Browse
Submit Data
Databases
API
Help

Dataset Information

26 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

Self-rectifying resistive memory in passive crossbar arrays.

ABSTRACT: Conventional computing architectures are poor suited to the unique workload demands of deep learning, which has led to a surge in interest in memory-centric computing. Herein, a trilayer (Hf_0.8Si_0.2O₂/Al₂O₃/Hf_0.5Si_0.5O₂)-based self-rectifying resistive memory cell (SRMC) that exhibits (i) large selectivity (ca. 10⁴), (ii) two-bit operation, (iii) low read power (4 and 0.8 nW for low and high resistance states, respectively), (iv) read latency (<10 μs), (v) excellent non-volatility (data retention >10⁴ s at 85 °C), and (vi) complementary metal-oxide-semiconductor compatibility (maximum supply voltage ≤5 V) is introduced, which outperforms previously reported SRMCs. These characteristics render the SRMC highly suitable for the main memory for memory-centric computing which can improve deep learning acceleration. Furthermore, the low programming power (ca. 18 nW), latency (100 μs), and endurance (>10⁶) highlight the energy-efficiency and highly reliable random-access memory of our SRMC. The feasible operation of individual SRMCs in passive crossbar arrays of different sizes (30 × 30, 160 × 160, and 320 × 320) is attributed to the large asymmetry and nonlinearity in the current-voltage behavior of the proposed SRMC, verifying its potential for application in large-scale and high-density non-volatile memory for memory-centric computing.

SUBMITTER: Jeon K

PROVIDER: S-EPMC8137934 | biostudies-literature |

REPOSITORIES: biostudies-literature

ACCESS DATA

Json Xml

Similar Datasets

Three-dimensional crossbar arrays of self-rectifying Si/SiO2/Si memristors.

Project description:Memristors are promising building blocks for the next-generation memory and neuromorphic computing systems. Most memristors use materials that are incompatible with the silicon dominant complementary metal-oxide-semiconductor technology, and require external selectors in order for large memristor arrays to function properly. Here we demonstrate a fully foundry-compatible, all-silicon-based and self-rectifying memristor that negates the need for external selectors in large arrays. With a p-Si/SiO2/n-Si structure, our memristor exhibits repeatable unipolar resistance switching behaviour (105 rectifying ratio, 104 ON/OFF) and excellent retention at 300 °C. We further build three-dimensinal crossbar arrays (up to five layers of 100 nm memristors) using fluid-supported silicon membranes, and experimentally confirm the successful suppression of both intra- and inter-layer sneak path currents through the built-in diodes. The current work opens up opportunities for low-cost mass production of three-dimensional memristor arrays on large silicon and flexible substrates without increasing circuit complexity.

| S-EPMC5465358 | biostudies-literature

Self-Rectifying Resistive Switching and Short-Term Memory Characteristics in Pt/HfO2/TaOx/TiN Artificial Synaptic Device.

Project description:Here, we propose a Pt/HfO2/TaOx/TiN artificial synaptic device that is an excellent candidate for artificial synapses. First, XPS analysis is conducted to provide the dielectric (HfO2/TaOx/TiN) information deposited by DC sputtering and atomic layer deposition (ALD). The self-rectifying resistive switching characteristics are achieved by the asymmetric device stack, which is an advantage of the current suppression in the crossbar array structure. The results show that the programmed data are lost over time and that the decay rate, which is verified from the retention test, can be adjusted by controlling the compliance current (CC). Based on these properties, we emulate bio-synaptic characteristics, such as short-term plasticity (STP), long-term plasticity (LTP), and paired-pulse facilitation (PPF), in the self-rectifying I-V characteristics of the Pt/HfO2/TaOx/TiN bilayer memristor device. The PPF characteristics are mimicked by replacing the bio-stimulation with the interval time of paired pulse inputs. The typical potentiation and depression are also implemented by optimizing the set and reset pulse. Finally, we demonstrate the natural depression by varying the interval time between pulse inputs.

| S-EPMC7693614 | biostudies-literature

Uniform self-rectifying resistive random-access memory based on an MXene-TiO2 Schottky junction.

