SiOx-based resistive switching memory integrated in nanopillar structure fabricated by nanosphere lithography

textA highly compact, one diode-one resistor (1D-1R) SiOx-based resistive switching memory device with nano-pillar architecture has been achieved for the first time using nano-sphere lithography. The average nano-pillar height and diameter are 1.3 μm and 130 nm, respectively. Low-voltage electroforming using DC bias and AC pulse response in the 50ns regime demonstrate good potential for high-speed, low-energy nonvolatile memory. Nano-sphere deposition, oxygen-plasma isolation, and nano-pillar formation by deep-Si-etching are studied and optimized for the 1D-1R configurations. Excellent electrical performance, data retention and the potential for wafer-scale integration are promising for future non-volatile memory applications.Materials Science and Engineerin

Impact of Line Resistance Combined with Device Variability on Resistive RAM Memories

International audienceIn this paper, the performance and reliability of oxide-based Resistive RAM (ReRAM) memory is investigated in a 28nm FDSOI technology versus interconnects resistivity combined with device variability. Indeed, common problems with ReRAM are related to high variability in operating conditions and low yield. At a cell level ReRAMs suffer from variability. At an array level, ReRAMs suffer from different voltage drops seen across the cells due to line resistances. Although research has taken steps to resolve these issues, variability combined with resistive paths remain an important characteristic for ReRAMs. In this context, a deeper understanding of the impact of these characteristics on ReRAM performances is needed to propose variability tolerant designs to ensure the robustness of the technology. The presented study addresses the memory cell, the memory word up to the memory matrix

An Augmented OxRAM Synapse for Spiking Neural Network (SNN) Circuits

International audienceIn this paper, the conductance modulation of OxRAM memristive devices is evaluated based on experimental data to reveal the memristor inherent analog synaptic behavior. Simulation results are presented to validate the use of OxRAMs as synapses at a circuit level in a spiking neural network context. In the proposed approach, the OxRAM synapse is augmented with a shift register associated with current compliance control transistors to provide an efficient monitoring of the OxRAM conductance

Characterization of low power HfO2 based switching devices for in-memory computing

Oxide based Resistive Random Access Memory (RRAM) devices are investigated as one of the promising non-volatile memories to be used for in-memory computing that will replace the classical von Neumann architecture and reduce the power consumption. These applications required multilevel cell (MLC) characteristics that can be achieved in RRAM devices. One of the methods to achieve this analog switching behavior is by performing an optimized electrical pulse. The RRAM device structure is basically an insulator between two metals as metal-insulator-metal (MIM) structure. Where one of the primary challenges is to assign an RRAM stack with both low power consumption and good switching performance. This thesis investigates different HfO2 based RRAM stacks and compares their electrical and MLC characteristics. By engineering the distribution of defects and oxygen vacancies in the switching layer, which have been done by exposing the dielectric with a hydrogen plasma treatment in the first device, using HfO2 and Al2O3 as a bilayer, or by adding Zr to the HfO2. While the plasma treated devices show a promising conductance quantization with low power consumption, the performance can be further enhanced by engineering the bottom electrode. The impact of introducing additional nitrogen at the bottom electrode, TiN, shows additional reduction in the switching power of the plasma treated devices

Memristive Non-Volatile Memory Based on Graphene Materials

Resistive random access memory (RRAM), which is considered as one of the most promising next-generation non-volatile memory (NVM) devices and a representative of memristor technologies, demonstrated great potential in acting as an artificial synapse in the industry of neuromorphic systems and artificial intelligence (AI), due its advantages such as fast operation speed, low power consumption, and high device density. Graphene and related materials (GRMs), especially graphene oxide (GO), acting as active materials for RRAM devices, are considered as a promising alternative to other materials including metal oxides and perovskite materials. Herein, an overview of GRM-based RRAM devices is provided, with discussion about the properties of GRMs, main operation mechanisms for resistive switching (RS) behavior, figure of merit (FoM) summary, and prospect extension of GRM-based RRAM devices. With excellent physical and chemical advantages like intrinsic Young’s modulus (1.0 TPa), good tensile strength (130 GPa), excellent carrier mobility (2.0 × 105 cm2∙V−1∙s−1), and high thermal (5000 Wm−1∙K−1) and superior electrical conductivity (1.0 × 106 S∙m−1), GRMs can act as electrodes and resistive switching media in RRAM devices. In addition, the GRM-based interface between electrode and dielectric can have an effect on atomic diffusion limitation in dielectric and surface effect suppression. Immense amounts of concrete research indicate that GRMs might play a significant role in promoting the large-scale commercialization possibility of RRAM devices

Treated HfO2 based rram devices with ru, tan, tin as top electrode for in-memory computing hardware

The scalability and power efficiency of the conventional CMOS technology is steadily coming to a halt due to increasing problems and challenges in fabrication technology. Many non-volatile memory devices have emerged recently to meet the scaling challenges. Memory devices such as RRAMs or ReRAM (Resistive Random-Access Memory) have proved to be a promising candidate for analog in memory computing applications related to inference and learning in artificial intelligence. A RRAM cell has a MIM (Metal insulator metal) structure that exhibits reversible resistive switching on application of positive or negative voltage. But detailed studies on the power consumption, repeatability and retention of during multi-level operation have not been undertaken previously. Transition metal oxide-based RRAMs, using HfO2, executes change in resistance (switching behavior) via electrochemical migration of oxygen vacancies. This thesis investigates the role of extra oxygen vacancies, introduced by plasma exposure (treated), in HfO2 to reduce the power consumption of RRAM. In addition to oxygen vacancy rich HfO2, various top metal electrodes including Ruthenium (Ru) are explored to enhance the switching behavior and power consumption. Use of Ru as a top metal reduced the switching energy of the treated HfO2 RRAM device

Configurable Operational Amplifier Architectures Based on Oxide Resistive RAMs

International audienceThis paper introduces memristor-based operational amplifiers in which semiconductor resistors are suppressed and replaced by memristors. The ability of the memristive elements to hold several resistance states is exploited to design programmable closed-loop operational amplifiers. An inverting operational amplifier, an integrator and a differentiator are studied. Such designs are developed based on a calibrated memristor model, and offer dynamic configurability to realize different gains and corner frequencies at reduced chip area

Memristive Systems Based on Two-Dimensional Materials

The unique electronic and optical properties of newly discovered 2D crystals such as graphene, graphene oxide, molybdenum disulfide, and so on demonstrate the tremendous potential in creating ultrahigh-density nano- and bioelectronics for innovative image recognition systems, storage and processing of big data. A new type of memristors with a floating photogate based on biocompatible graphene and other 2D crystals with extremely low power consumption and footprint is considered. The photocatalytic oxidation of graphene is proposed as an effective method of creating synapse-like 2D memristive devices with photoresistive switching for nonvolatile electronic memory of ultrahigh density. Particular attention is paid to the new concept of the formation of self-assembled nanoscale memristive elements interfacing artificial electronic neural networks. 2D photomemristors with a floating photogate exhibit multiple states controlled in a wide range of electromagnetic radiation and can be used for neuromorphic computations, pattern recognition and image processing needed to create artificial intelligence