphorus (BP) have been highlighted as the potential 2D materials candidates for complicated neuromorphic systems . Bulk MoS 2 exists an indefinite bandgap, and hence it is not really ideal for optoelectronic devices.…”

Section: Emerging Materials‐based Synaptic Devicesmentioning

confidence: 99%

Recent Progress in Photonic Synapses for Neuromorphic Systems

Zhang

Dai

Zhao

et al. 2020

154

118

Implementing synaptic functions with electronic devices is critical to achieve neuromorphic systems on the hardware platform, as synapses play important roles in brain computing and memory. Synapses modulated by light signals, which are also referred as photonic synapses, can not only make effective use of the outstanding properties of light to provide devices with ultrahigh propagation speed, high bandwidth and low crosstalk but also provide a noncontact writing method, which can facilitate the evolution of optical wireless communication and operation. More importantly, real‐time image processing can also be performed by photonic synapses which possess temporary memory. Thus far, tremendous efforts have been taken to design and fabricate photonic synapses. Herein, a summary of the development of different kinds of emerging materials utilized in photonic synaptic devices including memristors, field‐effect transistors, and phase change memory is presented, followed by the innovative applications of photonic synapses for neuromorphic systems. Finally, some current challenges and future study directions are discussed.