Project description:For filamentary resistive random-access memory (RRAM) devices, the switching behavior between different resistance states usually occurs abruptly, while the random formation of conductive filaments usually results in large fluctuations in resistance states, leading to poor uniformity. Schottky barrier modulation enables resistive switching through charge trapping/de-trapping at the top-electrode/oxide interface, which is effective for improving the uniformity of RRAM devices. Here, we report a uniform RRAM device based on a MXene-TiO2 Schottky junction. The defect traps within the MXene formed during its fabricating process can trap and release the charges at the MXene-TiO2 interface to modulate the Schottky barrier for the resistive switching behavior. Our devices exhibit excellent current on-off ratio uniformity, device-to-device reproducibility, long-term retention, and endurance reliability. Due to the different carrier-blocking abilities of the MXene-TiO2 and TiO2-Si interface barriers, a self-rectifying behavior can be obtained with a rectifying ratio of 103, which offers great potential for large-scale RRAM applications based on MXene materials.

| S-EPMC9680946 | biostudies-literature

Atomic Scale Modulation of Self-Rectifying Resistive Switching by Interfacial Defects.

Project description:Higher memory density and faster computational performance of resistive switching cells require reliable array-accessible architecture. However, selecting a designated cell within a crossbar array without interference from sneak path currents through neighboring cells is a general problem. Here, a highly doped n++ Si as the bottom electrode with Ni-electrode/HfO x /SiO2 asymmetric self-rectifying resistive switching device is fabricated. The interfacial defects in the HfO x /SiO2 junction and n++ Si substrate result in the reproducible rectifying behavior. In situ transmission electron microscopy is used to quantitatively study the properties of the morphology, chemistry, and dynamic nucleation-dissolution evolution of the chains of defects at the atomic scale. The spatial and temporal correlation between the concentration of oxygen vacancies and Ni-rich conductive filament modifies the resistive switching effect. This study has important implications at the array-level performance of high density resistive switching memories.

| S-EPMC6010905 | biostudies-literature

4K-memristor analog-grade passive crossbar circuit.

Project description:The superior density of passive analog-grade memristive crossbar circuits enables storing large neural network models directly on specialized neuromorphic chips to avoid costly off-chip communication. To ensure efficient use of such circuits in neuromorphic systems, memristor variations must be substantially lower than those of active memory devices. Here we report a 64 × 64 passive crossbar circuit with ~99% functional nonvolatile metal-oxide memristors. The fabrication technology is based on a foundry-compatible process with etch-down patterning and a low-temperature budget. The achieved <26% coefficient of variance in memristor switching voltages is sufficient for programming a 4K-pixel gray-scale pattern with a <4% relative tuning error on average. Analog properties are also successfully verified via experimental demonstration of a 64 × 10 vector-by-matrix multiplication with an average 1% relative conductance import accuracy to model the MNIST image classification by ex-situ trained single-layer perceptron, and modeling of a large-scale multilayer perceptron classifier based on more advanced conductance tuning algorithm.

| S-EPMC8408216 | biostudies-literature

One-step regression and classification with cross-point resistive memory arrays.

Project description:Machine learning has been getting attention in recent years as a tool to process big data generated by the ubiquitous sensors used in daily life. High-speed, low-energy computing machines are in demand to enable real-time artificial intelligence processing of such data. These requirements challenge the current metal-oxide-semiconductor technology, which is limited by Moore's law approaching its end and the communication bottleneck in conventional computing architecture. Novel computing concepts, architectures, and devices are thus strongly needed to accelerate data-intensive applications. Here, we show that a cross-point resistive memory circuit with feedback configuration can train traditional machine learning algorithms such as linear regression and logistic regression in just one step by computing the pseudoinverse matrix of the data within the memory. One-step learning is further supported by simulations of the prediction of housing price in Boston and the training of a two-layer neural network for MNIST digit recognition.

| S-EPMC6994204 | biostudies-literature

Current rectifying and resistive switching in high density BiFeO3 nanocapacitor arrays on Nb-SrTiO3 substrates.

Project description:Ultrahigh density well-registered oxide nanocapacitors are very essential for large scale integrated microelectronic devices. We report the fabrication of well-ordered multiferroic BiFeO3 nanocapacitor arrays by a combination of pulsed laser deposition (PLD) method and anodic aluminum oxide (AAO) template method. The capacitor cells consist of BiFeO3/SrRuO3 (BFO/SRO) heterostructural nanodots on conductive Nb-doped SrTiO3 (Nb-STO) substrates with a lateral size of ~60 nm. These capacitors also show reversible polarization domain structures, and well-established piezoresponse hysteresis loops. Moreover, apparent current-rectification and resistive switching behaviors were identified in these nanocapacitor cells using conductive-AFM technique, which are attributed to the polarization modulated p-n junctions. These make it possible to utilize these nanocapacitors in high-density (>100 Gbit/inch(2)) nonvolatile memories and other oxide nanoelectronic devices.

| S-EPMC4389717 | biostudies-other

Self-Compliant Bipolar Resistive Switching in SiN-Based Resistive Switching Memory.

Project description:Here, we present evidence of self-compliant and self-rectifying bipolar resistive switching behavior in Ni/SiNx/n⁺ Si and Ni/SiNx/n++ Si resistive-switching random access memory devices. The Ni/SiNx/n++ Si device's Si bottom electrode had a higher dopant concentration (As ion > 1019 cm-3) than the Ni/SiNx/n⁺ Si device; both unipolar and bipolar resistive switching behaviors were observed for the higher dopant concentration device owing to a large current overshoot. Conversely, for the device with the lower dopant concentration (As ion < 1018 cm-3), self-rectification and self-compliance were achieved owing to the series resistance of the Si bottom electrode.

| S-EPMC5459000 | biostudies-other

Implementing in-situ self-organizing maps with memristor crossbar arrays for data mining and optimization.

Project description:A self-organizing map (SOM) is a powerful unsupervised learning neural network for analyzing high-dimensional data in various applications. However, hardware implementation of SOM is challenging because of the complexity in calculating the similarities and determining neighborhoods. We experimentally demonstrated a memristor-based SOM based on Ta/TaOx/Pt 1T1R chips for the first time, which has advantages in computing speed, throughput, and energy efficiency compared with the CMOS digital counterpart, by utilizing the topological structure of the array and physical laws for computing without complicated circuits. We employed additional rows in the crossbar arrays and identified the best matching units by directly calculating the similarities between the input vectors and the weight matrix in the hardware. Using the memristor-based SOM, we demonstrated data clustering, image processing and solved the traveling salesman problem with much-improved energy efficiency and computing throughput. The physical implementation of SOM in memristor crossbar arrays extends the capability of memristor-based neuromorphic computing systems in machine learning and artificial intelligence.

| S-EPMC9051161 | biostudies-literature

Self-Selecting Resistive Switching Scheme Using TiO2 Nanorod Arrays.

Project description:In this study, the resistive switching scheme using TiO2 nanorod arrays synthesized by a large-scale and low-cost hydrothermal process was reported. Especially, the nonlinear I-V characteristics of TiO2 nanorod arrays with a nonlinearity of up to ~10, which suppress the leakage current less than 10-4 Acm-2, were demonstrated, exhibiting a self-selecting resistive switching behavior. It provides a simple pathway for integration of RRAM crossbar arrays without additional stacking of active devices. The mechanisms of the nonlinear resistive switching behaviors were discussed in detail. In addition, the maximum array numbers of 79 for self-selecting RRAM cells were estimated. The results demonstrate an opportunity of using the concept of self-selecting resistive switching characteristics in a single material, which offers a new strategy to tackle the sneak path issue of RRAM in the crossbar arrays structure.

| S-EPMC5437035 | biostudies-literature

OmicsDI is part of the ELIXIR infrastructure

OmicsDI is an Elixir interoperability service. Learn more ›

Tweets

OmicsDI Databases

PRIDE
PeptideAtlas
MassIVE
JPOST Repository
Physiome Model Repository

EGA
EVA
ENA
LINCS
PAXDB
Cell Collective

MetaboLights
Metabolomics Workbench
MetabolomeExpress
GNPS
BioModels
FAIRDOMHub

ArrayExpress
dbGaP
ExpressionAtlas
GEO
NODE

Information

Databases
Help
API
Contact us
Code on GitHub
Terms of use
Submit Data